From 1e7460fd5850a761d763b3126db3476ab0e0ddc6 Mon Sep 17 00:00:00 2001
From: m-guberina <gubi.guberina@gmail.com>
Date: Mon, 17 Feb 2025 00:18:07 +0100
Subject: [PATCH] bit more rewriting, now debugging initialization
---
.../check_tcp_payload | Bin
.../currents.png | Bin
.../frame_validation.py | 0
.../freedrive.py | 0
.../freedrive_v2.0.py | 0
.../ft_readings.py | 0
.../fts.png | Bin
.../jog_example | Bin
.../measuring_stick.py | 0
.../notes.md | 0
.../open_close_gripper.py | 0
.../taus.png | Bin
development_plan.toml | 8 +-
examples/cart_pulling/behavior_tree.py | 76 +
.../cart_pulling/cart_pulling.py | 167 +-
examples/cart_pulling/cart_pulling_notes.md | 93 +
.../cart_pulling/dual_arm_path_following.py | 2 +
.../path_following_from_planner.py | 60 +
examples/cart_pulling/pose_initialization.py | 3 +
.../cart_pulling/starify_nav2_map.py | 0
.../cart_pulling/trajectory_following_mpc.py | 1 +
.../cartesian_space/clik.py | 5 +-
.../data/from_writing_to_handover.pickle_save | Bin
{python/examples => examples}/data/fts.png | Bin
{python/examples => examples}/data/latest_run | Bin
.../examples => examples}/data/latest_run_0 | Bin
.../drawing/drawing_from_input_drawing.py | 0
.../drawing/plane_pose.pickle_save | Bin
.../drawing/wiping_path.csv_save | 0
.../graz/cartesian_pose_command_server.py | 0
.../examples => examples}/graz/clik_client.py | 0
{python/examples => examples}/graz/clik_m.py | 0
.../graz/joint_copier_client.py | 0
.../graz/message_specs_pb2.py | 0
.../graz/point_impedance_server.py | 0
.../graz/protobuf_ros1_bridge.py | 0
.../graz/protobuf_to_ros1.py | 0
.../graz/ros1_to_protobuf.py | 0
.../graz/to_install_on_ubuntu18.md | 0
.../examples => examples}/graz/traiettoria.m | 0
.../impedance/point_impedance_control.py | 0
.../log_analysis/comparing_logs_example.py | 0
.../log_analysis/log_analysis_example.py | 0
.../old_or_experimental/clik_old.py | 0
.../force_control/force_control_test.py | 0
.../force_control/force_mode_api.py | 0
.../force_control/forcemode_example.py | 0
.../force_control/point_force_control.py | 0
.../ft_calibration_via_amperes_test.py | 0
.../test_crocoddyl_opt_ctrl.py | 0
.../old_or_experimental/test_gripper.py | 0
.../old_or_experimental/test_traj_w_movej.py | 0
.../old_or_experimental/test_traj_w_speedj.py | 0
.../casadi_ocp_collision_avoidance.py | 0
.../optimal_control/croco_ik_mpc.py | 0
.../optimal_control/croco_ik_ocp.py | 0
.../optimal_control/path_following_mpc.py | 53 +
.../path6d_from_path2d.py | 47 +-
.../pin_contact3d.py | 0
.../pushing_via_friction_cones.py | 0
.../ros/dummy_cmds_pub.py | 0
.../examples => examples}/ros/ros2_clik.py | 0
{python/examples => examples}/ros/smc_node.py | 0
.../ros/smc_yumi_challenge.py | 0
.../examples => examples}/test_by_running.sh | 0
.../vision/camera_no_lag.py | 0
...ve_me_the_calibrated_model.cpython-310.pyc | Bin 1595 -> 0 bytes
.../robotiq_gripper.cpython-310.pyc | Bin 12223 -> 0 bytes
.../cart_pulling/cart_pulling_notes.md | 67 -
python/examples/data/clik_comparison_0.pickle | Bin 593470 -> 0 bytes
python/examples/data/clik_comparison_1.pickle | Bin 593468 -> 0 bytes
python/examples/data/joint_trajectory.csv | 8412 -----------------
python/examples/data/path_in_pixels.csv | 20 -
.../optimal_control/path_following_mpc.py | 82 -
.../cartesian_space_trajectory_following.py | 10 +-
python/smc/control/control_loop_manager.py | 5 +-
.../joint_space_trajectory_following.py | 8 +-
.../optimal_control/abstract_croco_ocp.py | 17 +-
.../croco_mpc_path_following.py | 35 +-
.../point_to_point_croco_ocp.py | 25 +-
...LD_sebas_path_generator_WITH_EVERYTHING.py | 629 ++
.../smc/path_generation/maps/premade_maps.py | 21 +
python/smc/path_generation/path_generator.py | 551 +-
.../path_generation/path_math/path2d_to_6d.py | 131 +
.../path_math/path_to_trajectory.py | 91 +
python/smc/path_generation/planner.py | 752 +-
python/smc/path_generation/scene_updater.py | 142 +
python/smc/robots/implementations/heron.py | 86 +-
.../interfaces/mobile_base_interface.py | 78 +
python/smc/robots/robotmanager_abstract.py | 26 +-
python/smc/robots/utils.py | 9 +-
.../meshcat_viewer_wrapper/visualizer.py | 22 +-
python/smc/visualization/path_visualizer.py | 40 +
93 files changed, 1801 insertions(+), 9973 deletions(-)
rename {python/convenience_tool_box => convenience_tool_box}/check_tcp_payload (100%)
rename {python/convenience_tool_box => convenience_tool_box}/currents.png (100%)
rename {python/convenience_tool_box => convenience_tool_box}/frame_validation.py (100%)
rename {python/convenience_tool_box => convenience_tool_box}/freedrive.py (100%)
rename {python/convenience_tool_box => convenience_tool_box}/freedrive_v2.0.py (100%)
rename {python/convenience_tool_box => convenience_tool_box}/ft_readings.py (100%)
rename {python/convenience_tool_box => convenience_tool_box}/fts.png (100%)
rename {python/convenience_tool_box => convenience_tool_box}/jog_example (100%)
rename {python/convenience_tool_box => convenience_tool_box}/measuring_stick.py (100%)
rename {python/convenience_tool_box => convenience_tool_box}/notes.md (100%)
rename {python/convenience_tool_box => convenience_tool_box}/open_close_gripper.py (100%)
rename {python/convenience_tool_box => convenience_tool_box}/taus.png (100%)
create mode 100644 examples/cart_pulling/behavior_tree.py
rename {python/examples => examples}/cart_pulling/cart_pulling.py (70%)
create mode 100644 examples/cart_pulling/cart_pulling_notes.md
create mode 100644 examples/cart_pulling/dual_arm_path_following.py
create mode 100644 examples/cart_pulling/path_following_from_planner.py
create mode 100644 examples/cart_pulling/pose_initialization.py
rename {python/examples => examples}/cart_pulling/starify_nav2_map.py (100%)
create mode 100644 examples/cart_pulling/trajectory_following_mpc.py
rename {python/examples => examples}/cartesian_space/clik.py (97%)
rename {python/examples => examples}/data/from_writing_to_handover.pickle_save (100%)
rename {python/examples => examples}/data/fts.png (100%)
rename {python/examples => examples}/data/latest_run (100%)
rename {python/examples => examples}/data/latest_run_0 (100%)
rename {python/examples => examples}/drawing/drawing_from_input_drawing.py (100%)
rename {python/examples => examples}/drawing/plane_pose.pickle_save (100%)
rename {python/examples => examples}/drawing/wiping_path.csv_save (100%)
rename {python/examples => examples}/graz/cartesian_pose_command_server.py (100%)
rename {python/examples => examples}/graz/clik_client.py (100%)
rename {python/examples => examples}/graz/clik_m.py (100%)
rename {python/examples => examples}/graz/joint_copier_client.py (100%)
rename {python/examples => examples}/graz/message_specs_pb2.py (100%)
rename {python/examples => examples}/graz/point_impedance_server.py (100%)
rename {python/examples => examples}/graz/protobuf_ros1_bridge.py (100%)
rename {python/examples => examples}/graz/protobuf_to_ros1.py (100%)
rename {python/examples => examples}/graz/ros1_to_protobuf.py (100%)
rename {python/examples => examples}/graz/to_install_on_ubuntu18.md (100%)
rename {python/examples => examples}/graz/traiettoria.m (100%)
rename {python/examples => examples}/impedance/point_impedance_control.py (100%)
rename {python/examples => examples}/log_analysis/comparing_logs_example.py (100%)
rename {python/examples => examples}/log_analysis/log_analysis_example.py (100%)
rename {python/examples => examples}/old_or_experimental/clik_old.py (100%)
rename {python/examples => examples}/old_or_experimental/force_control/force_control_test.py (100%)
rename {python/examples => examples}/old_or_experimental/force_control/force_mode_api.py (100%)
rename {python/examples => examples}/old_or_experimental/force_control/forcemode_example.py (100%)
rename {python/examples => examples}/old_or_experimental/force_control/point_force_control.py (100%)
rename {python/examples => examples}/old_or_experimental/ft_calibration_via_amperes_test.py (100%)
rename {python/examples => examples}/old_or_experimental/test_crocoddyl_opt_ctrl.py (100%)
rename {python/examples => examples}/old_or_experimental/test_gripper.py (100%)
rename {python/examples => examples}/old_or_experimental/test_traj_w_movej.py (100%)
rename {python/examples => examples}/old_or_experimental/test_traj_w_speedj.py (100%)
rename {python/examples => examples}/optimal_control/casadi_ocp_collision_avoidance.py (100%)
rename {python/examples => examples}/optimal_control/croco_ik_mpc.py (100%)
rename {python/examples => examples}/optimal_control/croco_ik_ocp.py (100%)
create mode 100644 examples/optimal_control/path_following_mpc.py
rename {python/examples => examples}/path6d_from_path2d.py (75%)
rename {python/examples => examples}/pinocchio_math_understanding/pin_contact3d.py (100%)
rename {python/examples => examples}/pushing_via_friction_cones.py (100%)
rename {python/examples => examples}/ros/dummy_cmds_pub.py (100%)
rename {python/examples => examples}/ros/ros2_clik.py (100%)
rename {python/examples => examples}/ros/smc_node.py (100%)
rename {python/examples => examples}/ros/smc_yumi_challenge.py (100%)
rename {python/examples => examples}/test_by_running.sh (100%)
rename {python/examples => examples}/vision/camera_no_lag.py (100%)
delete mode 100644 python/convenience_tool_box/__pycache__/give_me_the_calibrated_model.cpython-310.pyc
delete mode 100644 python/convenience_tool_box/__pycache__/robotiq_gripper.cpython-310.pyc
delete mode 100644 python/examples/cart_pulling/cart_pulling_notes.md
delete mode 100644 python/examples/data/clik_comparison_0.pickle
delete mode 100644 python/examples/data/clik_comparison_1.pickle
delete mode 100644 python/examples/data/joint_trajectory.csv
delete mode 100644 python/examples/data/path_in_pixels.csv
delete mode 100644 python/examples/optimal_control/path_following_mpc.py
create mode 100644 python/smc/path_generation/OLD_sebas_path_generator_WITH_EVERYTHING.py
create mode 100644 python/smc/path_generation/maps/premade_maps.py
create mode 100644 python/smc/path_generation/path_math/path2d_to_6d.py
create mode 100644 python/smc/path_generation/path_math/path_to_trajectory.py
create mode 100644 python/smc/path_generation/scene_updater.py
create mode 100644 python/smc/visualization/path_visualizer.py
diff --git a/python/convenience_tool_box/check_tcp_payload b/convenience_tool_box/check_tcp_payload
similarity index 100%
rename from python/convenience_tool_box/check_tcp_payload
rename to convenience_tool_box/check_tcp_payload
diff --git a/python/convenience_tool_box/currents.png b/convenience_tool_box/currents.png
similarity index 100%
rename from python/convenience_tool_box/currents.png
rename to convenience_tool_box/currents.png
diff --git a/python/convenience_tool_box/frame_validation.py b/convenience_tool_box/frame_validation.py
similarity index 100%
rename from python/convenience_tool_box/frame_validation.py
rename to convenience_tool_box/frame_validation.py
diff --git a/python/convenience_tool_box/freedrive.py b/convenience_tool_box/freedrive.py
similarity index 100%
rename from python/convenience_tool_box/freedrive.py
rename to convenience_tool_box/freedrive.py
diff --git a/python/convenience_tool_box/freedrive_v2.0.py b/convenience_tool_box/freedrive_v2.0.py
similarity index 100%
rename from python/convenience_tool_box/freedrive_v2.0.py
rename to convenience_tool_box/freedrive_v2.0.py
diff --git a/python/convenience_tool_box/ft_readings.py b/convenience_tool_box/ft_readings.py
similarity index 100%
rename from python/convenience_tool_box/ft_readings.py
rename to convenience_tool_box/ft_readings.py
diff --git a/python/convenience_tool_box/fts.png b/convenience_tool_box/fts.png
similarity index 100%
rename from python/convenience_tool_box/fts.png
rename to convenience_tool_box/fts.png
diff --git a/python/convenience_tool_box/jog_example b/convenience_tool_box/jog_example
similarity index 100%
rename from python/convenience_tool_box/jog_example
rename to convenience_tool_box/jog_example
diff --git a/python/convenience_tool_box/measuring_stick.py b/convenience_tool_box/measuring_stick.py
similarity index 100%
rename from python/convenience_tool_box/measuring_stick.py
rename to convenience_tool_box/measuring_stick.py
diff --git a/python/convenience_tool_box/notes.md b/convenience_tool_box/notes.md
similarity index 100%
rename from python/convenience_tool_box/notes.md
rename to convenience_tool_box/notes.md
diff --git a/python/convenience_tool_box/open_close_gripper.py b/convenience_tool_box/open_close_gripper.py
similarity index 100%
rename from python/convenience_tool_box/open_close_gripper.py
rename to convenience_tool_box/open_close_gripper.py
diff --git a/python/convenience_tool_box/taus.png b/convenience_tool_box/taus.png
similarity index 100%
rename from python/convenience_tool_box/taus.png
rename to convenience_tool_box/taus.png
diff --git a/development_plan.toml b/development_plan.toml
index b39b40a..8c4361b 100644
--- a/development_plan.toml
+++ b/development_plan.toml
@@ -77,7 +77,7 @@ dependencies = []
purpose = """making typing possible - easier code writting and debugging, making library professional-grade"""
# -1 means it should be a sum over sub-items
hours_required = 10
-is_done = false
+is_done = true
[[project_plan.action_items.action_items]]
@@ -107,6 +107,8 @@ for the pinocchio-only option.
these can be done via ifs in those functions because 1 if is ok.
various things are to be implemented with interfaces (like 2 arms, which just gives you more functions, but which only make sense if the robot has two arms). interfaces don't exist in python, but you can do multiple inheritance. these extra functions should thus be made available by inheriting stuff like TwoArmsRobot etc. have one such class for simulated robots as they all use the same getq etc.
+
+UPDATE: BTW, this took more like 3 full days than 4 hours. getting OOP right is hard and i have 0 real experience with it. i'm not even sure i got the design right. also, setting the scope was hella difficult. this required both seba's help and chatgpt
"""
priority = 1
deadline = 2025-01-31
@@ -114,7 +116,7 @@ dependencies = []
purpose = """making codebase usable"""
# -1 means it should be a sum over sub-items
hours_required = 4
-is_done = false
+is_done = true
[[project_plan.action_items.action_items]]
@@ -242,7 +244,7 @@ with some different parameters - finish that. this checks whether anything runs,
and whether things converge. probably a good idea to run some of these multiple times
in the case they are not supposed to converge every time (if you have crazy init conditions etc).
"""
-priority = 1
+priority = 2
deadline = ["before going to vasteros", 2025-02-10]
dependencies = ["smc refactor"]
purpose = """makes you sleep safe at night, saves a lot of time after writing of a feature -
diff --git a/examples/cart_pulling/behavior_tree.py b/examples/cart_pulling/behavior_tree.py
new file mode 100644
index 0000000..f5305d1
--- /dev/null
+++ b/examples/cart_pulling/behavior_tree.py
@@ -0,0 +1,76 @@
+import pinocchio as pin
+import numpy as np
+
+
+################################################################
+# STATE TRANSITION CONDITIONS
+################################################################
+def isGraspOK(args, robot, grasp_pose):
+ isOK = False
+ SEerror = robot.T_w_e().actInv(grasp_pose)
+ err_vector = pin.log6(SEerror).vector
+ # TODO: figure this out
+ # it seems you have to use just the arm to get to finish with this precision
+ # if np.linalg.norm(err_vector) < robot.args.goal_error:
+ if np.linalg.norm(err_vector) < 2 * 1e-1:
+ isOK = True
+ return not isOK
+
+
+def isGripperRelativeToBaseOK(args, robot):
+ isOK = False
+ # we want to be in the back of the base (x-axis) and on handlebar height
+ T_w_base = robot.data.oMi[1]
+ # rotation for the gripper is base with z flipped to point into the ground
+ rotate = pin.SE3(pin.rpy.rpyToMatrix(np.pi, 0.0, 0.0), np.zeros(3))
+ # translation is prefered distance from base
+ translate = pin.SE3(
+ np.eye(3),
+ np.array(
+ [args.base_to_handlebar_preferred_distance, 0.0, args.handlebar_height]
+ ),
+ )
+ # grasp_pose = T_w_base.act(rotate.act(translate))
+ grasp_pose = T_w_base.act(translate.act(rotate))
+ SEerror = robot.T_w_e().actInv(grasp_pose)
+ err_vector = pin.log6(SEerror).vector
+ # TODO: figure this out
+ # it seems you have to use just the arm to get to finish with this precision
+ # if np.linalg.norm(err_vector) < robot.args.goal_error:
+ if np.linalg.norm(err_vector) < 2 * 1e-1:
+ isOK = True
+ return isOK, grasp_pose
+
+
+def areDualGrippersRelativeToBaseOK(args, goal_transform, robot):
+ isOK = False
+ # we want to be in the back of the base (x-axis) and on handlebar height
+ T_w_base = robot.data.oMi[1]
+ # rotation for the gripper is base with z flipped to point into the ground
+ rotate = pin.SE3(pin.rpy.rpyToMatrix(np.pi, 0.0, 0.0), np.zeros(3))
+ # translation is prefered distance from base
+ translate = pin.SE3(
+ np.eye(3),
+ np.array(
+ [args.base_to_handlebar_preferred_distance, 0.0, args.handlebar_height]
+ ),
+ )
+ # grasp_pose = T_w_base.act(rotate.act(translate))
+ grasp_pose = T_w_base.act(translate.act(rotate))
+
+ grasp_pose_left = goal_transform.act(grasp_pose)
+ grasp_pose_right = goal_transform.inverse().act(grasp_pose)
+
+ T_w_e_left, T_w_e_right = robot.T_w_e()
+ SEerror_left = T_w_e_left.actInv(grasp_pose_left)
+ SEerror_right = T_w_e_right.actInv(grasp_pose_right)
+ err_vector_left = pin.log6(SEerror_left).vector
+ err_vector_right = pin.log6(SEerror_right).vector
+ # TODO: figure this out
+ # it seems you have to use just the arm to get to finish with this precision
+ # if np.linalg.norm(err_vector) < robot.args.goal_error:
+ if (np.linalg.norm(err_vector_left) < 2 * 1e-1) and (
+ np.linalg.norm(err_vector_right) < 2 * 1e-1
+ ):
+ isOK = True
+ return isOK, grasp_pose, grasp_pose_left, grasp_pose_right
diff --git a/python/examples/cart_pulling/cart_pulling.py b/examples/cart_pulling/cart_pulling.py
similarity index 70%
rename from python/examples/cart_pulling/cart_pulling.py
rename to examples/cart_pulling/cart_pulling.py
index 2687ca8..6ed8bed 100644
--- a/python/examples/cart_pulling/cart_pulling.py
+++ b/examples/cart_pulling/cart_pulling.py
@@ -1,21 +1,10 @@
-# PYTHON_ARGCOMPLETE_OK
-import numpy as np
-import time
-import argparse, argcomplete
-from functools import partial
-from smc.managers import (
- getMinimalArgParser,
- ControlLoopManager,
- RobotManager,
- ProcessManager,
-)
-from smc.optimal_control.get_ocp_args import get_OCP_args
-from smc.optimal_control.crocoddyl_optimal_control import *
-from smc.optimal_control.crocoddyl_mpc import *
-from smc.basics.basics import followKinematicJointTrajP
-from smc.util.logging_utils import LogManager
-from smc.visualize.visualize import plotFromDict
-from smc.clik.clik import (
+from behavior_tree import *
+from smc.path_generation.maps.premade_maps import createSampleStaticMap
+from smc.path_generation.planner import starPlanner, getPlanningArgs
+from smc import getMinimalArgParser, getRobotFromArgs
+from smc.util.define_random_goal import getRandomlyGeneratedGoal
+from smc.control.optimal_control import *
+from smc.control.cartesian_space import (
getClikArgs,
cartesianPathFollowingWithPlanner,
controlLoopClik,
@@ -25,11 +14,11 @@ from smc.clik.clik import (
controlLoopClikDualArmsOnly,
)
import pinocchio as pin
+import numpy as np
import crocoddyl
from functools import partial
-import importlib.util
-from smc.path_generation.planner import starPlanner, getPlanningArgs, createMap
import yaml
+import time
# TODO:
# - make reference step size in path_generator an argument here
@@ -53,7 +42,6 @@ def get_args():
default=0.7,
help="prefered path arclength from mobile base position to handlebar",
)
- argcomplete.autocomplete(parser)
args = parser.parse_args()
# TODO TODO TODO: REMOVE PRESET HACKS
robot_type = "Unicycle"
@@ -68,77 +56,6 @@ def get_args():
return args
-def isGraspOK(args, robot, grasp_pose):
- isOK = False
- SEerror = robot.getT_w_e().actInv(grasp_pose)
- err_vector = pin.log6(SEerror).vector
- # TODO: figure this out
- # it seems you have to use just the arm to get to finish with this precision
- # if np.linalg.norm(err_vector) < robot.args.goal_error:
- if np.linalg.norm(err_vector) < 2 * 1e-1:
- isOK = True
- return not isOK
-
-
-def isGripperRelativeToBaseOK(args, robot):
- isOK = False
- # we want to be in the back of the base (x-axis) and on handlebar height
- T_w_base = robot.data.oMi[1]
- # rotation for the gripper is base with z flipped to point into the ground
- rotate = pin.SE3(pin.rpy.rpyToMatrix(np.pi, 0.0, 0.0), np.zeros(3))
- # translation is prefered distance from base
- translate = pin.SE3(
- np.eye(3),
- np.array(
- [args.base_to_handlebar_preferred_distance, 0.0, args.handlebar_height]
- ),
- )
- # grasp_pose = T_w_base.act(rotate.act(translate))
- grasp_pose = T_w_base.act(translate.act(rotate))
- SEerror = robot.getT_w_e().actInv(grasp_pose)
- err_vector = pin.log6(SEerror).vector
- # TODO: figure this out
- # it seems you have to use just the arm to get to finish with this precision
- # if np.linalg.norm(err_vector) < robot.args.goal_error:
- if np.linalg.norm(err_vector) < 2 * 1e-1:
- isOK = True
- return isOK, grasp_pose
-
-
-def areDualGrippersRelativeToBaseOK(args, goal_transform, robot):
- isOK = False
- # we want to be in the back of the base (x-axis) and on handlebar height
- T_w_base = robot.data.oMi[1]
- # rotation for the gripper is base with z flipped to point into the ground
- rotate = pin.SE3(pin.rpy.rpyToMatrix(np.pi, 0.0, 0.0), np.zeros(3))
- # translation is prefered distance from base
- translate = pin.SE3(
- np.eye(3),
- np.array(
- [args.base_to_handlebar_preferred_distance, 0.0, args.handlebar_height]
- ),
- )
- # grasp_pose = T_w_base.act(rotate.act(translate))
- grasp_pose = T_w_base.act(translate.act(rotate))
-
- grasp_pose_left = goal_transform.act(grasp_pose)
- grasp_pose_right = goal_transform.inverse().act(grasp_pose)
-
- T_w_e_left, T_w_e_right = robot.getT_w_e()
- SEerror_left = T_w_e_left.actInv(grasp_pose_left)
- SEerror_right = T_w_e_right.actInv(grasp_pose_right)
- err_vector_left = pin.log6(SEerror_left).vector
- err_vector_right = pin.log6(SEerror_right).vector
- # TODO: figure this out
- # it seems you have to use just the arm to get to finish with this precision
- # if np.linalg.norm(err_vector) < robot.args.goal_error:
- if (np.linalg.norm(err_vector_left) < 2 * 1e-1) and (
- np.linalg.norm(err_vector_right) < 2 * 1e-1
- ):
- isOK = True
- return isOK, grasp_pose, grasp_pose_left, grasp_pose_right
-
-
def cartPullingControlLoop(
args,
robot: RobotManager,
@@ -159,11 +76,11 @@ def cartPullingControlLoop(
4) ? always true (or do nothing is always true, whatever)
"""
- q = robot.getQ()
+ q = robot.q
if robot.robot_name != "yumi":
- T_w_e = robot.getT_w_e()
+ T_w_e = robot.T_w_e
else:
- T_w_e_left, T_w_e_right = robot.getT_w_e()
+ T_w_e_left, T_w_e_right = robot.T_w_e
# we use binary as string representation (i don't want to deal with python's binary representation).
# the reason for this is that then we don't have disgusting nested ifs
@@ -305,7 +222,7 @@ def cartPullingControlLoop(
# TODO plot priority register out
# log_item['prio_reg'] = ...
log_item["qs"] = q.reshape((robot.model.nq,))
- log_item["dqs"] = robot.getQd().reshape((robot.model.nv,))
+ log_item["dqs"] = robot.v.reshape((robot.model.nv,))
print(priority_register)
return breakFlag, save_past_item, log_item
@@ -321,7 +238,7 @@ def cartPulling(args, robot: RobotManager, goal, path_planner):
############################
# setup cart-pulling mpc #
############################
- x0 = np.concatenate([robot.getQ(), robot.getQd()])
+ x0 = np.concatenate([robot.q, robot.v])
problem_pulling = createBaseAndEEPathFollowingOCP(args, robot, x0)
if args.solver == "boxfddp":
solver_pulling = crocoddyl.SolverBoxFDDP(problem_pulling)
@@ -335,7 +252,7 @@ def cartPulling(args, robot: RobotManager, goal, path_planner):
# setup point-to-point handlebar grasping #
# TODO: have option to swith this for clik #
#############################################
- grasp_pose = robot.getT_w_e()
+ grasp_pose = robot.T_w_e()
problem_grasp = createCrocoIKOCP(args, robot, x0, grasp_pose)
if args.solver == "boxfddp":
solver_grasp = crocoddyl.SolverBoxFDDP(problem_grasp)
@@ -357,13 +274,13 @@ def cartPulling(args, robot: RobotManager, goal, path_planner):
)
log_item = {}
- q = robot.getQ()
+ q = robot.q
if robot.robot_name != "yumi":
- T_w_e = robot.getT_w_e()
+ T_w_e = robot.T_w_e()
else:
- T_w_e_l, T_w_e_right = robot.getT_w_e()
+ T_w_e_l, T_w_e_right = robot.T_w_e()
log_item["qs"] = q.reshape((robot.model.nq,))
- log_item["dqs"] = robot.getQd().reshape((robot.model.nv,))
+ log_item["dqs"] = robot.v.reshape((robot.model.nv,))
# T_base = self.robot_manager.data.oMi[1]
# NOTE: why the fuck was the past path defined from the end-effector?????
# save_past_item = {'path2D_untimed' : T_w_e.translation[:2],
@@ -376,46 +293,29 @@ def cartPulling(args, robot: RobotManager, goal, path_planner):
if __name__ == "__main__":
args = get_args()
- if importlib.util.find_spec("mim_solvers"):
- import mim_solvers
- robot = RobotManager(args)
- robot.q[0] = 9.0
- robot.q[1] = 4.0
+ robot = getRobotFromArgs(args)
+ # TODO: here you have to exit unless the robot has a mobile base interface
+ robot._q[0] = 9.0
+ robot._q[1] = 4.0
- x0 = np.concatenate([robot.getQ(), robot.getQd()])
+ x0 = np.concatenate([robot.q, robot.v])
robot._step()
goal = np.array([0.5, 5.5])
###########################
# visualizing obstacles #
###########################
- _, map_as_list = createMap()
+ _, map_as_list = createSampleStaticMap()
# we're assuming rectangles here
# be my guest and implement other options
- if args.visualize_manipulator:
- for obstacle in map_as_list:
- length = obstacle[1][0] - obstacle[0][0]
- width = obstacle[3][1] - obstacle[0][1]
- height = 0.4 # doesn't matter because plan because planning is 2D
- pose = pin.SE3(
- np.eye(3),
- np.array(
- [
- obstacle[0][0] + (obstacle[1][0] - obstacle[0][0]) / 2,
- obstacle[0][1] + (obstacle[3][1] - obstacle[0][1]) / 2,
- 0.0,
- ]
- ),
- )
- dims = [length, width, height]
- command = {"obstacle": [pose, dims]}
- robot.visualizer_manager.sendCommand(command)
+ if args.visualizer:
+ robot.sendRectangular2DMapToVisualizer(map_as_list)
planning_function = partial(starPlanner, goal)
# TODO: ensure alignment in orientation between planner and actual robot
path_planner = ProcessManager(args, planning_function, robot.q[:2], 3, None)
# wait for meshcat to initialize
- if args.visualize_manipulator:
+ if args.visualizer:
time.sleep(5)
# clik version
# cartesianPathFollowingWithPlanner(args, robot, path_planner)
@@ -427,18 +327,15 @@ if __name__ == "__main__":
# atm this is just mobile base tracking
cartPulling(args, robot, goal, path_planner)
print("final position:")
- print(robot.getT_w_e())
+ print(robot.T_w_e)
path_planner.terminateProcess()
if args.save_log:
- robot.log_manager.plotAllControlLoops()
+ robot._log_manager.saveLog()
+ robot._log_manager.plotAllControlLoops()
if not args.pinocchio_only:
robot.stopRobot()
- if args.visualize_manipulator:
+ if args.visualizer:
robot.killManipulatorVisualizer()
-
- if args.save_log:
- robot.log_manager.saveLog()
- # loop_manager.stopHandler(None, None)
diff --git a/examples/cart_pulling/cart_pulling_notes.md b/examples/cart_pulling/cart_pulling_notes.md
new file mode 100644
index 0000000..95fe861
--- /dev/null
+++ b/examples/cart_pulling/cart_pulling_notes.md
@@ -0,0 +1,93 @@
+# stuff to try
+--------------
+once the the basic version of the control is done you can try putting in something extra to the objective function of the ocp maybe?
+
+# a thing to take care of
+-------------------------
+if the end-effector is not at the starting point of the path, it goes to shit. it can't make the path because the path is constantly changing.
+there are 3 different ways to solve the problem:
+1) don't update the path until the end-effector is close enough
+2) run a behavior tree where you don't do path following unless you are at the path, and have a different controller get you to the point (this should be just initialization)
+3) transform the path differently so that it goes to the desired height, i.e. change the path to make it followable
+
+discussion.
+1) is not a good idea because we want the path planner to run on the side and do it's thing. it is resposible for the path, control is responsible to follow the path. the path is guaranteed to exist, and it is our duty to deal with non-linearity to get us on the path.
+2) a totally valid solution and makes sense in the final version. does add some complexity because you then need to make sure you designed the state transitions well.
+3) just a bit of math, can't fail. does not fuck with the path planner, introduces no new concepts - it's just a different transformation of the 2D path. once put in place there is no reason for it not to work.
+
+conclusion:
+we go with option 2) because option 2) is false! it does not account for orientation! we want a fixed orientation which changes along the path. this makes interpolating to the 6d path weird. hence we need to swap to a clik if the initial error is too big.
+you could also try "freezing" the first point tbh
+
+
+
+
+# the desided joint space position
+name:
+- myumi_001_yumi_robr_joint_1
+- myumi_001_yumi_robr_joint_2
+- myumi_001_yumi_robr_joint_3
+- myumi_001_yumi_robr_joint_4
+- myumi_001_yumi_robr_joint_5
+- myumi_001_yumi_robr_joint_6
+- myumi_001_yumi_robr_joint_7
+- myumi_001_yumi_robl_joint_1
+- myumi_001_yumi_robl_joint_2
+- myumi_001_yumi_robl_joint_3
+- myumi_001_yumi_robl_joint_4
+- myumi_001_yumi_robl_joint_5
+- myumi_001_yumi_robl_joint_6
+- myumi_001_yumi_robl_joint_7
+position:
+- 1.1781062998438787
+- -0.000595335227562165
+- -1.749208945070357
+- 0.4106104712156747
+- -2.060408639509931
+- 0.3044975499507672
+- 1.7246295661055797
+- -0.7019778380440242
+- 0.0394624047824147
+- 1.1381740635109037
+- 0.40438559848372346
+- 1.5945463973962233
+- 0.3724310942226761
+- -1.3882579393586854
+velocity: []
+effort: []
+
+# better
+name:
+- myumi_001_yumi_robr_joint_1
+- myumi_001_yumi_robr_joint_2
+- myumi_001_yumi_robr_joint_3
+- myumi_001_yumi_robr_joint_4
+- myumi_001_yumi_robr_joint_5
+- myumi_001_yumi_robr_joint_6
+- myumi_001_yumi_robr_joint_7
+- myumi_001_yumi_robl_joint_1
+- myumi_001_yumi_robl_joint_2
+- myumi_001_yumi_robl_joint_3
+- myumi_001_yumi_robl_joint_4
+- myumi_001_yumi_robl_joint_5
+- myumi_001_yumi_robl_joint_6
+- myumi_001_yumi_robl_joint_7
+position:
+- 1.177726666230488
+- -0.0014802503425588292
+- -1.7605729197578341
+- 0.4215065284996249
+- -1.9415690706067803
+- 0.2803584248036936
+- 1.6919710884998835
+- -0.7879804975484437
+- 0.22470208644656367
+- 1.3406988091378869
+- 0.35854547216754384
+- 1.7128065956042273
+- 0.22850344929702915
+- -1.5302555581528383
+velocity: []
+effort: []
+
+
diff --git a/examples/cart_pulling/dual_arm_path_following.py b/examples/cart_pulling/dual_arm_path_following.py
new file mode 100644
index 0000000..2b0e218
--- /dev/null
+++ b/examples/cart_pulling/dual_arm_path_following.py
@@ -0,0 +1,2 @@
+# same as path_following_mpc in a circle, just use a dual arm instead.
+# the reference is still a single 6D path, but now with dual arms
diff --git a/examples/cart_pulling/path_following_from_planner.py b/examples/cart_pulling/path_following_from_planner.py
new file mode 100644
index 0000000..54581b9
--- /dev/null
+++ b/examples/cart_pulling/path_following_from_planner.py
@@ -0,0 +1,60 @@
+from smc import getRobotFromArgs
+from smc import getMinimalArgParser
+from smc.path_generation.maps.premade_maps import createSampleStaticMap
+from smc.control.optimal_control.util import get_OCP_args
+from smc.control.cartesian_space import getClikArgs
+from smc.path_generation.planner import starPlanner, getPlanningArgs
+from smc.control.optimal_control.croco_mpc_path_following import (
+ CrocoEndEffectorPathFollowingMPC,
+)
+from smc.multiprocessing import ProcessManager
+
+import time
+import numpy as np
+from functools import partial
+
+
+def get_args():
+ parser = getMinimalArgParser()
+ parser = get_OCP_args(parser)
+ parser = getClikArgs(parser) # literally just for goal error
+ parser = getPlanningArgs(parser)
+ parser.add_argument(
+ "--handlebar-height",
+ type=float,
+ default=0.5,
+ help="heigh of handlebar of the cart to be pulled",
+ )
+ args = parser.parse_args()
+ return args
+
+
+if __name__ == "__main__":
+ args = get_args()
+ robot = getRobotFromArgs(args)
+ robot._q[0] = 9.0
+ robot._q[1] = 4.0
+ robot._step()
+ x0 = np.concatenate([robot.q, robot.v])
+ goal = np.array([0.5, 5.5])
+
+ planning_function = partial(starPlanner, goal)
+ # TODO: ensure alignment in orientation between planner and actual robot
+ path_planner = ProcessManager(args, planning_function, robot.q[:2], 3, None)
+ _, map_as_list = createSampleStaticMap()
+ if args.visualizer:
+ robot.sendRectangular2DMapToVisualizer(map_as_list)
+ time.sleep(5)
+ CrocoEndEffectorPathFollowingMPC(args, robot, x0, path_planner)
+
+ print("final position:", robot.T_w_e)
+
+ if args.real:
+ robot.stopRobot()
+
+ if args.save_log:
+ robot._log_manager.saveLog()
+ robot._log_manager.plotAllControlLoops()
+
+ if args.visualizer:
+ robot.killManipulatorVisualizer()
diff --git a/examples/cart_pulling/pose_initialization.py b/examples/cart_pulling/pose_initialization.py
new file mode 100644
index 0000000..f73a160
--- /dev/null
+++ b/examples/cart_pulling/pose_initialization.py
@@ -0,0 +1,3 @@
+# whatever control you use to get to the starting point for cart pulling.
+# in the past this might have been clik, but we can run with p2p mpc honestly.
+# that too needs to be dual-arm compatible though
diff --git a/python/examples/cart_pulling/starify_nav2_map.py b/examples/cart_pulling/starify_nav2_map.py
similarity index 100%
rename from python/examples/cart_pulling/starify_nav2_map.py
rename to examples/cart_pulling/starify_nav2_map.py
diff --git a/examples/cart_pulling/trajectory_following_mpc.py b/examples/cart_pulling/trajectory_following_mpc.py
new file mode 100644
index 0000000..d7a5393
--- /dev/null
+++ b/examples/cart_pulling/trajectory_following_mpc.py
@@ -0,0 +1 @@
+# same as dual_arm_path_following, but with the trajectory math in it
diff --git a/python/examples/cartesian_space/clik.py b/examples/cartesian_space/clik.py
similarity index 97%
rename from python/examples/cartesian_space/clik.py
rename to examples/cartesian_space/clik.py
index 9383a64..b1a8b84 100644
--- a/python/examples/cartesian_space/clik.py
+++ b/examples/cartesian_space/clik.py
@@ -1,13 +1,10 @@
-# PYTHON_ARGCOMPLETE_OK
from smc import getMinimalArgParser, getRobotFromArgs
-from smc.control.cartesian_space import getClikArgs
from smc.util.define_random_goal import getRandomlyGeneratedGoal
+from smc.control.cartesian_space import getClikArgs
from smc.robots.utils import defineGoalPointCLI
from smc.control.cartesian_space.cartesian_space_point_to_point import moveL
import numpy as np
-
-# import pinocchio as pin
import argparse
diff --git a/python/examples/data/from_writing_to_handover.pickle_save b/examples/data/from_writing_to_handover.pickle_save
similarity index 100%
rename from python/examples/data/from_writing_to_handover.pickle_save
rename to examples/data/from_writing_to_handover.pickle_save
diff --git a/python/examples/data/fts.png b/examples/data/fts.png
similarity index 100%
rename from python/examples/data/fts.png
rename to examples/data/fts.png
diff --git a/python/examples/data/latest_run b/examples/data/latest_run
similarity index 100%
rename from python/examples/data/latest_run
rename to examples/data/latest_run
diff --git a/python/examples/data/latest_run_0 b/examples/data/latest_run_0
similarity index 100%
rename from python/examples/data/latest_run_0
rename to examples/data/latest_run_0
diff --git a/python/examples/drawing/drawing_from_input_drawing.py b/examples/drawing/drawing_from_input_drawing.py
similarity index 100%
rename from python/examples/drawing/drawing_from_input_drawing.py
rename to examples/drawing/drawing_from_input_drawing.py
diff --git a/python/examples/drawing/plane_pose.pickle_save b/examples/drawing/plane_pose.pickle_save
similarity index 100%
rename from python/examples/drawing/plane_pose.pickle_save
rename to examples/drawing/plane_pose.pickle_save
diff --git a/python/examples/drawing/wiping_path.csv_save b/examples/drawing/wiping_path.csv_save
similarity index 100%
rename from python/examples/drawing/wiping_path.csv_save
rename to examples/drawing/wiping_path.csv_save
diff --git a/python/examples/graz/cartesian_pose_command_server.py b/examples/graz/cartesian_pose_command_server.py
similarity index 100%
rename from python/examples/graz/cartesian_pose_command_server.py
rename to examples/graz/cartesian_pose_command_server.py
diff --git a/python/examples/graz/clik_client.py b/examples/graz/clik_client.py
similarity index 100%
rename from python/examples/graz/clik_client.py
rename to examples/graz/clik_client.py
diff --git a/python/examples/graz/clik_m.py b/examples/graz/clik_m.py
similarity index 100%
rename from python/examples/graz/clik_m.py
rename to examples/graz/clik_m.py
diff --git a/python/examples/graz/joint_copier_client.py b/examples/graz/joint_copier_client.py
similarity index 100%
rename from python/examples/graz/joint_copier_client.py
rename to examples/graz/joint_copier_client.py
diff --git a/python/examples/graz/message_specs_pb2.py b/examples/graz/message_specs_pb2.py
similarity index 100%
rename from python/examples/graz/message_specs_pb2.py
rename to examples/graz/message_specs_pb2.py
diff --git a/python/examples/graz/point_impedance_server.py b/examples/graz/point_impedance_server.py
similarity index 100%
rename from python/examples/graz/point_impedance_server.py
rename to examples/graz/point_impedance_server.py
diff --git a/python/examples/graz/protobuf_ros1_bridge.py b/examples/graz/protobuf_ros1_bridge.py
similarity index 100%
rename from python/examples/graz/protobuf_ros1_bridge.py
rename to examples/graz/protobuf_ros1_bridge.py
diff --git a/python/examples/graz/protobuf_to_ros1.py b/examples/graz/protobuf_to_ros1.py
similarity index 100%
rename from python/examples/graz/protobuf_to_ros1.py
rename to examples/graz/protobuf_to_ros1.py
diff --git a/python/examples/graz/ros1_to_protobuf.py b/examples/graz/ros1_to_protobuf.py
similarity index 100%
rename from python/examples/graz/ros1_to_protobuf.py
rename to examples/graz/ros1_to_protobuf.py
diff --git a/python/examples/graz/to_install_on_ubuntu18.md b/examples/graz/to_install_on_ubuntu18.md
similarity index 100%
rename from python/examples/graz/to_install_on_ubuntu18.md
rename to examples/graz/to_install_on_ubuntu18.md
diff --git a/python/examples/graz/traiettoria.m b/examples/graz/traiettoria.m
similarity index 100%
rename from python/examples/graz/traiettoria.m
rename to examples/graz/traiettoria.m
diff --git a/python/examples/impedance/point_impedance_control.py b/examples/impedance/point_impedance_control.py
similarity index 100%
rename from python/examples/impedance/point_impedance_control.py
rename to examples/impedance/point_impedance_control.py
diff --git a/python/examples/log_analysis/comparing_logs_example.py b/examples/log_analysis/comparing_logs_example.py
similarity index 100%
rename from python/examples/log_analysis/comparing_logs_example.py
rename to examples/log_analysis/comparing_logs_example.py
diff --git a/python/examples/log_analysis/log_analysis_example.py b/examples/log_analysis/log_analysis_example.py
similarity index 100%
rename from python/examples/log_analysis/log_analysis_example.py
rename to examples/log_analysis/log_analysis_example.py
diff --git a/python/examples/old_or_experimental/clik_old.py b/examples/old_or_experimental/clik_old.py
similarity index 100%
rename from python/examples/old_or_experimental/clik_old.py
rename to examples/old_or_experimental/clik_old.py
diff --git a/python/examples/old_or_experimental/force_control/force_control_test.py b/examples/old_or_experimental/force_control/force_control_test.py
similarity index 100%
rename from python/examples/old_or_experimental/force_control/force_control_test.py
rename to examples/old_or_experimental/force_control/force_control_test.py
diff --git a/python/examples/old_or_experimental/force_control/force_mode_api.py b/examples/old_or_experimental/force_control/force_mode_api.py
similarity index 100%
rename from python/examples/old_or_experimental/force_control/force_mode_api.py
rename to examples/old_or_experimental/force_control/force_mode_api.py
diff --git a/python/examples/old_or_experimental/force_control/forcemode_example.py b/examples/old_or_experimental/force_control/forcemode_example.py
similarity index 100%
rename from python/examples/old_or_experimental/force_control/forcemode_example.py
rename to examples/old_or_experimental/force_control/forcemode_example.py
diff --git a/python/examples/old_or_experimental/force_control/point_force_control.py b/examples/old_or_experimental/force_control/point_force_control.py
similarity index 100%
rename from python/examples/old_or_experimental/force_control/point_force_control.py
rename to examples/old_or_experimental/force_control/point_force_control.py
diff --git a/python/examples/old_or_experimental/ft_calibration_via_amperes_test.py b/examples/old_or_experimental/ft_calibration_via_amperes_test.py
similarity index 100%
rename from python/examples/old_or_experimental/ft_calibration_via_amperes_test.py
rename to examples/old_or_experimental/ft_calibration_via_amperes_test.py
diff --git a/python/examples/old_or_experimental/test_crocoddyl_opt_ctrl.py b/examples/old_or_experimental/test_crocoddyl_opt_ctrl.py
similarity index 100%
rename from python/examples/old_or_experimental/test_crocoddyl_opt_ctrl.py
rename to examples/old_or_experimental/test_crocoddyl_opt_ctrl.py
diff --git a/python/examples/old_or_experimental/test_gripper.py b/examples/old_or_experimental/test_gripper.py
similarity index 100%
rename from python/examples/old_or_experimental/test_gripper.py
rename to examples/old_or_experimental/test_gripper.py
diff --git a/python/examples/old_or_experimental/test_traj_w_movej.py b/examples/old_or_experimental/test_traj_w_movej.py
similarity index 100%
rename from python/examples/old_or_experimental/test_traj_w_movej.py
rename to examples/old_or_experimental/test_traj_w_movej.py
diff --git a/python/examples/old_or_experimental/test_traj_w_speedj.py b/examples/old_or_experimental/test_traj_w_speedj.py
similarity index 100%
rename from python/examples/old_or_experimental/test_traj_w_speedj.py
rename to examples/old_or_experimental/test_traj_w_speedj.py
diff --git a/python/examples/optimal_control/casadi_ocp_collision_avoidance.py b/examples/optimal_control/casadi_ocp_collision_avoidance.py
similarity index 100%
rename from python/examples/optimal_control/casadi_ocp_collision_avoidance.py
rename to examples/optimal_control/casadi_ocp_collision_avoidance.py
diff --git a/python/examples/optimal_control/croco_ik_mpc.py b/examples/optimal_control/croco_ik_mpc.py
similarity index 100%
rename from python/examples/optimal_control/croco_ik_mpc.py
rename to examples/optimal_control/croco_ik_mpc.py
diff --git a/python/examples/optimal_control/croco_ik_ocp.py b/examples/optimal_control/croco_ik_ocp.py
similarity index 100%
rename from python/examples/optimal_control/croco_ik_ocp.py
rename to examples/optimal_control/croco_ik_ocp.py
diff --git a/examples/optimal_control/path_following_mpc.py b/examples/optimal_control/path_following_mpc.py
new file mode 100644
index 0000000..4393525
--- /dev/null
+++ b/examples/optimal_control/path_following_mpc.py
@@ -0,0 +1,53 @@
+from smc import getRobotFromArgs
+from smc import getMinimalArgParser
+from smc.control.optimal_control.util import get_OCP_args
+from smc.control.cartesian_space import getClikArgs
+from smc.control.optimal_control.croco_mpc_path_following import (
+ CrocoEndEffectorPathFollowingMPC,
+)
+import numpy as np
+
+
+def path(T_w_e, i):
+ # 2) do T_mobile_base_ee_pos to get
+ # end-effector reference frame(s)
+
+ # generate bullshit just to see it works
+ path = []
+ t = i * robot.dt
+ for i in range(args.n_knots):
+ t += 0.01
+ new = T_w_e.copy()
+ translation = 2 * np.array([np.cos(t / 20), np.sin(t / 20), 0.3])
+ new.translation = translation
+ path.append(new)
+ return path
+
+
+def get_args():
+ parser = getMinimalArgParser()
+ parser = get_OCP_args(parser)
+ parser = getClikArgs(parser) # literally just for goal error
+ args = parser.parse_args()
+ return args
+
+
+if __name__ == "__main__":
+ args = get_args()
+ robot = getRobotFromArgs(args)
+ x0 = np.concatenate([robot.q, robot.v])
+ robot._step()
+
+ CrocoEndEffectorPathFollowingMPC(args, robot, x0, path)
+
+ print("final position:", robot.T_w_e)
+
+ if args.real:
+ robot.stopRobot()
+
+ if args.save_log:
+ robot._log_manager.saveLog()
+ robot._log_manager.plotAllControlLoops()
+
+ if args.visualizer:
+ robot.killManipulatorVisualizer()
diff --git a/python/examples/path6d_from_path2d.py b/examples/path6d_from_path2d.py
similarity index 75%
rename from python/examples/path6d_from_path2d.py
rename to examples/path6d_from_path2d.py
index 11334ef..a2636e2 100644
--- a/python/examples/path6d_from_path2d.py
+++ b/examples/path6d_from_path2d.py
@@ -1,43 +1,19 @@
-# PYTHON_ARGCOMPLETE_OK
import numpy as np
import time
-import argparse, argcomplete
from functools import partial
-from smc.managers import (
- getMinimalArgParser,
- ControlLoopManager,
- RobotManager,
- ProcessManager,
-)
-from smc.optimal_control.get_ocp_args import get_OCP_args
-from smc.optimal_control.crocoddyl_optimal_control import *
-from smc.optimal_control.crocoddyl_mpc import *
-from smc.basics.basics import followKinematicJointTrajP
-from smc.util.logging_utils import LogManager
-from smc.visualize.visualize import plotFromDict
-from smc.clik.clik import (
- getClikArgs,
- cartesianPathFollowingWithPlanner,
- controlLoopClik,
- invKinmQP,
- dampedPseudoinverse,
-)
-import pinocchio as pin
-import crocoddyl
+from smc import getMinimalArgParser
+from smc.multiprocessing.process_manager import ProcessManager
+from smc.control.cartesian_space import getClikArgs
from functools import partial
-import importlib.util
-from smc.path_generation.planner import starPlanner, getPlanningArgs, createMap
+from smc.path_generation.planner import getPlanningArgs
import yaml
import numpy as np
from functools import partial
-from smc.managers import ProcessManager, getMinimalArgParser
-from smc.util.get_model import heron_approximation
-from smc.visualize.visualize import plotFromDict, realTimePlotter, manipulatorVisualizer
-from smc.path_generation.planner import (
- path2D_timed,
- pathPointFromPathParam,
- path2D_to_SE3,
-)
+from smc.robots.implementations.heron import heron_approximation
+from smc.visualization.visualizer import manipulatorVisualizer
+from smc.path_generation.path_math.path2d_to_6d import path2D_to_SE3_fixed
+from smc.path_generation.path_math.path_to_trajectory import path2D_timed
+from smc.control.optimal_control.util import get_OCP_args
def get_args():
@@ -57,7 +33,6 @@ def get_args():
default=0.7,
help="prefered path arclength from mobile base position to handlebar",
)
- argcomplete.autocomplete(parser)
args = parser.parse_args()
# TODO TODO TODO: REMOVE PRESET HACKS
robot_type = "Unicycle"
@@ -112,7 +87,7 @@ path2D_untimed_base = np.array(path2D_untimed_base).reshape((-1, 2))
# ideally should be precomputed somewhere
# base just needs timing on the path,
# and it's of height 0 (i.e. the height of base's planar joint)
-path_base = path2D_timed(args, None, path2D_untimed_base, max_base_v, 0.0)
+# path_base = path2D_timed(args, None, path2D_untimed_base, max_base_v, 0.0)
path_arclength = np.linalg.norm(p - past_data["path2D_untimed"])
handlebar_path_index = -1
@@ -137,7 +112,7 @@ path2D_handlebar_1 = np.hstack(
)
# TODO: combine with base for maximum correctness
-pathSE3_handlebar = path2D_to_SE3(path2D_handlebar_1, args.handlebar_height)
+pathSE3_handlebar = path2D_to_SE3_fixed(path2D_handlebar_1, args.handlebar_height)
for p in pathSE3_handlebar:
print(p)
diff --git a/python/examples/pinocchio_math_understanding/pin_contact3d.py b/examples/pinocchio_math_understanding/pin_contact3d.py
similarity index 100%
rename from python/examples/pinocchio_math_understanding/pin_contact3d.py
rename to examples/pinocchio_math_understanding/pin_contact3d.py
diff --git a/python/examples/pushing_via_friction_cones.py b/examples/pushing_via_friction_cones.py
similarity index 100%
rename from python/examples/pushing_via_friction_cones.py
rename to examples/pushing_via_friction_cones.py
diff --git a/python/examples/ros/dummy_cmds_pub.py b/examples/ros/dummy_cmds_pub.py
similarity index 100%
rename from python/examples/ros/dummy_cmds_pub.py
rename to examples/ros/dummy_cmds_pub.py
diff --git a/python/examples/ros/ros2_clik.py b/examples/ros/ros2_clik.py
similarity index 100%
rename from python/examples/ros/ros2_clik.py
rename to examples/ros/ros2_clik.py
diff --git a/python/examples/ros/smc_node.py b/examples/ros/smc_node.py
similarity index 100%
rename from python/examples/ros/smc_node.py
rename to examples/ros/smc_node.py
diff --git a/python/examples/ros/smc_yumi_challenge.py b/examples/ros/smc_yumi_challenge.py
similarity index 100%
rename from python/examples/ros/smc_yumi_challenge.py
rename to examples/ros/smc_yumi_challenge.py
diff --git a/python/examples/test_by_running.sh b/examples/test_by_running.sh
similarity index 100%
rename from python/examples/test_by_running.sh
rename to examples/test_by_running.sh
diff --git a/python/examples/vision/camera_no_lag.py b/examples/vision/camera_no_lag.py
similarity index 100%
rename from python/examples/vision/camera_no_lag.py
rename to examples/vision/camera_no_lag.py
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diff --git a/python/examples/cart_pulling/cart_pulling_notes.md b/python/examples/cart_pulling/cart_pulling_notes.md
deleted file mode 100644
index 928c8c4..0000000
--- a/python/examples/cart_pulling/cart_pulling_notes.md
+++ /dev/null
@@ -1,67 +0,0 @@
-# the desided joint space position
-name:
-- myumi_001_yumi_robr_joint_1
-- myumi_001_yumi_robr_joint_2
-- myumi_001_yumi_robr_joint_3
-- myumi_001_yumi_robr_joint_4
-- myumi_001_yumi_robr_joint_5
-- myumi_001_yumi_robr_joint_6
-- myumi_001_yumi_robr_joint_7
-- myumi_001_yumi_robl_joint_1
-- myumi_001_yumi_robl_joint_2
-- myumi_001_yumi_robl_joint_3
-- myumi_001_yumi_robl_joint_4
-- myumi_001_yumi_robl_joint_5
-- myumi_001_yumi_robl_joint_6
-- myumi_001_yumi_robl_joint_7
-position:
-- 1.1781062998438787
-- -0.000595335227562165
-- -1.749208945070357
-- 0.4106104712156747
-- -2.060408639509931
-- 0.3044975499507672
-- 1.7246295661055797
-- -0.7019778380440242
-- 0.0394624047824147
-- 1.1381740635109037
-- 0.40438559848372346
-- 1.5945463973962233
-- 0.3724310942226761
-- -1.3882579393586854
-velocity: []
-effort: []
-
-# better
-name:
-- myumi_001_yumi_robr_joint_1
-- myumi_001_yumi_robr_joint_2
-- myumi_001_yumi_robr_joint_3
-- myumi_001_yumi_robr_joint_4
-- myumi_001_yumi_robr_joint_5
-- myumi_001_yumi_robr_joint_6
-- myumi_001_yumi_robr_joint_7
-- myumi_001_yumi_robl_joint_1
-- myumi_001_yumi_robl_joint_2
-- myumi_001_yumi_robl_joint_3
-- myumi_001_yumi_robl_joint_4
-- myumi_001_yumi_robl_joint_5
-- myumi_001_yumi_robl_joint_6
-- myumi_001_yumi_robl_joint_7
-position:
-- 1.177726666230488
-- -0.0014802503425588292
-- -1.7605729197578341
-- 0.4215065284996249
-- -1.9415690706067803
-- 0.2803584248036936
-- 1.6919710884998835
-- -0.7879804975484437
-- 0.22470208644656367
-- 1.3406988091378869
-- 0.35854547216754384
-- 1.7128065956042273
-- 0.22850344929702915
-- -1.5302555581528383
-velocity: []
-effort: []
diff --git a/python/examples/data/clik_comparison_0.pickle b/python/examples/data/clik_comparison_0.pickle
deleted file mode 100644
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diff --git a/python/examples/data/joint_trajectory.csv b/python/examples/data/joint_trajectory.csv
deleted file mode 100644
index 582ecca..0000000
--- a/python/examples/data/joint_trajectory.csv
+++ /dev/null
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diff --git a/python/examples/data/path_in_pixels.csv b/python/examples/data/path_in_pixels.csv
deleted file mode 100644
index de74278..0000000
--- a/python/examples/data/path_in_pixels.csv
+++ /dev/null
@@ -1,20 +0,0 @@
-0.11902,0.81172
-0.12940,0.79825
-0.16945,0.76234
-0.23471,0.68903
-0.31331,0.61572
-0.34001,0.58579
-0.36522,0.56335
-0.43938,0.50649
-0.44976,0.49602
-0.48090,0.47208
-0.51798,0.45413
-0.53578,0.44216
-0.55803,0.43019
-0.57138,0.42121
-0.58621,0.40924
-0.59362,0.40326
-0.60104,0.39428
-0.62329,0.35837
-0.63070,0.34640
-0.63070,0.34640
diff --git a/python/examples/optimal_control/path_following_mpc.py b/python/examples/optimal_control/path_following_mpc.py
deleted file mode 100644
index 5563f29..0000000
--- a/python/examples/optimal_control/path_following_mpc.py
+++ /dev/null
@@ -1,82 +0,0 @@
-# PYTHON_ARGCOMPLETE_OK
-import numpy as np
-import time
-import argparse, argcomplete
-from functools import partial
-from smc.managers import getMinimalArgParser, ControlLoopManager, RobotManager
-from smc.optimal_control.get_ocp_args import get_OCP_args
-from smc.optimal_control.crocoddyl_optimal_control import (
- createCrocoEEPathFollowingOCP,
- solveCrocoOCP,
-)
-from smc.optimal_control.crocoddyl_mpc import (
- CrocoEndEffectorPathFollowingMPCControlLoop,
- CrocoEndEffectorPathFollowingMPC,
-)
-from smc.basics.basics import followKinematicJointTrajP
-from smc.util.logging_utils import LogManager
-from smc.visualize.visualize import plotFromDict
-from smc.clik.clik import getClikArgs
-import pinocchio as pin
-import crocoddyl
-import importlib.util
-
-
-def path(T_w_e, i):
- # 2) do T_mobile_base_ee_pos to get
- # end-effector reference frame(s)
-
- # generate bullshit just to see it works
- path = []
- t = i * robot.dt
- for i in range(args.n_knots):
- t += 0.01
- new = T_w_e.copy()
- translation = 2 * np.array([np.cos(t / 20), np.sin(t / 20), 0.3])
- new.translation = translation
- path.append(new)
- return path
-
-
-def get_args():
- parser = getMinimalArgParser()
- parser = get_OCP_args(parser)
- parser = getClikArgs(parser) # literally just for goal error
- argcomplete.autocomplete(parser)
- args = parser.parse_args()
- return args
-
-
-if __name__ == "__main__":
- args = get_args()
- if importlib.util.find_spec("mim_solvers"):
- import mim_solvers
- robot = RobotManager(args)
- # time.sleep(5)
-
- # create and solve the optimal control problem of
- # getting from current to goal end-effector position.
- # reference is position and velocity reference (as a dictionary),
- # while solver is a crocoddyl object containing a lot more information
- # starting state
- x0 = np.concatenate([robot.getQ(), robot.getQd()])
- robot._step()
-
- problem = createCrocoEEPathFollowingOCP(args, robot, x0)
- CrocoEndEffectorPathFollowingMPC(args, robot, x0, path)
-
- print("final position:")
- print(robot.getT_w_e())
-
- if args.save_log:
- robot.log_manager.plotAllControlLoops()
-
- if not args.pinocchio_only:
- robot.stopRobot()
-
- if args.visualize_manipulator:
- robot.killManipulatorVisualizer()
-
- if args.save_log:
- robot.log_manager.saveLog()
- # loop_manager.stopHandler(None, None)
diff --git a/python/smc/control/cartesian_space/cartesian_space_trajectory_following.py b/python/smc/control/cartesian_space/cartesian_space_trajectory_following.py
index 3626c7c..6eaf369 100644
--- a/python/smc/control/cartesian_space/cartesian_space_trajectory_following.py
+++ b/python/smc/control/cartesian_space/cartesian_space_trajectory_following.py
@@ -5,13 +5,7 @@ from functools import partial
import pinocchio as pin
import numpy as np
-from importlib.util import find_spec
-
-if find_spec("shapely"):
- from smc.path_generation.planner import (
- path2D_to_timed_SE3,
- pathPointFromPathParam,
- )
+from smc.path_generation.path_math.path_to_trajectory import path2D_to_timed_SE3
def cartesianPathFollowingWithPlannerControlLoop(
@@ -27,7 +21,7 @@ def cartesianPathFollowingWithPlannerControlLoop(
save_past_dict = {}
q = robot.q
- T_w_e = robot.T_w_e()
+ T_w_e = robot.T_w_e
p = T_w_e.translation[:2]
path_planner.sendFreshestCommand({"p": p})
diff --git a/python/smc/control/control_loop_manager.py b/python/smc/control/control_loop_manager.py
index d85d12f..47fc612 100644
--- a/python/smc/control/control_loop_manager.py
+++ b/python/smc/control/control_loop_manager.py
@@ -71,6 +71,9 @@ class ControlLoopManager:
self.args = args
self.iter_n = 0
self.past_data = {}
+ # NOTE: viz update rate is a magic number that seems to work fine and i don't have
+ # any plans to make it smarter
+ self.viz_update_rate = int(np.ceil(self.args.ctrl_freq / 25))
# save_past_dict has to have the key and 1 example of what you're saving
# so that it's type can be inferred (but we're in python so types don't really work).
# the good thing is this way you also immediatelly put in the initial values
@@ -132,7 +135,7 @@ class ControlLoopManager:
# but if not, we want to control the speed of the simulation,
# and how much we're plotting.
# so this should be an argument that is use ONLY if we're in simulation
- if i % 20 == 0:
+ if i % self.viz_update_rate == 0:
# don't send what wasn't ready
if self.args.visualizer:
if self.robot_manager.robot_name != "yumi":
diff --git a/python/smc/control/joint_space/joint_space_trajectory_following.py b/python/smc/control/joint_space/joint_space_trajectory_following.py
index a787aee..dc5ee95 100644
--- a/python/smc/control/joint_space/joint_space_trajectory_following.py
+++ b/python/smc/control/joint_space/joint_space_trajectory_following.py
@@ -67,11 +67,7 @@ def followKinematicJointTrajPControlLoop(
reference["vs"][t_index_lower] * time_difference,
)
- # TODO: why not use pin.difference for both?
- if robot.robot_name == "ur5e":
- error_q = q_ref - q
- if robot.robot_name == "heron":
- error_q = pin.difference(robot.model, q, q_ref) # / robot.dt
+ error_q = pin.difference(robot.model, q, q_ref)
error_v = v_ref - v
Kp = 1.0
Kd = 0.5
@@ -98,7 +94,7 @@ def followKinematicJointTrajP(args, robot, reference):
followKinematicJointTrajPControlLoop, args.stop_at_final, robot, reference
)
log_item = {
- "error_qs": np.zeros(robot.model.nq),
+ "error_qs": np.zeros(robot.model.nv), # differences live in the tangent space
"error_vs": np.zeros(robot.model.nv),
"qs": np.zeros(robot.model.nq),
"vs": np.zeros(robot.model.nv),
diff --git a/python/smc/control/optimal_control/abstract_croco_ocp.py b/python/smc/control/optimal_control/abstract_croco_ocp.py
index 867d351..fd68018 100644
--- a/python/smc/control/optimal_control/abstract_croco_ocp.py
+++ b/python/smc/control/optimal_control/abstract_croco_ocp.py
@@ -223,7 +223,6 @@ class CrocoOCP(abc.ABC):
0.0,
)
-
@abc.abstractmethod
def constructTaskObjectiveFunction(self, goal) -> None: ...
@@ -242,10 +241,10 @@ class CrocoOCP(abc.ABC):
# solver = crocoddyl.SolverIpopt(problem)
# TODO: select other solvers from arguments
self.solver = crocoddyl.SolverBoxFDDP(self.problem)
- # TODO: make these callbacks optional
- self.solver.setCallbacks(
- [crocoddyl.CallbackVerbose(), crocoddyl.CallbackLogger()]
- )
+ if self.args.debug_prints:
+ self.solver.setCallbacks(
+ [crocoddyl.CallbackVerbose(), crocoddyl.CallbackLogger()]
+ )
# NOTE: used by csqp
def createMimSolver(self) -> None:
@@ -259,10 +258,10 @@ class CrocoOCP(abc.ABC):
# solver = mim_solvers.SolverSQP(problem)
# TODO: select other solvers from arguments
self.solver = mim_solvers.SolverCSQP(self.problem)
- # TODO: make these callbacks optional
- self.solver.setCallbacks(
- [mim_solvers.CallbackVerbose(), mim_solvers.CallbackLogger()]
- )
+ if self.args.debug_prints:
+ self.solver.setCallbacks(
+ [mim_solvers.CallbackVerbose(), mim_solvers.CallbackLogger()]
+ )
# this shouldn't really depend on x0 but i can't be bothered
def solveOCP(self, x0: np.ndarray) -> tuple[dict, Any]:
diff --git a/python/smc/control/optimal_control/croco_mpc_path_following.py b/python/smc/control/optimal_control/croco_mpc_path_following.py
index 7e63a89..2ce182f 100644
--- a/python/smc/control/optimal_control/croco_mpc_path_following.py
+++ b/python/smc/control/optimal_control/croco_mpc_path_following.py
@@ -5,6 +5,11 @@ from smc.control.optimal_control.path_following_croco_ocp import (
CrocoEEPathFollowingOCP,
BaseAndEEPathFollowingOCP,
)
+from smc.path_generation.path_math.path2d_to_6d import (
+ path2D_to_SE3_fixed,
+ path2D_to_SE3_with_transition_from_current_pose,
+)
+from smc.path_generation.path_math.path_to_trajectory import path2D_timed
import crocoddyl
import numpy as np
@@ -20,14 +25,6 @@ if importlib.util.find_spec("mim_solvers"):
"mim_solvers installation is broken, rebuild and reinstall it if you want it"
)
-# TODO: put others here too
-if importlib.util.find_spec("shapely"):
- from smc.path_generation.planner import (
- path2D_timed,
- pathPointFromPathParam,
- path2D_to_SE3,
- )
-
def CrocoEndEffectorPathFollowingMPCControlLoop(
args,
@@ -35,7 +32,7 @@ def CrocoEndEffectorPathFollowingMPCControlLoop(
solver: crocoddyl.SolverBoxFDDP,
path_planner: ProcessManager,
t,
- past_data,
+ _,
):
"""
CrocoPathFollowingMPCControlLoop
@@ -51,7 +48,7 @@ def CrocoEndEffectorPathFollowingMPCControlLoop(
save_past_dict = {}
q = robot.q
- T_w_e = robot.getT_w_e()
+ T_w_e = robot.T_w_e
p = T_w_e.translation[:2]
# NOTE: it's pointless to recalculate the path every time
@@ -64,7 +61,7 @@ def CrocoEndEffectorPathFollowingMPCControlLoop(
data = path_planner.getData()
if data == None:
if args.debug_prints:
- print("CTRL: got no path so not i won't move")
+ print("CTRL: got no path so i won't move")
robot.sendVelocityCommand(np.zeros(robot.model.nv))
log_item["qs"] = q.reshape((robot.model.nq,))
log_item["dqs"] = robot.v.reshape((robot.model.nv,))
@@ -82,11 +79,12 @@ def CrocoEndEffectorPathFollowingMPCControlLoop(
path2D = path2D_timed(args, path2D_untimed, max_base_v)
# create a 3D reference out of the path
- pathSE3 = path2D_to_SE3(path2D, args.handlebar_height)
+ pathSE3 = path2D_to_SE3_fixed(path2D, args.handlebar_height)
# TODO: EVIL AND HAS TO BE REMOVED FROM HERE
- if args.visualize_manipulator:
- if t % 20 == 0:
+ if args.visualizer:
+ # if t % 20 == 0:
+ if t % int(np.ceil(args.ctrl_freq / 25)) == 0:
robot.visualizer_manager.sendCommand({"frame_path": pathSE3})
x0 = np.concatenate([robot.q, robot.v])
@@ -130,11 +128,8 @@ def CrocoEndEffectorPathFollowingMPC(args, robot, x0, path_planner):
are actually extracted from the state x(q,dq).
"""
- problem = createCrocoEEPathFollowingOCP(args, robot, x0)
- if args.solver == "boxfddp":
- solver = crocoddyl.SolverBoxFDDP(problem)
- if args.solver == "csqp":
- solver = mim_solvers.SolverCSQP(problem)
+ ocp = CrocoEEPathFollowingOCP(args, robot, x0)
+ solver = ocp.getSolver()
# technically should be done in controlloop because now
# it's solved 2 times before the first command,
@@ -277,7 +272,7 @@ def BaseAndEEPathFollowingMPCControlLoop(
pathSE3_handlebar_left.append(goal_transform.act(pathSE3))
pathSE3_handlebar_right.append(goal_transform.inverse().act(pathSE3))
- if args.visualize_manipulator:
+ if args.visualizer:
if iter_n % 20 == 0:
robot.visualizer_manager.sendCommand({"path": path_base})
robot.visualizer_manager.sendCommand({"frame_path": pathSE3_handlebar})
diff --git a/python/smc/control/optimal_control/point_to_point_croco_ocp.py b/python/smc/control/optimal_control/point_to_point_croco_ocp.py
index 39c6e19..6748a09 100644
--- a/python/smc/control/optimal_control/point_to_point_croco_ocp.py
+++ b/python/smc/control/optimal_control/point_to_point_croco_ocp.py
@@ -17,7 +17,6 @@ class SingleArmIKOCP(CrocoOCP):
T_w_goal: pin.SE3,
):
super().__init__(args, robot, x0, T_w_goal)
- self.constructTaskObjectiveFunction(T_w_goal)
def constructTaskObjectiveFunction(self, goal):
T_w_goal = goal
@@ -49,8 +48,12 @@ class SingleArmIKOCP(CrocoOCP):
self.state, frameVelocityResidual
)
for i in range(self.args.n_knots):
- self.runningCostModels[i].addCost("gripperPose", goalTrackingCost, 1e2)
- self.terminalCostModel.addCost("gripperPose", goalTrackingCost, 1e2)
+ self.runningCostModels[i].addCost(
+ "gripperPose" + str(i), goalTrackingCost, 1e2
+ )
+ self.terminalCostModel.addCost(
+ "gripperPose" + str(self.args.n_knots), goalTrackingCost, 1e2
+ )
self.terminalCostModel.addCost("velFinal", frameVelocityCost, 1e3)
@@ -99,9 +102,17 @@ class DualArmIKOCP(CrocoOCP):
self.state, frameVelocityResidual_r
)
for i in range(self.args.n_knots):
- self.runningCostModels[i].addCost("gripperPose_l", goalTrackingCost_l, 1e2)
- self.runningCostModels[i].addCost("gripperPose_r", goalTrackingCost_r, 1e2)
- self.terminalCostModel.addCost("gripperPose_l", goalTrackingCost_l, 1e2)
- self.terminalCostModel.addCost("gripperPose_r", goalTrackingCost_r, 1e2)
+ self.runningCostModels[i].addCost(
+ "gripperPose_l" + str(i), goalTrackingCost_l, 1e2
+ )
+ self.runningCostModels[i].addCost(
+ "gripperPose_r" + str(i), goalTrackingCost_r, 1e2
+ )
+ self.terminalCostModel.addCost(
+ "gripperPose_l" + str(self.args.n_knots), goalTrackingCost_l, 1e2
+ )
+ self.terminalCostModel.addCost(
+ "gripperPose_r" + str(self.args.n_knots), goalTrackingCost_r, 1e2
+ )
self.terminalCostModel.addCost("velFinal_l", frameVelocityCost_l, 1e3)
self.terminalCostModel.addCost("velFinal_r", frameVelocityCost_r, 1e3)
diff --git a/python/smc/path_generation/OLD_sebas_path_generator_WITH_EVERYTHING.py b/python/smc/path_generation/OLD_sebas_path_generator_WITH_EVERYTHING.py
new file mode 100644
index 0000000..3f6bea6
--- /dev/null
+++ b/python/smc/path_generation/OLD_sebas_path_generator_WITH_EVERYTHING.py
@@ -0,0 +1,629 @@
+from typing import List
+from abc import ABC, abstractmethod
+
+import numpy as np
+from starworlds.obstacles import StarshapedObstacle
+from starworlds.starshaped_hull import cluster_and_starify, ObstacleCluster
+from starworlds.utils.misc import tic, toc
+import shapely
+import yaml
+
+import matplotlib.pyplot as plt
+import matplotlib.collections as plt_col
+
+
+
+### mobile_robot.py
+
+class MobileRobot(ABC):
+
+ def __init__(self, nu, nx, width, name, u_min=None, u_max=None, x_min=None, x_max=None):
+ """
+ nx : int number of state variables
+ nu : int number of input variables
+ baseline : float distance between the wheels
+ name : str the name of the robot
+ u_min : np.ndarray minimum input value
+ u_max : np.ndarray maximum input value
+ x_min : np.ndarray minimum state value
+ x_max : np.ndarray maximum state value
+ """
+ self.nx = nx
+ self.nu = nu
+ self.width = width
+ self.name = name
+ def valid_u_bound(bound): return bound is not None and len(bound) == self.nu
+ def valid_q_bound(bound): return bound is not None and len(bound) == self.nx
+ self.u_min = u_min if valid_u_bound(u_min) else [-np.inf] * self.nu
+ self.u_max = u_max if valid_u_bound(u_max) else [np.inf] * self.nu
+ self.x_min = x_min if valid_q_bound(x_min) else [-np.inf] * self.nx
+ self.x_max = x_max if valid_q_bound(x_max) else [np.inf] * self.nx
+
+ @abstractmethod
+ def f(self, x, u):
+ """ Forward dynamics ? """
+ pass
+
+ @abstractmethod
+ def h(self, q):
+ """ Forward kinematics """
+ pass
+
+ @abstractmethod
+ def vel_min(self):
+ pass
+
+ @abstractmethod
+ def vel_max(self):
+ pass
+
+ def move(self, x, u, dt):
+ u_sat = np.clip(u, self.u_min, self.u_max)
+ x_next = x + np.array(self.f(x, u_sat)) * dt
+ x_next = np.clip(x_next, self.x_min, self.x_max)
+ return x_next, u_sat
+
+
+class Unicycle(MobileRobot):
+
+ def __init__(self, width, vel_min=None, vel_max=None, name='robot'):
+ self.vmax = vel_max[0]
+ super().__init__(nu=2, nx=3, width=width, name=name, u_min=vel_min, u_max=vel_max)
+
+ def f(self, x, u):
+ return [u[0] * np.cos(x[2]), # vx
+ u[0] * np.sin(x[2]), # vy
+ u[1]] # wz
+
+ def h(self, x):
+ return x[:2] # x, y
+
+ def vel_min(self):
+ return self.u_min
+
+ def vel_max(self):
+ return self.u_max
+
+ def init_plot(self, ax=None, color='b', alpha=0.7, markersize=10):
+ handles, ax = super(Unicycle, self).init_plot(ax=ax, color=color, alpha=alpha)
+ handles += ax.plot(0, 0, marker=(3, 0, np.rad2deg(0)), markersize=markersize, color=color)
+ handles += ax.plot(0, 0, marker=(2, 0, np.rad2deg(0)), markersize=0.5*markersize, color='w')
+ return handles, ax
+
+ def update_plot(self, x, handles):
+ super(Unicycle, self).update_plot(x, handles)
+ handles[1].set_data([x[0]], [x[1]])
+ handles[1].set_marker((3, 0, np.rad2deg(x[2]-np.pi/2)))
+ handles[1].set_markersize(handles[1].get_markersize())
+ handles[2].set_data([x[0]], [x[1]])
+ handles[2].set_marker((2, 0, np.rad2deg(x[2]-np.pi/2)))
+ handles[2].set_markersize(handles[2].get_markersize())
+
+
+
+### tunnel_mpc_controller.py
+
+class SceneUpdater():
+
+ def __init__(self, params: dict, verbosity=0):
+ self.params = params
+ self.verbosity = verbosity
+ self.reset()
+
+ def reset(self):
+ self.obstacle_clusters : List[ObstacleCluster] = None
+ self.free_star = None
+
+ # workspace_modification.py
+
+ # TODO: Check why computational time varies so much for same static scene
+ @staticmethod
+ def workspace_modification(obstacles, p, pg, rho0, max_compute_time, hull_epsilon, gamma=0.5, make_convex=0, obstacle_clusters_prev=None, free_star_prev=None, verbosity=0):
+
+ # Clearance variable initialization
+ rho = rho0 / gamma # First rho should be rho0
+ t_init = tic()
+
+ while True:
+ if toc(t_init) > max_compute_time:
+ if verbosity > 0:
+ print("[Workspace modification]: Max compute time in rho iteration.")
+ break
+
+ # Reduce rho
+ rho *= gamma
+
+ # Pad obstacles with rho
+ obstacles_rho = [o.dilated_obstacle(padding=rho, id="duplicate") for o in obstacles]
+
+ # TODO: Fix boundaries
+ free_rho = shapely.geometry.box(-20, -20, 20, 20)
+ for o in obstacles_rho:
+ free_rho = free_rho.difference(o.polygon())
+
+ # TODO: Check buffering fix
+ # Find P0
+ Bp = shapely.geometry.Point(p).buffer(0.95 * rho)
+ initial_reference_set = Bp.intersection(free_rho.buffer(-0.1 * rho))
+
+ if not initial_reference_set.is_empty:
+ break
+
+ # Initial and goal reference position selection
+ r0_sh, _ = shapely.ops.nearest_points(initial_reference_set, shapely.geometry.Point(p))
+ r0 = np.array(r0_sh.coords[0])
+ rg_sh, _ = shapely.ops.nearest_points(free_rho, shapely.geometry.Point(pg))
+ rg = np.array(rg_sh.coords[0])
+
+
+ # TODO: Check more thoroughly
+ if free_star_prev is not None:
+ free_star_prev = free_star_prev.buffer(-1e-4)
+ if free_star_prev is not None and free_star_prev.contains(r0_sh) and free_star_prev.contains(rg_sh) and free_rho.contains(free_star_prev):# not any([free_star_prev.covers(o.polygon()) for o in obstacles_rho]):
+ if verbosity > 1:
+ print("[Workspace modification]: Reuse workspace from previous time step.")
+ obstacle_clusters = obstacle_clusters_prev
+ exit_flag = 10
+ else:
+ # Apply cluster and starify
+ obstacle_clusters, obstacle_timing, exit_flag, n_iter = \
+ cluster_and_starify(obstacles_rho, r0, rg, hull_epsilon, max_compute_time=max_compute_time-toc(t_init),
+ previous_clusters=obstacle_clusters_prev,
+ make_convex=make_convex, verbose=verbosity)
+
+ free_star = shapely.geometry.box(-20, -20, 20, 20)
+ for o in obstacle_clusters:
+ free_star = free_star.difference(o.polygon())
+
+ compute_time = toc(t_init)
+ return obstacle_clusters, r0, rg, rho, free_star, compute_time, exit_flag
+
+ def update(self, p: np.ndarray, pg: np.ndarray, obstacles) -> tuple[np.ndarray, np.ndarray, float, list[StarshapedObstacle]]:
+ # Update obstacles
+ if not self.params['use_prev_workspace']:
+ self.free_star = None
+ self.obstacle_clusters, r0, rg, rho, self.free_star, _, _ = SceneUpdater.workspace_modification(
+ obstacles, p, pg, self.params['rho0'], self.params['max_obs_compute_time'],
+ self.params['hull_epsilon'], self.params['gamma'],
+ make_convex=self.params['make_convex'], obstacle_clusters_prev=self.obstacle_clusters,
+ free_star_prev=self.free_star, verbosity=self.verbosity)
+ obstacles_star : List[StarshapedObstacle] = [o.cluster_obstacle for o in self.obstacle_clusters]
+ # Make sure all polygon representations are computed
+ [o._compute_polygon_representation() for o in obstacles_star]
+
+ return r0, rg, rho, obstacles_star
+
+
+class PathGenerator():
+
+ def __init__(self, params: dict, verbosity=0):
+ self.params = params
+ self.verbosity = verbosity
+ self.reset()
+
+ def reset(self):
+ self.target_path = []
+
+ ### soads.py
+
+ # TODO: Check if can make more computationally efficient
+ @staticmethod
+ def soads_f(r, rg, obstacles: List[StarshapedObstacle], adapt_obstacle_velocity=False, unit_magnitude=False, crep=1., reactivity=1., tail_effect=False, convergence_tolerance=1e-4, d=False):
+ goal_vector = rg - r
+ goal_dist = np.linalg.norm(goal_vector)
+ if goal_dist < convergence_tolerance:
+ return 0 * r
+
+ No = len(obstacles)
+ fa = goal_vector / goal_dist # Attractor dynamics
+ if No == 0:
+ return fa
+
+ ef = [-fa[1], fa[0]]
+ Rf = np.vstack((fa, ef)).T
+
+ mu = [obs.reference_direction(r) for obs in obstacles]
+ normal = [obs.normal(r) for obs in obstacles]
+ gamma = [obs.distance_function(r) for obs in obstacles]
+ # Compute weights
+ w = PathGenerator.compute_weights(gamma, weightPow=1)
+
+ # Compute obstacle velocities
+ xd_o = np.zeros((2, No))
+ if adapt_obstacle_velocity:
+ for i, obs in enumerate(obstacles):
+ xd_o[:, i] = obs.vel_intertial_frame(r)
+
+ kappa = 0.
+ f_mag = 0.
+ for i in range(No):
+ # Compute basis matrix
+ E = np.zeros((2, 2))
+ E[:, 0] = mu[i]
+ E[:, 1] = [-normal[i][1], normal[i][0]]
+ # Compute eigenvalues
+ D = np.zeros((2, 2))
+ D[0, 0] = 1 - crep / (gamma[i] ** (1 / reactivity)) if tail_effect or normal[i].dot(fa) < 0. else 1
+ D[1, 1] = 1 + 1 / gamma[i] ** (1 / reactivity)
+ # Compute modulation
+ M = E.dot(D).dot(np.linalg.inv(E))
+ # f_i = M.dot(fa)
+ f_i = M.dot(fa - xd_o[:, i]) + xd_o[:, i]
+ # Compute contribution to velocity magnitude
+ f_i_abs = np.linalg.norm(f_i)
+ f_mag += w[i] * f_i_abs
+ # Compute contribution to velocity direction
+ nu_i = f_i / f_i_abs
+ nu_i_hat = Rf.T.dot(nu_i)
+ kappa_i = np.arccos(np.clip(nu_i_hat[0], -1, 1)) * np.sign(nu_i_hat[1])
+ kappa += w[i] * kappa_i
+ kappa_norm = abs(kappa)
+ f_o = Rf.dot([np.cos(kappa_norm), np.sin(kappa_norm) / kappa_norm * kappa]) if kappa_norm > 0. else fa
+
+ if unit_magnitude:
+ f_mag = 1.
+ return f_mag * f_o
+
+ @staticmethod
+ def compute_weights(
+ distMeas,
+ N=0,
+ distMeas_lowerLimit=1,
+ weightType="inverseGamma",
+ weightPow=2,
+ ):
+ """Compute weights based on a distance measure (with no upper limit)"""
+ distMeas = np.array(distMeas)
+ n_points = distMeas.shape[0]
+
+ critical_points = distMeas <= distMeas_lowerLimit
+
+ if np.sum(critical_points): # at least one
+ if np.sum(critical_points) == 1:
+ w = critical_points * 1.0
+ return w
+ else:
+ # TODO: continuous weighting function
+ # warnings.warn("Implement continuity of weighting function.")
+ w = critical_points * 1.0 / np.sum(critical_points)
+ return w
+
+ distMeas = distMeas - distMeas_lowerLimit
+ w = (1 / distMeas) ** weightPow
+ if np.sum(w) == 0:
+ return w
+ w = w / np.sum(w) # Normalization
+ return w
+
+ ### path_generator.py
+
+ @staticmethod
+ def pol2pos(path_pol, s):
+ n_pol = len(path_pol) // 2 - 1
+ return [sum([path_pol[j * (n_pol + 1) + i] * s ** (n_pol - i) for i in range(n_pol + 1)]) for j in range(2)]
+
+ @staticmethod
+ def path_generator(r0, rg, obstacles, dp_max, N, dt, max_compute_time, n_pol, ds_decay_rate=0.5,
+ ds_increase_rate=2., max_nr_steps=1000, convergence_tolerance=1e-5, P_prev=None, s_prev=None,
+ reactivity=1., crep=1., tail_effect=False, reference_step_size=0.5, verbosity=0):
+ """
+ r0 : np.ndarray initial reference position
+ rg : np.ndarray goal reference position
+ obstacles : list[StarshapedObstacle] obstacles in the scene
+ dp_max : float maximum position increment (i.e. vmax * dt)
+ N : int ???
+ dt : float time step
+ max_compute_time : float timeout for computation
+ n_pol : int degree of the polynomial that fits the reference ???
+ ds_decay_rate : float ???
+ ds_increase_rate : float ???
+ max_nr_steps : int computation steps threshold
+ convergence_tolerance : float ???
+ P_prev : np.ndarray previous path ???
+ s_prev : np.ndarray previous path time ???
+ reactivity : float ???
+ crep : float ???
+ tail_effect : bool ???
+ reference_step_size : float ???
+ verbosity : int you guessed it...
+ """
+
+ t0 = tic()
+
+ # Initialize
+ ds = 1
+ s = np.zeros(max_nr_steps)
+ r = np.zeros((max_nr_steps, r0.size))
+ if P_prev is not None:
+ i = P_prev.shape[0]
+ r[:i, :] = P_prev
+ s[:i] = s_prev
+ else:
+ i = 1
+ r[0, :] = r0
+
+ while True:
+ dist_to_goal = np.linalg.norm(r[i - 1, :] - rg)
+ # Check exit conditions
+ if dist_to_goal < convergence_tolerance:
+ if verbosity > 1:
+ print(f"[Path Generator]: Path converged. {int(100 * (s[i - 1] / N))}% of path completed.")
+ break
+ if s[i - 1] >= N:
+ if verbosity > 1:
+ print(f"[Path Generator]: Completed path length. {int(100 * (s[i - 1] / N))}% of path completed.")
+ break
+ if toc(t0) > max_compute_time:
+ if verbosity > 1:
+ print(f"[Path Generator]: Max compute time in path integrator. {int(100 * (s[i - 1] / N))}% of path completed.")
+ break
+ if i >= max_nr_steps:
+ if verbosity > 1:
+ print(f"[Path Generator]: Max steps taken in path integrator. {int(100 * (s[i - 1] / N))}% of path completed.")
+ break
+
+ # Movement using SOADS dynamics
+ dr = min(dp_max, dist_to_goal) * PathGenerator.soads_f(r[i - 1, :], rg, obstacles, adapt_obstacle_velocity=False,
+ unit_magnitude=True, crep=crep,
+ reactivity=reactivity, tail_effect=tail_effect,
+ convergence_tolerance=convergence_tolerance)
+
+ r[i, :] = r[i - 1, :] + dr * ds
+
+ ri_in_obstacle = False
+ while any([o.interior_point(r[i, :]) for o in obstacles]):
+ if verbosity > 1:
+ print("[Path Generator]: Path inside obstacle. Reducing integration step from {:5f} to {:5f}.".format(ds, ds*ds_decay_rate))
+ ds *= ds_decay_rate
+ r[i, :] = r[i - 1, :] + dr * ds
+ # Additional compute time check
+ if toc(t0) > max_compute_time:
+ ri_in_obstacle = True
+ break
+ if ri_in_obstacle:
+ continue
+
+ # Update travelled distance
+ s[i] = s[i - 1] + ds
+ # Try to increase step rate again
+ ds = min(ds_increase_rate * ds, 1)
+ # Increase iteration counter
+ i += 1
+
+ r = r[:i, :]
+ s = s[:i]
+
+ # Evenly spaced path
+ s_vec = np.arange(0, s[-1] + reference_step_size, reference_step_size)
+ xs, ys = np.interp(s_vec, s, r[:, 0]), np.interp(s_vec, s, r[:, 1])
+ # Append not finished path with fixed final position
+ s_vec = np.append(s_vec, np.arange(s[-1] + reference_step_size, N + reference_step_size, reference_step_size))
+ xs = np.append(xs, xs[-1] * np.ones(len(s_vec)-len(xs)))
+ ys = np.append(ys, ys[-1] * np.ones(len(s_vec)-len(ys)))
+
+ reference_path = [el for p in zip(xs, ys) for el in p] # [x0 y0 x1 y1 ...]
+
+ # TODO: Fix when close to goal
+ # TODO: Adjust for short arc length, skip higher order terms..
+ path_pol = np.polyfit(s_vec, reference_path[::2], n_pol).tolist() + \
+ np.polyfit(s_vec, reference_path[1::2], n_pol).tolist() # [px0 px1 ... pxn py0 py1 ... pyn]
+ # Force init position to be correct
+ path_pol[n_pol] = reference_path[0]
+ path_pol[-1] = reference_path[1]
+
+ # Compute polyfit approximation error
+ epsilon = [np.linalg.norm(np.array(reference_path[2 * i:2 * (i + 1)]) - np.array(PathGenerator.pol2pos(path_pol, s_vec[i]))) for i in range(N + 1)]
+
+ compute_time = toc(t0)
+
+ """
+ path_pol : np.ndarray the polynomial approximation of `reference_path`
+ epsilon : [float] approximation error between the polynomial fit and the actual path
+ reference_path : list the actual path (used for P_prev later on) in [x1, y1, x2, y2, ...] format
+ compute_time : float overall timing of this function
+ """
+ return path_pol, epsilon, reference_path, compute_time
+
+ def prepare_prev(self, p: np.ndarray, rho: float, obstacles_star: List[StarshapedObstacle]):
+ P_prev = np.array([self.target_path[::2], self.target_path[1::2]]).T
+ # Shift path to start at point closest to robot position
+ P_prev = P_prev[np.argmin(np.linalg.norm(p - P_prev, axis=1)):, :]
+ # P_prev[0, :] = self.r0
+ if np.linalg.norm(p - P_prev[0, :]) > rho:
+ if self.verbosity > 0:
+ print("[Path Generator]: No reuse of previous path. Path not within distance rho from robot.")
+ P_prev = None
+ else:
+ for r in P_prev:
+ if any([o.interior_point(r) for o in obstacles_star]):
+ if self.verbosity > 0:
+ print("[Path Generator]: No reuse of previous path. Path not collision-free.")
+ P_prev = None
+
+ if P_prev is not None:
+ # Cut off stand still padding in previous path
+ P_prev_stepsize = np.linalg.norm(np.diff(P_prev, axis=0), axis=1)
+ s_prev = np.hstack((0, np.cumsum(P_prev_stepsize) / self.params['dp_max']))
+ P_prev_mask = [True] + (P_prev_stepsize > 1e-8).tolist()
+ P_prev = P_prev[P_prev_mask, :]
+ s_prev = s_prev[P_prev_mask]
+ else:
+ s_prev = None
+
+ return P_prev, s_prev
+
+ def update(self, p: np.ndarray, r0: np.ndarray, rg: np.ndarray, rho: float, obstacles_star: List[StarshapedObstacle]) -> tuple[List[float], float]:
+ # Buffer previous target path
+ if self.params['buffer'] and self.target_path:
+ P_prev, s_prev = self.prepare_prev(p, rho, obstacles_star)
+ else:
+ P_prev, s_prev = None, None
+ # Generate the new path
+ path_pol, epsilon, self.target_path, _ = PathGenerator.path_generator(
+ r0, rg, obstacles_star, self.params['dp_max'], self.params['N'],
+ self.params['dt'], self.params['max_compute_time'], self.params['n_pol'],
+ ds_decay_rate=0.5, ds_increase_rate=2., max_nr_steps=1000, P_prev=P_prev, s_prev=s_prev,
+ reactivity=self.params['reactivity'], crep=self.params['crep'],
+ convergence_tolerance=self.params['convergence_tolerance'], verbosity=self.verbosity)
+ return path_pol, epsilon
+
+
+class MpcController():
+
+ def __init__(self, params: dict, robot, verbosity=0):
+ self.params = params
+ self.robot = robot
+ # self.mpc_solver = TunnelMpc(params, robot)
+ self.verbosity = verbosity
+ self.reset()
+
+ def reset(self):
+ # self.mpc_solver.reset()
+ self.u_prev = [0] * self.robot.nu
+
+ def check_convergence(self, p: np.ndarray, pg: np.ndarray):
+ return np.linalg.norm(p - pg) < self.params['convergence_margin']
+
+ def e_max(self, rho, epsilon):
+ if rho > 0:
+ e_max = rho - max(epsilon)
+ else:
+ e_max = 1.e6
+ return e_max
+
+ def ref(self, path_pol, s):
+ n_pol = self.params['n_pol']
+ return [np.polyval(path_pol[j*(n_pol+1):(j+1)*(n_pol+1)], s) for j in range(self.params['np'])]
+
+ def compute_u(self, x, p, path_pol, rg, rho, epsilon):
+ # Compute MPC solution
+
+ # e_max = self.e_max(rho, epsilon) # parameter for tracking error constraint
+ # solution = self.mpc_solver.run(x.tolist(), self.u_prev, path_pol, self.params, e_max, rg.tolist(), self.verbosity) # call to Rust solver
+ # # Extract first control signal and store it for later
+ # self.u_prev = solution['u'][:self.robot.nu]
+
+ p = np.array(p)
+ e_max = self.e_max(rho, epsilon)
+ s_kappa = 0.9 * e_max / self.robot.vmax
+ p_ref = self.ref(path_pol, s_kappa)
+
+ err = p_ref - p
+ dir = np.array([np.cos(x[2]), np.sin(x[2])])
+ vel = 0.65 * self.robot.vmax * max(0.1, (dir @ (err / np.linalg.norm(err))))
+ wel = 0.85 * ((np.arctan2(err[1], err[0]) - x[2] + np.pi) % (2*np.pi) - np.pi)
+
+ alpha = 1
+ self.u_prev = [alpha*vel, alpha*wel]
+
+ return np.array(self.u_prev)
+
+
+if __name__ == "__main__":
+
+ from starworlds.obstacles import StarshapedPolygon
+
+ # environment
+ obstacles = [
+ StarshapedPolygon([[2, 2], [8, 2], [8, 3], [2, 3]]),
+ StarshapedPolygon([[2, 3], [3, 3], [3, 4.25], [2, 4.25]]),
+ StarshapedPolygon([[2, 5], [8, 5], [8, 6], [2, 6]]),
+ StarshapedPolygon([[2, 8], [8, 8], [8, 9], [2, 9]]),
+ ]
+ pg = np.array([0.5, 5.5]) # goal position
+ p0 = np.array([9, 4]) # initial position
+ theta0 = np.arctan2(pg[1]-p0[1], pg[0]-p0[0]) # initial heading (simply start heading towards goal)
+
+ # robot
+ robot_type = "Unicycle"
+ with open(r'robot_params.yaml') as f:
+ params = yaml.safe_load(f)
+ robot_params = params[robot_type]
+ robot = Unicycle(width=robot_params['width'],
+ vel_min=[robot_params['lin_vel_min'], -robot_params['ang_vel_max']],
+ vel_max=[robot_params['lin_vel_max'], robot_params['ang_vel_max']],
+ name=robot_type)
+ x0 = np.append(p0, [theta0]) # initial robot state
+
+ # MPC
+ with open(r'./tunnel_mpc_params.yaml') as f:
+ params = yaml.safe_load(f)
+ mpc_params = params["tunnel_mpc"]
+ mpc_params['dp_max'] = robot.vmax * mpc_params['dt']
+
+ # components
+ verbosity = 1
+ scene_updater = SceneUpdater(mpc_params, verbosity)
+ path_gen = PathGenerator(mpc_params, verbosity)
+ controller = MpcController(mpc_params, robot, verbosity)
+
+
+ # plotting
+ fig = plt.figure()
+ handle_goal = plt.plot(*pg, c="g")[0]
+ handle_init = plt.plot(*x0[:2], c="b")[0]
+ handle_curr = plt.plot(*x0[:2], c="r", marker=(3, 0, np.rad2deg(x0[2]-np.pi/2)), markersize=10)[0]
+ handle_curr_dir = plt.plot(0, 0, marker=(2, 0, np.rad2deg(0)), markersize=5, color='w')[0]
+ handle_path = plt.plot([], [], c="k")[0]
+ coll = plt_col.PolyCollection([
+ np.array([[2, 2], [8, 2], [8, 3], [2, 3]]),
+ np.array([[2, 3], [3, 3], [3, 4.25], [2, 4.25]]),
+ np.array([[2, 5], [8, 5], [8, 6], [2, 6]]),
+ np.array([[2, 8], [8, 8], [8, 9], [2, 9]])
+ ])
+ plt.gca().add_collection(coll)
+ handle_title = plt.text(5, 9.5, "", bbox={'facecolor':'w', 'alpha':0.5, 'pad':5}, ha="center")
+ plt.gca().set_aspect("equal")
+ plt.xlim([0, 10])
+ plt.ylim([0, 10])
+ plt.draw()
+
+
+ # run the controller
+ T_max = 30
+ dt = controller.params['dt']
+ t = 0.
+ x = x0
+ u_prev = np.zeros(robot.nu)
+ convergence_threshold = 0.05
+ converged = False
+ try:
+ while t < T_max:
+ p = robot.h(x)
+
+ if np.linalg.norm(p - pg) < convergence_threshold:
+ break
+
+ # Udpate the scene
+ r0, rg, rho, obstacles_star = scene_updater.update(p, pg, obstacles)
+
+ # Check for convergence
+ if controller.check_convergence(p, pg):
+ u = np.zeros(robot.nu)
+ else:
+ # Update target path
+ path_pol, epsilon = path_gen.update(p, r0, rg, rho, obstacles_star)
+ # Calculate next control input
+ u = controller.compute_u(x, p, path_pol, rg, rho, epsilon)
+
+ # update robot position
+ x, _ = robot.move(x, u, dt)
+ u_prev = u
+ t += dt
+
+ # plot
+ handle_curr.set_data([x[0]], [x[1]])
+ handle_curr.set_marker((3, 0, np.rad2deg(x[2]-np.pi/2)))
+ handle_curr_dir.set_data([x[0]], [x[1]])
+ handle_curr_dir.set_marker((2, 0, np.rad2deg(x[2]-np.pi/2)))
+ handle_path.set_data([path_gen.target_path[::2], path_gen.target_path[1::2]])
+ handle_title.set_text(f"{t:5.3f}")
+ fig.canvas.draw()
+ plt.pause(0.005)
+
+ except Exception as ex:
+ raise ex
+ pass
+
+ plt.show()
+
diff --git a/python/smc/path_generation/maps/premade_maps.py b/python/smc/path_generation/maps/premade_maps.py
new file mode 100644
index 0000000..7037857
--- /dev/null
+++ b/python/smc/path_generation/maps/premade_maps.py
@@ -0,0 +1,21 @@
+from smc.path_generation.starworlds.obstacles import StarshapedPolygon
+
+
+def createSampleStaticMap():
+ """
+ createMap
+ ---------
+ return obstacles that define the 2D map
+ """
+ # [lower_left, lower_right, top_right, top_left]
+ map_as_list = [
+ [[2, 2], [8, 2], [8, 3], [2, 3]],
+ [[2, 3], [3, 3], [3, 4.25], [2, 4.25]],
+ [[2, 5], [8, 5], [8, 6], [2, 6]],
+ [[2, 8], [8, 8], [8, 9], [2, 9]],
+ ]
+
+ obstacles = []
+ for map_element in map_as_list:
+ obstacles.append(StarshapedPolygon(map_element))
+ return obstacles, map_as_list
diff --git a/python/smc/path_generation/path_generator.py b/python/smc/path_generation/path_generator.py
index 3f6bea6..04cbaf0 100644
--- a/python/smc/path_generation/path_generator.py
+++ b/python/smc/path_generation/path_generator.py
@@ -1,201 +1,10 @@
-from typing import List
-from abc import ABC, abstractmethod
+from smc.path_generation.starworlds.obstacles import StarshapedObstacle
+from smc.path_generation.starworlds.utils.misc import tic, toc
import numpy as np
-from starworlds.obstacles import StarshapedObstacle
-from starworlds.starshaped_hull import cluster_and_starify, ObstacleCluster
-from starworlds.utils.misc import tic, toc
-import shapely
-import yaml
-
-import matplotlib.pyplot as plt
-import matplotlib.collections as plt_col
-
-
-
-### mobile_robot.py
-
-class MobileRobot(ABC):
-
- def __init__(self, nu, nx, width, name, u_min=None, u_max=None, x_min=None, x_max=None):
- """
- nx : int number of state variables
- nu : int number of input variables
- baseline : float distance between the wheels
- name : str the name of the robot
- u_min : np.ndarray minimum input value
- u_max : np.ndarray maximum input value
- x_min : np.ndarray minimum state value
- x_max : np.ndarray maximum state value
- """
- self.nx = nx
- self.nu = nu
- self.width = width
- self.name = name
- def valid_u_bound(bound): return bound is not None and len(bound) == self.nu
- def valid_q_bound(bound): return bound is not None and len(bound) == self.nx
- self.u_min = u_min if valid_u_bound(u_min) else [-np.inf] * self.nu
- self.u_max = u_max if valid_u_bound(u_max) else [np.inf] * self.nu
- self.x_min = x_min if valid_q_bound(x_min) else [-np.inf] * self.nx
- self.x_max = x_max if valid_q_bound(x_max) else [np.inf] * self.nx
-
- @abstractmethod
- def f(self, x, u):
- """ Forward dynamics ? """
- pass
-
- @abstractmethod
- def h(self, q):
- """ Forward kinematics """
- pass
-
- @abstractmethod
- def vel_min(self):
- pass
-
- @abstractmethod
- def vel_max(self):
- pass
-
- def move(self, x, u, dt):
- u_sat = np.clip(u, self.u_min, self.u_max)
- x_next = x + np.array(self.f(x, u_sat)) * dt
- x_next = np.clip(x_next, self.x_min, self.x_max)
- return x_next, u_sat
-
-
-class Unicycle(MobileRobot):
-
- def __init__(self, width, vel_min=None, vel_max=None, name='robot'):
- self.vmax = vel_max[0]
- super().__init__(nu=2, nx=3, width=width, name=name, u_min=vel_min, u_max=vel_max)
-
- def f(self, x, u):
- return [u[0] * np.cos(x[2]), # vx
- u[0] * np.sin(x[2]), # vy
- u[1]] # wz
-
- def h(self, x):
- return x[:2] # x, y
-
- def vel_min(self):
- return self.u_min
-
- def vel_max(self):
- return self.u_max
-
- def init_plot(self, ax=None, color='b', alpha=0.7, markersize=10):
- handles, ax = super(Unicycle, self).init_plot(ax=ax, color=color, alpha=alpha)
- handles += ax.plot(0, 0, marker=(3, 0, np.rad2deg(0)), markersize=markersize, color=color)
- handles += ax.plot(0, 0, marker=(2, 0, np.rad2deg(0)), markersize=0.5*markersize, color='w')
- return handles, ax
-
- def update_plot(self, x, handles):
- super(Unicycle, self).update_plot(x, handles)
- handles[1].set_data([x[0]], [x[1]])
- handles[1].set_marker((3, 0, np.rad2deg(x[2]-np.pi/2)))
- handles[1].set_markersize(handles[1].get_markersize())
- handles[2].set_data([x[0]], [x[1]])
- handles[2].set_marker((2, 0, np.rad2deg(x[2]-np.pi/2)))
- handles[2].set_markersize(handles[2].get_markersize())
-
-
-
-### tunnel_mpc_controller.py
-
-class SceneUpdater():
-
- def __init__(self, params: dict, verbosity=0):
- self.params = params
- self.verbosity = verbosity
- self.reset()
-
- def reset(self):
- self.obstacle_clusters : List[ObstacleCluster] = None
- self.free_star = None
-
- # workspace_modification.py
-
- # TODO: Check why computational time varies so much for same static scene
- @staticmethod
- def workspace_modification(obstacles, p, pg, rho0, max_compute_time, hull_epsilon, gamma=0.5, make_convex=0, obstacle_clusters_prev=None, free_star_prev=None, verbosity=0):
-
- # Clearance variable initialization
- rho = rho0 / gamma # First rho should be rho0
- t_init = tic()
-
- while True:
- if toc(t_init) > max_compute_time:
- if verbosity > 0:
- print("[Workspace modification]: Max compute time in rho iteration.")
- break
-
- # Reduce rho
- rho *= gamma
-
- # Pad obstacles with rho
- obstacles_rho = [o.dilated_obstacle(padding=rho, id="duplicate") for o in obstacles]
-
- # TODO: Fix boundaries
- free_rho = shapely.geometry.box(-20, -20, 20, 20)
- for o in obstacles_rho:
- free_rho = free_rho.difference(o.polygon())
-
- # TODO: Check buffering fix
- # Find P0
- Bp = shapely.geometry.Point(p).buffer(0.95 * rho)
- initial_reference_set = Bp.intersection(free_rho.buffer(-0.1 * rho))
-
- if not initial_reference_set.is_empty:
- break
+from typing import List
- # Initial and goal reference position selection
- r0_sh, _ = shapely.ops.nearest_points(initial_reference_set, shapely.geometry.Point(p))
- r0 = np.array(r0_sh.coords[0])
- rg_sh, _ = shapely.ops.nearest_points(free_rho, shapely.geometry.Point(pg))
- rg = np.array(rg_sh.coords[0])
-
-
- # TODO: Check more thoroughly
- if free_star_prev is not None:
- free_star_prev = free_star_prev.buffer(-1e-4)
- if free_star_prev is not None and free_star_prev.contains(r0_sh) and free_star_prev.contains(rg_sh) and free_rho.contains(free_star_prev):# not any([free_star_prev.covers(o.polygon()) for o in obstacles_rho]):
- if verbosity > 1:
- print("[Workspace modification]: Reuse workspace from previous time step.")
- obstacle_clusters = obstacle_clusters_prev
- exit_flag = 10
- else:
- # Apply cluster and starify
- obstacle_clusters, obstacle_timing, exit_flag, n_iter = \
- cluster_and_starify(obstacles_rho, r0, rg, hull_epsilon, max_compute_time=max_compute_time-toc(t_init),
- previous_clusters=obstacle_clusters_prev,
- make_convex=make_convex, verbose=verbosity)
-
- free_star = shapely.geometry.box(-20, -20, 20, 20)
- for o in obstacle_clusters:
- free_star = free_star.difference(o.polygon())
-
- compute_time = toc(t_init)
- return obstacle_clusters, r0, rg, rho, free_star, compute_time, exit_flag
-
- def update(self, p: np.ndarray, pg: np.ndarray, obstacles) -> tuple[np.ndarray, np.ndarray, float, list[StarshapedObstacle]]:
- # Update obstacles
- if not self.params['use_prev_workspace']:
- self.free_star = None
- self.obstacle_clusters, r0, rg, rho, self.free_star, _, _ = SceneUpdater.workspace_modification(
- obstacles, p, pg, self.params['rho0'], self.params['max_obs_compute_time'],
- self.params['hull_epsilon'], self.params['gamma'],
- make_convex=self.params['make_convex'], obstacle_clusters_prev=self.obstacle_clusters,
- free_star_prev=self.free_star, verbosity=self.verbosity)
- obstacles_star : List[StarshapedObstacle] = [o.cluster_obstacle for o in self.obstacle_clusters]
- # Make sure all polygon representations are computed
- [o._compute_polygon_representation() for o in obstacles_star]
-
- return r0, rg, rho, obstacles_star
-
-
-class PathGenerator():
-
+class PathGenerator:
def __init__(self, params: dict, verbosity=0):
self.params = params
self.verbosity = verbosity
@@ -203,12 +12,23 @@ class PathGenerator():
def reset(self):
self.target_path = []
-
+
### soads.py
# TODO: Check if can make more computationally efficient
@staticmethod
- def soads_f(r, rg, obstacles: List[StarshapedObstacle], adapt_obstacle_velocity=False, unit_magnitude=False, crep=1., reactivity=1., tail_effect=False, convergence_tolerance=1e-4, d=False):
+ def soads_f(
+ r,
+ rg,
+ obstacles: List[StarshapedObstacle],
+ adapt_obstacle_velocity=False,
+ unit_magnitude=False,
+ crep=1.0,
+ reactivity=1.0,
+ tail_effect=False,
+ convergence_tolerance=1e-4,
+ d=False,
+ ):
goal_vector = rg - r
goal_dist = np.linalg.norm(goal_vector)
if goal_dist < convergence_tolerance:
@@ -234,8 +54,8 @@ class PathGenerator():
for i, obs in enumerate(obstacles):
xd_o[:, i] = obs.vel_intertial_frame(r)
- kappa = 0.
- f_mag = 0.
+ kappa = 0.0
+ f_mag = 0.0
for i in range(No):
# Compute basis matrix
E = np.zeros((2, 2))
@@ -243,7 +63,11 @@ class PathGenerator():
E[:, 1] = [-normal[i][1], normal[i][0]]
# Compute eigenvalues
D = np.zeros((2, 2))
- D[0, 0] = 1 - crep / (gamma[i] ** (1 / reactivity)) if tail_effect or normal[i].dot(fa) < 0. else 1
+ D[0, 0] = (
+ 1 - crep / (gamma[i] ** (1 / reactivity))
+ if tail_effect or normal[i].dot(fa) < 0.0
+ else 1
+ )
D[1, 1] = 1 + 1 / gamma[i] ** (1 / reactivity)
# Compute modulation
M = E.dot(D).dot(np.linalg.inv(E))
@@ -258,10 +82,14 @@ class PathGenerator():
kappa_i = np.arccos(np.clip(nu_i_hat[0], -1, 1)) * np.sign(nu_i_hat[1])
kappa += w[i] * kappa_i
kappa_norm = abs(kappa)
- f_o = Rf.dot([np.cos(kappa_norm), np.sin(kappa_norm) / kappa_norm * kappa]) if kappa_norm > 0. else fa
+ f_o = (
+ Rf.dot([np.cos(kappa_norm), np.sin(kappa_norm) / kappa_norm * kappa])
+ if kappa_norm > 0.0
+ else fa
+ )
if unit_magnitude:
- f_mag = 1.
+ f_mag = 1.0
return f_mag * f_o
@staticmethod
@@ -300,12 +128,38 @@ class PathGenerator():
@staticmethod
def pol2pos(path_pol, s):
n_pol = len(path_pol) // 2 - 1
- return [sum([path_pol[j * (n_pol + 1) + i] * s ** (n_pol - i) for i in range(n_pol + 1)]) for j in range(2)]
+ return [
+ sum(
+ [
+ path_pol[j * (n_pol + 1) + i] * s ** (n_pol - i)
+ for i in range(n_pol + 1)
+ ]
+ )
+ for j in range(2)
+ ]
@staticmethod
- def path_generator(r0, rg, obstacles, dp_max, N, dt, max_compute_time, n_pol, ds_decay_rate=0.5,
- ds_increase_rate=2., max_nr_steps=1000, convergence_tolerance=1e-5, P_prev=None, s_prev=None,
- reactivity=1., crep=1., tail_effect=False, reference_step_size=0.5, verbosity=0):
+ def path_generator(
+ r0,
+ rg,
+ obstacles,
+ dp_max,
+ N,
+ dt,
+ max_compute_time,
+ n_pol,
+ ds_decay_rate=0.5,
+ ds_increase_rate=2.0,
+ max_nr_steps=1000,
+ convergence_tolerance=1e-5,
+ P_prev=None,
+ s_prev=None,
+ reactivity=1.0,
+ crep=1.0,
+ tail_effect=False,
+ reference_step_size=0.5,
+ verbosity=0,
+ ):
"""
r0 : np.ndarray initial reference position
rg : np.ndarray goal reference position
@@ -327,7 +181,7 @@ class PathGenerator():
reference_step_size : float ???
verbosity : int you guessed it...
"""
-
+
t0 = tic()
# Initialize
@@ -347,33 +201,52 @@ class PathGenerator():
# Check exit conditions
if dist_to_goal < convergence_tolerance:
if verbosity > 1:
- print(f"[Path Generator]: Path converged. {int(100 * (s[i - 1] / N))}% of path completed.")
+ print(
+ f"[Path Generator]: Path converged. {int(100 * (s[i - 1] / N))}% of path completed."
+ )
break
if s[i - 1] >= N:
if verbosity > 1:
- print(f"[Path Generator]: Completed path length. {int(100 * (s[i - 1] / N))}% of path completed.")
+ print(
+ f"[Path Generator]: Completed path length. {int(100 * (s[i - 1] / N))}% of path completed."
+ )
break
if toc(t0) > max_compute_time:
if verbosity > 1:
- print(f"[Path Generator]: Max compute time in path integrator. {int(100 * (s[i - 1] / N))}% of path completed.")
+ print(
+ f"[Path Generator]: Max compute time in path integrator. {int(100 * (s[i - 1] / N))}% of path completed."
+ )
break
if i >= max_nr_steps:
if verbosity > 1:
- print(f"[Path Generator]: Max steps taken in path integrator. {int(100 * (s[i - 1] / N))}% of path completed.")
+ print(
+ f"[Path Generator]: Max steps taken in path integrator. {int(100 * (s[i - 1] / N))}% of path completed."
+ )
break
# Movement using SOADS dynamics
- dr = min(dp_max, dist_to_goal) * PathGenerator.soads_f(r[i - 1, :], rg, obstacles, adapt_obstacle_velocity=False,
- unit_magnitude=True, crep=crep,
- reactivity=reactivity, tail_effect=tail_effect,
- convergence_tolerance=convergence_tolerance)
+ dr = min(dp_max, dist_to_goal) * PathGenerator.soads_f(
+ r[i - 1, :],
+ rg,
+ obstacles,
+ adapt_obstacle_velocity=False,
+ unit_magnitude=True,
+ crep=crep,
+ reactivity=reactivity,
+ tail_effect=tail_effect,
+ convergence_tolerance=convergence_tolerance,
+ )
r[i, :] = r[i - 1, :] + dr * ds
ri_in_obstacle = False
while any([o.interior_point(r[i, :]) for o in obstacles]):
if verbosity > 1:
- print("[Path Generator]: Path inside obstacle. Reducing integration step from {:5f} to {:5f}.".format(ds, ds*ds_decay_rate))
+ print(
+ "[Path Generator]: Path inside obstacle. Reducing integration step from {:5f} to {:5f}.".format(
+ ds, ds * ds_decay_rate
+ )
+ )
ds *= ds_decay_rate
r[i, :] = r[i - 1, :] + dr * ds
# Additional compute time check
@@ -397,25 +270,40 @@ class PathGenerator():
s_vec = np.arange(0, s[-1] + reference_step_size, reference_step_size)
xs, ys = np.interp(s_vec, s, r[:, 0]), np.interp(s_vec, s, r[:, 1])
# Append not finished path with fixed final position
- s_vec = np.append(s_vec, np.arange(s[-1] + reference_step_size, N + reference_step_size, reference_step_size))
- xs = np.append(xs, xs[-1] * np.ones(len(s_vec)-len(xs)))
- ys = np.append(ys, ys[-1] * np.ones(len(s_vec)-len(ys)))
+ s_vec = np.append(
+ s_vec,
+ np.arange(
+ s[-1] + reference_step_size,
+ N + reference_step_size,
+ reference_step_size,
+ ),
+ )
+ xs = np.append(xs, xs[-1] * np.ones(len(s_vec) - len(xs)))
+ ys = np.append(ys, ys[-1] * np.ones(len(s_vec) - len(ys)))
reference_path = [el for p in zip(xs, ys) for el in p] # [x0 y0 x1 y1 ...]
# TODO: Fix when close to goal
# TODO: Adjust for short arc length, skip higher order terms..
- path_pol = np.polyfit(s_vec, reference_path[::2], n_pol).tolist() + \
- np.polyfit(s_vec, reference_path[1::2], n_pol).tolist() # [px0 px1 ... pxn py0 py1 ... pyn]
+ path_pol = (
+ np.polyfit(s_vec, reference_path[::2], n_pol).tolist()
+ + np.polyfit(s_vec, reference_path[1::2], n_pol).tolist()
+ ) # [px0 px1 ... pxn py0 py1 ... pyn]
# Force init position to be correct
path_pol[n_pol] = reference_path[0]
path_pol[-1] = reference_path[1]
# Compute polyfit approximation error
- epsilon = [np.linalg.norm(np.array(reference_path[2 * i:2 * (i + 1)]) - np.array(PathGenerator.pol2pos(path_pol, s_vec[i]))) for i in range(N + 1)]
+ epsilon = [
+ np.linalg.norm(
+ np.array(reference_path[2 * i : 2 * (i + 1)])
+ - np.array(PathGenerator.pol2pos(path_pol, s_vec[i]))
+ )
+ for i in range(N + 1)
+ ]
compute_time = toc(t0)
-
+
"""
path_pol : np.ndarray the polynomial approximation of `reference_path`
epsilon : [float] approximation error between the polynomial fit and the actual path
@@ -423,207 +311,72 @@ class PathGenerator():
compute_time : float overall timing of this function
"""
return path_pol, epsilon, reference_path, compute_time
-
- def prepare_prev(self, p: np.ndarray, rho: float, obstacles_star: List[StarshapedObstacle]):
+
+ def prepare_prev(
+ self, p: np.ndarray, rho: float, obstacles_star: List[StarshapedObstacle]
+ ):
P_prev = np.array([self.target_path[::2], self.target_path[1::2]]).T
# Shift path to start at point closest to robot position
- P_prev = P_prev[np.argmin(np.linalg.norm(p - P_prev, axis=1)):, :]
+ P_prev = P_prev[np.argmin(np.linalg.norm(p - P_prev, axis=1)) :, :]
# P_prev[0, :] = self.r0
if np.linalg.norm(p - P_prev[0, :]) > rho:
if self.verbosity > 0:
- print("[Path Generator]: No reuse of previous path. Path not within distance rho from robot.")
+ print(
+ "[Path Generator]: No reuse of previous path. Path not within distance rho from robot."
+ )
P_prev = None
else:
for r in P_prev:
if any([o.interior_point(r) for o in obstacles_star]):
if self.verbosity > 0:
- print("[Path Generator]: No reuse of previous path. Path not collision-free.")
+ print(
+ "[Path Generator]: No reuse of previous path. Path not collision-free."
+ )
P_prev = None
if P_prev is not None:
# Cut off stand still padding in previous path
P_prev_stepsize = np.linalg.norm(np.diff(P_prev, axis=0), axis=1)
- s_prev = np.hstack((0, np.cumsum(P_prev_stepsize) / self.params['dp_max']))
+ s_prev = np.hstack((0, np.cumsum(P_prev_stepsize) / self.params["dp_max"]))
P_prev_mask = [True] + (P_prev_stepsize > 1e-8).tolist()
P_prev = P_prev[P_prev_mask, :]
s_prev = s_prev[P_prev_mask]
else:
s_prev = None
-
+
return P_prev, s_prev
-
- def update(self, p: np.ndarray, r0: np.ndarray, rg: np.ndarray, rho: float, obstacles_star: List[StarshapedObstacle]) -> tuple[List[float], float]:
+
+ def update(
+ self,
+ p: np.ndarray,
+ r0: np.ndarray,
+ rg: np.ndarray,
+ rho: float,
+ obstacles_star: List[StarshapedObstacle],
+ ) -> tuple[List[float], float]:
# Buffer previous target path
- if self.params['buffer'] and self.target_path:
+ if self.params["buffer"] and self.target_path:
P_prev, s_prev = self.prepare_prev(p, rho, obstacles_star)
else:
P_prev, s_prev = None, None
# Generate the new path
path_pol, epsilon, self.target_path, _ = PathGenerator.path_generator(
- r0, rg, obstacles_star, self.params['dp_max'], self.params['N'],
- self.params['dt'], self.params['max_compute_time'], self.params['n_pol'],
- ds_decay_rate=0.5, ds_increase_rate=2., max_nr_steps=1000, P_prev=P_prev, s_prev=s_prev,
- reactivity=self.params['reactivity'], crep=self.params['crep'],
- convergence_tolerance=self.params['convergence_tolerance'], verbosity=self.verbosity)
+ r0,
+ rg,
+ obstacles_star,
+ self.params["dp_max"],
+ self.params["N"],
+ self.params["dt"],
+ self.params["max_compute_time"],
+ self.params["n_pol"],
+ ds_decay_rate=0.5,
+ ds_increase_rate=2.0,
+ max_nr_steps=1000,
+ P_prev=P_prev,
+ s_prev=s_prev,
+ reactivity=self.params["reactivity"],
+ crep=self.params["crep"],
+ convergence_tolerance=self.params["convergence_tolerance"],
+ verbosity=self.verbosity,
+ )
return path_pol, epsilon
-
-
-class MpcController():
-
- def __init__(self, params: dict, robot, verbosity=0):
- self.params = params
- self.robot = robot
- # self.mpc_solver = TunnelMpc(params, robot)
- self.verbosity = verbosity
- self.reset()
-
- def reset(self):
- # self.mpc_solver.reset()
- self.u_prev = [0] * self.robot.nu
-
- def check_convergence(self, p: np.ndarray, pg: np.ndarray):
- return np.linalg.norm(p - pg) < self.params['convergence_margin']
-
- def e_max(self, rho, epsilon):
- if rho > 0:
- e_max = rho - max(epsilon)
- else:
- e_max = 1.e6
- return e_max
-
- def ref(self, path_pol, s):
- n_pol = self.params['n_pol']
- return [np.polyval(path_pol[j*(n_pol+1):(j+1)*(n_pol+1)], s) for j in range(self.params['np'])]
-
- def compute_u(self, x, p, path_pol, rg, rho, epsilon):
- # Compute MPC solution
-
- # e_max = self.e_max(rho, epsilon) # parameter for tracking error constraint
- # solution = self.mpc_solver.run(x.tolist(), self.u_prev, path_pol, self.params, e_max, rg.tolist(), self.verbosity) # call to Rust solver
- # # Extract first control signal and store it for later
- # self.u_prev = solution['u'][:self.robot.nu]
-
- p = np.array(p)
- e_max = self.e_max(rho, epsilon)
- s_kappa = 0.9 * e_max / self.robot.vmax
- p_ref = self.ref(path_pol, s_kappa)
-
- err = p_ref - p
- dir = np.array([np.cos(x[2]), np.sin(x[2])])
- vel = 0.65 * self.robot.vmax * max(0.1, (dir @ (err / np.linalg.norm(err))))
- wel = 0.85 * ((np.arctan2(err[1], err[0]) - x[2] + np.pi) % (2*np.pi) - np.pi)
-
- alpha = 1
- self.u_prev = [alpha*vel, alpha*wel]
-
- return np.array(self.u_prev)
-
-
-if __name__ == "__main__":
-
- from starworlds.obstacles import StarshapedPolygon
-
- # environment
- obstacles = [
- StarshapedPolygon([[2, 2], [8, 2], [8, 3], [2, 3]]),
- StarshapedPolygon([[2, 3], [3, 3], [3, 4.25], [2, 4.25]]),
- StarshapedPolygon([[2, 5], [8, 5], [8, 6], [2, 6]]),
- StarshapedPolygon([[2, 8], [8, 8], [8, 9], [2, 9]]),
- ]
- pg = np.array([0.5, 5.5]) # goal position
- p0 = np.array([9, 4]) # initial position
- theta0 = np.arctan2(pg[1]-p0[1], pg[0]-p0[0]) # initial heading (simply start heading towards goal)
-
- # robot
- robot_type = "Unicycle"
- with open(r'robot_params.yaml') as f:
- params = yaml.safe_load(f)
- robot_params = params[robot_type]
- robot = Unicycle(width=robot_params['width'],
- vel_min=[robot_params['lin_vel_min'], -robot_params['ang_vel_max']],
- vel_max=[robot_params['lin_vel_max'], robot_params['ang_vel_max']],
- name=robot_type)
- x0 = np.append(p0, [theta0]) # initial robot state
-
- # MPC
- with open(r'./tunnel_mpc_params.yaml') as f:
- params = yaml.safe_load(f)
- mpc_params = params["tunnel_mpc"]
- mpc_params['dp_max'] = robot.vmax * mpc_params['dt']
-
- # components
- verbosity = 1
- scene_updater = SceneUpdater(mpc_params, verbosity)
- path_gen = PathGenerator(mpc_params, verbosity)
- controller = MpcController(mpc_params, robot, verbosity)
-
-
- # plotting
- fig = plt.figure()
- handle_goal = plt.plot(*pg, c="g")[0]
- handle_init = plt.plot(*x0[:2], c="b")[0]
- handle_curr = plt.plot(*x0[:2], c="r", marker=(3, 0, np.rad2deg(x0[2]-np.pi/2)), markersize=10)[0]
- handle_curr_dir = plt.plot(0, 0, marker=(2, 0, np.rad2deg(0)), markersize=5, color='w')[0]
- handle_path = plt.plot([], [], c="k")[0]
- coll = plt_col.PolyCollection([
- np.array([[2, 2], [8, 2], [8, 3], [2, 3]]),
- np.array([[2, 3], [3, 3], [3, 4.25], [2, 4.25]]),
- np.array([[2, 5], [8, 5], [8, 6], [2, 6]]),
- np.array([[2, 8], [8, 8], [8, 9], [2, 9]])
- ])
- plt.gca().add_collection(coll)
- handle_title = plt.text(5, 9.5, "", bbox={'facecolor':'w', 'alpha':0.5, 'pad':5}, ha="center")
- plt.gca().set_aspect("equal")
- plt.xlim([0, 10])
- plt.ylim([0, 10])
- plt.draw()
-
-
- # run the controller
- T_max = 30
- dt = controller.params['dt']
- t = 0.
- x = x0
- u_prev = np.zeros(robot.nu)
- convergence_threshold = 0.05
- converged = False
- try:
- while t < T_max:
- p = robot.h(x)
-
- if np.linalg.norm(p - pg) < convergence_threshold:
- break
-
- # Udpate the scene
- r0, rg, rho, obstacles_star = scene_updater.update(p, pg, obstacles)
-
- # Check for convergence
- if controller.check_convergence(p, pg):
- u = np.zeros(robot.nu)
- else:
- # Update target path
- path_pol, epsilon = path_gen.update(p, r0, rg, rho, obstacles_star)
- # Calculate next control input
- u = controller.compute_u(x, p, path_pol, rg, rho, epsilon)
-
- # update robot position
- x, _ = robot.move(x, u, dt)
- u_prev = u
- t += dt
-
- # plot
- handle_curr.set_data([x[0]], [x[1]])
- handle_curr.set_marker((3, 0, np.rad2deg(x[2]-np.pi/2)))
- handle_curr_dir.set_data([x[0]], [x[1]])
- handle_curr_dir.set_marker((2, 0, np.rad2deg(x[2]-np.pi/2)))
- handle_path.set_data([path_gen.target_path[::2], path_gen.target_path[1::2]])
- handle_title.set_text(f"{t:5.3f}")
- fig.canvas.draw()
- plt.pause(0.005)
-
- except Exception as ex:
- raise ex
- pass
-
- plt.show()
-
diff --git a/python/smc/path_generation/path_math/path2d_to_6d.py b/python/smc/path_generation/path_math/path2d_to_6d.py
new file mode 100644
index 0000000..a90b9c5
--- /dev/null
+++ b/python/smc/path_generation/path_math/path2d_to_6d.py
@@ -0,0 +1,131 @@
+import pinocchio as pin
+import numpy as np
+
+
+def path2D_to_SE3_fixed(path2D: np.ndarray, path_height: float):
+ """
+ path2D_SE3
+ ----------
+ we have a 2D path of (N,2) shape as reference
+ the OCP accepts SE3 (it could just rotations or translation too),
+ so this function constructs it from the 2D path.
+ """
+ #########################
+ # path2D into pathSE2 #
+ #########################
+ # construct theta
+ # since it's a pairwise operation it can't be done on the last point
+ x_i = path2D[:, 0][:-1] # no last element
+ y_i = path2D[:, 1][:-1] # no last element
+ x_i_plus_1 = path2D[:, 0][1:] # no first element
+ y_i_plus_1 = path2D[:, 1][1:] # no first element
+ x_diff = x_i_plus_1 - x_i
+ y_diff = y_i_plus_1 - y_i
+ # elementwise arctan2
+ thetas = np.arctan2(x_diff, y_diff)
+
+ ######################################
+ # construct SE3 from SE2 reference #
+ ######################################
+ # the plan is parallel to the ground because it's a mobile
+ # manipulation task
+ pathSE3 = []
+ for i in range(len(path2D) - 1):
+ # first set the x axis to be in the theta direction
+ # TODO: make sure this one makes sense
+ rotation = np.array(
+ [
+ [-np.cos(thetas[i]), np.sin(thetas[i]), 0.0],
+ [-np.sin(thetas[i]), -np.cos(thetas[i]), 0.0],
+ [0.0, 0.0, -1.0],
+ ]
+ )
+ # rotation = pin.rpy.rpyToMatrix(0.0, 0.0, thetas[i])
+ # rotation = pin.rpy.rpyToMatrix(np.pi/2, np.pi/2,0.0) @ rotation
+ translation = np.array([path2D[i][0], path2D[i][1], path_height])
+ pathSE3.append(pin.SE3(rotation, translation))
+ pathSE3.append(pin.SE3(rotation, translation))
+ return pathSE3
+
+
+# NOTE: this is a complete hack, but it does not need to be
+def path2D_to_SE3_with_transition_from_current_pose(
+ path2D: np.ndarray, path_height: float, T_w_current: pin.SE3
+):
+ """
+ path2D_SE3
+ ----------
+ we have a 2D path of (N,2) shape as reference
+ the OCP accepts SE3 (it could just rotations or translation too),
+ so this function constructs it from the 2D path.
+ """
+ #########################
+ # path2D into pathSE2 #
+ #########################
+ # construct theta
+ # since it's a pairwise operation it can't be done on the last point
+ x_i = path2D[:, 0][:-1] # no last element
+ y_i = path2D[:, 1][:-1] # no last element
+ x_i_plus_1 = path2D[:, 0][1:] # no first element
+ y_i_plus_1 = path2D[:, 1][1:] # no first element
+ x_diff = x_i_plus_1 - x_i
+ y_diff = y_i_plus_1 - y_i
+ # elementwise arctan2
+ thetas = np.arctan2(x_diff, y_diff)
+
+ # there is a height difference, we can't teleport in height
+ height_current = T_w_current.translation[2]
+ height_difference = path_height - height_current
+ height_difference_step = height_difference / len(path2D)
+
+ ######################################
+ # construct SE3 from SE2 reference #
+ ######################################
+ # the plan is parallel to the ground because it's a mobile
+ # manipulation task
+ pathSE3 = []
+ for i in range(len(path2D) - 1):
+ # first set the x axis to be in the theta direction
+ # TODO: make sure this one makes sense
+ # should be: z pointing down (our arbitrary choice)
+ # x follows the path tangent
+ # y just completes the frame (in this case orthogonal to path tangent parallel to the floor).
+ # this is good for cart pulling, but could be anything else for anything else:
+ # you can slap any orientation you want on a 2D path.
+
+ # NOTE: for mpc to follow this reference, the path needs to be followable.
+ # this means we need to interpolate between the current thing and the reference
+ rotation = np.array(
+ [
+ [-np.cos(thetas[i]), np.sin(thetas[i]), 0.0],
+ [-np.sin(thetas[i]), -np.cos(thetas[i]), 0.0],
+ [0.0, 0.0, -1.0],
+ ]
+ )
+ # rotation = pin.rpy.rpyToMatrix(0.0, 0.0, thetas[i])
+ # rotation = pin.rpy.rpyToMatrix(np.pi/2, np.pi/2,0.0) @ rotation
+ translation = np.array(
+ [path2D[i][0], path2D[i][1], height_current + i * height_difference_step]
+ )
+ pathSE3.append(pin.SE3(rotation, translation))
+ pathSE3.append(pin.SE3(rotation, translation))
+ return pathSE3
+
+
+# stupid function for stupid data re-assembly
+# def path2D_to_SE2(path2D : list):
+# path2D = np.array(path2D)
+# # create (N,2) path for list [x0,y0,x1,y1,...]
+# # of course it shouldn't be like that to begin with but
+# # i have no time for that
+# path2D = path2D.reshape((-1, 2))
+# # since it's a pairwise operation it can't be done on the last point
+# x_i = path2D[:,0][:-1] # no last element
+# y_i = path2D[:,1][:-1] # no last element
+# x_i_plus_1 = path2D[:,0][1:] # no first element
+# y_i_plus_1 = path2D[:,1][1:] # no first element
+# # elementwise arctan2
+# thetas = np.arctan2(x_i_plus_1 - x_i, y_i_plus_1 - y_i)
+# thetas = thetas.reshape((-1, 1))
+# path_SE2 = np.hstack((path2D, thetas))
+# return path_SE2
diff --git a/python/smc/path_generation/path_math/path_to_trajectory.py b/python/smc/path_generation/path_math/path_to_trajectory.py
new file mode 100644
index 0000000..743f4c4
--- /dev/null
+++ b/python/smc/path_generation/path_math/path_to_trajectory.py
@@ -0,0 +1,91 @@
+import numpy as np
+
+
+def path2D_timed(args, path2D_untimed, max_base_v):
+ """
+ path2D_timed
+ ---------------------
+ we have a 2D path of (N,2) shape as reference.
+ it times it as this is what the current ocp formulation needs:
+ there should be a timing associated with the path,
+ because defining the cost function as the fit of the rollout to the path
+ is complicated to do software-wise.
+ as it is now, we're pre-selecting points of the path, and associating those
+ with rollout states at a set time step between the states in the rollout.
+ this isn't a problem if we have an idea of how fast the robot can go,
+ which gives us a heuristic of how to select the points (if you went at maximum
+ speed, this is how far you'd go in this time, so this is your point).
+ thankfully this does not need to be exact because we just care about the distance
+ between the current point and the point on the path, so if it's further out
+ that just means the error is larger, and that doesn't necessarily correspond
+ to a different action.
+ NOTE: we are constructing a possibly bullshit
+ trajectory here, it's a man-made heuristic,
+ and we should leave that to the MPC,
+ but that requires too much coding and there is no time rn.
+ the idea is to use compute the tangent of the path,
+ and use that to make a 2D frenet frame.
+ this is the put to some height, making it SE3.
+ i.e. roll and pitch are fixed to be 0,
+ but you could make them some other constant
+ """
+
+ ####################################################
+ # getting a timed 2D trajectory from a heuristic #
+ ####################################################
+ # the strategy is somewhat reasonable at least:
+ # assume we're moving at 90% max velocity in the base,
+ # and use that.
+ perc_of_max_v = 0.9
+ base_v = perc_of_max_v * max_base_v
+
+ # 1) the length of the path divided by 0.9 * max_vel
+ # gives us the total time of the trajectory,
+ # so we first compute that
+ # easiest possible way to get approximate path length
+ # (yes it should be from the polynomial approximation but that takes time to write)
+ x_i = path2D_untimed[:, 0][:-1] # no last element
+ y_i = path2D_untimed[:, 1][:-1] # no last element
+ x_i_plus_1 = path2D_untimed[:, 0][1:] # no first element
+ y_i_plus_1 = path2D_untimed[:, 1][1:] # no first element
+ x_diff = x_i_plus_1 - x_i
+ x_diff = x_diff.reshape((-1, 1))
+ y_diff = y_i_plus_1 - y_i
+ y_diff = y_diff.reshape((-1, 1))
+ path_length = np.sum(np.linalg.norm(np.hstack((x_diff, y_diff)), axis=1))
+ total_time = path_length / base_v
+ # 2) we find the correspondence between s and time
+ # TODO: read from where it should be, but seba checked that it's 5 for some reason
+ # TODO THIS IS N IN PATH PLANNING, MAKE THIS A SHARED ARGUMENT
+ max_s = 5
+ t_to_s = max_s / total_time
+ # 3) construct the ocp-timed 2D path
+ # TODO MAKE REFERENCE_STEP_SIZE A SHARED ARGUMENT
+ # TODO: we should know max s a priori
+ reference_step_size = 0.5
+ s_vec = np.arange(0, len(path2D_untimed)) / reference_step_size
+
+ path2D = []
+ # time is i * args.ocp_dt
+ for i in range(args.n_knots + 1):
+ # what it should be but gets stuck if we're not yet on path
+ # s = (i * args.ocp_dt) * t_to_s
+ # full path
+ # NOTE: this should be wrong, and ocp_dt correct,
+ # but it works better for some reason xd
+ s = i * (max_s / args.n_knots)
+ path2D.append(
+ np.array(
+ [
+ np.interp(s, s_vec, path2D_untimed[:, 0]),
+ np.interp(s, s_vec, path2D_untimed[:, 1]),
+ ]
+ )
+ )
+ path2D = np.array(path2D)
+ return path2D
+
+
+# TODO: todo
+def path2D_to_timed_SE3():
+ pass
diff --git a/python/smc/path_generation/planner.py b/python/smc/path_generation/planner.py
index 02bf2f1..0fb591b 100644
--- a/python/smc/path_generation/planner.py
+++ b/python/smc/path_generation/planner.py
@@ -1,24 +1,13 @@
-from typing import List
-from abc import ABC, abstractmethod
-
-import numpy as np
-from smc.path_generation.starworlds.obstacles import StarshapedObstacle
-from smc.path_generation.starworlds.starshaped_hull import (
- cluster_and_starify,
- ObstacleCluster,
-)
-from smc.path_generation.starworlds.utils.misc import tic, toc
+from smc.path_generation.maps.premade_maps import createSampleStaticMap
from smc.path_generation.star_navigation.robot.unicycle import Unicycle
-from smc.path_generation.starworlds.obstacles import StarshapedPolygon
-import shapely
+from smc.path_generation.scene_updater import SceneUpdater
+from smc.path_generation.path_generator import PathGenerator
+
+from importlib.resources import files
+from multiprocessing import Lock, shared_memory
import yaml
-import pinocchio as pin
import pickle
-from importlib.resources import files
-
-import matplotlib.pyplot as plt
-import matplotlib.collections as plt_col
-from multiprocessing import Queue, Lock, shared_memory
+import numpy as np
def getPlanningArgs(parser):
@@ -57,592 +46,6 @@ def getPlanningArgs(parser):
return parser
-class SceneUpdater:
- def __init__(self, params: dict, verbosity=0):
- self.params = params
- self.verbosity = verbosity
- self.reset()
-
- def reset(self):
- self.obstacle_clusters: List[ObstacleCluster] = None
- self.free_star = None
-
- # TODO: Check why computational time varies so much for same static scene
- @staticmethod
- def workspace_modification(
- obstacles,
- p,
- pg,
- rho0,
- max_compute_time,
- hull_epsilon,
- gamma=0.5,
- make_convex=0,
- obstacle_clusters_prev=None,
- free_star_prev=None,
- verbosity=0,
- ):
-
- # Clearance variable initialization
- rho = rho0 / gamma # First rho should be rho0
- t_init = tic()
-
- while True:
- if toc(t_init) > max_compute_time:
- if verbosity > 0:
- print(
- "[Workspace modification]: Max compute time in rho iteration."
- )
- break
-
- # Reduce rho
- rho *= gamma
-
- # Pad obstacles with rho
- obstacles_rho = [
- o.dilated_obstacle(padding=rho, id="duplicate") for o in obstacles
- ]
-
- # TODO: Fix boundaries
- free_rho = shapely.geometry.box(-20, -20, 20, 20)
- for o in obstacles_rho:
- free_rho = free_rho.difference(o.polygon())
-
- # TODO: Check buffering fix
- # Find P0
- Bp = shapely.geometry.Point(p).buffer(0.95 * rho)
- initial_reference_set = Bp.intersection(free_rho.buffer(-0.1 * rho))
-
- if not initial_reference_set.is_empty:
- break
-
- # Initial and goal reference position selection
- r0_sh, _ = shapely.ops.nearest_points(
- initial_reference_set, shapely.geometry.Point(p)
- )
- r0 = np.array(r0_sh.coords[0])
- rg_sh, _ = shapely.ops.nearest_points(free_rho, shapely.geometry.Point(pg))
- rg = np.array(rg_sh.coords[0])
-
- # TODO: Check more thoroughly
- if free_star_prev is not None:
- free_star_prev = free_star_prev.buffer(-1e-4)
- if (
- free_star_prev is not None
- and free_star_prev.contains(r0_sh)
- and free_star_prev.contains(rg_sh)
- and free_rho.contains(free_star_prev)
- ): # not any([free_star_prev.covers(o.polygon()) for o in obstacles_rho]):
- if verbosity > 1:
- print(
- "[Workspace modification]: Reuse workspace from previous time step."
- )
- obstacle_clusters = obstacle_clusters_prev
- exit_flag = 10
- else:
- # Apply cluster and starify
- obstacle_clusters, obstacle_timing, exit_flag, n_iter = cluster_and_starify(
- obstacles_rho,
- r0,
- rg,
- hull_epsilon,
- max_compute_time=max_compute_time - toc(t_init),
- previous_clusters=obstacle_clusters_prev,
- make_convex=make_convex,
- verbose=verbosity,
- )
-
- free_star = shapely.geometry.box(-20, -20, 20, 20)
- for o in obstacle_clusters:
- free_star = free_star.difference(o.polygon())
-
- compute_time = toc(t_init)
- return obstacle_clusters, r0, rg, rho, free_star, compute_time, exit_flag
-
- def update(
- self, p: np.ndarray, pg: np.ndarray, obstacles
- ) -> tuple[np.ndarray, np.ndarray, float, list[StarshapedObstacle]]:
- # Update obstacles
- if not self.params["use_prev_workspace"]:
- self.free_star = None
- self.obstacle_clusters, r0, rg, rho, self.free_star, _, _ = (
- SceneUpdater.workspace_modification(
- obstacles,
- p,
- pg,
- self.params["rho0"],
- self.params["max_obs_compute_time"],
- self.params["hull_epsilon"],
- self.params["gamma"],
- make_convex=self.params["make_convex"],
- obstacle_clusters_prev=self.obstacle_clusters,
- free_star_prev=self.free_star,
- verbosity=self.verbosity,
- )
- )
- obstacles_star: List[StarshapedObstacle] = [
- o.cluster_obstacle for o in self.obstacle_clusters
- ]
- # Make sure all polygon representations are computed
- [o._compute_polygon_representation() for o in obstacles_star]
-
- return r0, rg, rho, obstacles_star
-
-
-class PathGenerator:
- def __init__(self, params: dict, verbosity=0):
- self.params = params
- self.verbosity = verbosity
- self.reset()
-
- def reset(self):
- self.target_path = []
-
- ### soads.py
-
- # TODO: Check if can make more computationally efficient
- @staticmethod
- def soads_f(
- r,
- rg,
- obstacles: List[StarshapedObstacle],
- adapt_obstacle_velocity=False,
- unit_magnitude=False,
- crep=1.0,
- reactivity=1.0,
- tail_effect=False,
- convergence_tolerance=1e-4,
- d=False,
- ):
- goal_vector = rg - r
- goal_dist = np.linalg.norm(goal_vector)
- if goal_dist < convergence_tolerance:
- return 0 * r
-
- No = len(obstacles)
- fa = goal_vector / goal_dist # Attractor dynamics
- if No == 0:
- return fa
-
- ef = [-fa[1], fa[0]]
- Rf = np.vstack((fa, ef)).T
-
- mu = [obs.reference_direction(r) for obs in obstacles]
- normal = [obs.normal(r) for obs in obstacles]
- gamma = [obs.distance_function(r) for obs in obstacles]
- # Compute weights
- w = PathGenerator.compute_weights(gamma, weightPow=1)
-
- # Compute obstacle velocities
- xd_o = np.zeros((2, No))
- if adapt_obstacle_velocity:
- for i, obs in enumerate(obstacles):
- xd_o[:, i] = obs.vel_intertial_frame(r)
-
- kappa = 0.0
- f_mag = 0.0
- for i in range(No):
- # Compute basis matrix
- E = np.zeros((2, 2))
- E[:, 0] = mu[i]
- E[:, 1] = [-normal[i][1], normal[i][0]]
- # Compute eigenvalues
- D = np.zeros((2, 2))
- D[0, 0] = (
- 1 - crep / (gamma[i] ** (1 / reactivity))
- if tail_effect or normal[i].dot(fa) < 0.0
- else 1
- )
- D[1, 1] = 1 + 1 / gamma[i] ** (1 / reactivity)
- # Compute modulation
- M = E.dot(D).dot(np.linalg.inv(E))
- # f_i = M.dot(fa)
- f_i = M.dot(fa - xd_o[:, i]) + xd_o[:, i]
- # Compute contribution to velocity magnitude
- f_i_abs = np.linalg.norm(f_i)
- f_mag += w[i] * f_i_abs
- # Compute contribution to velocity direction
- nu_i = f_i / f_i_abs
- nu_i_hat = Rf.T.dot(nu_i)
- kappa_i = np.arccos(np.clip(nu_i_hat[0], -1, 1)) * np.sign(nu_i_hat[1])
- kappa += w[i] * kappa_i
- kappa_norm = abs(kappa)
- f_o = (
- Rf.dot([np.cos(kappa_norm), np.sin(kappa_norm) / kappa_norm * kappa])
- if kappa_norm > 0.0
- else fa
- )
-
- if unit_magnitude:
- f_mag = 1.0
- return f_mag * f_o
-
- @staticmethod
- def compute_weights(
- distMeas,
- N=0,
- distMeas_lowerLimit=1,
- weightType="inverseGamma",
- weightPow=2,
- ):
- """Compute weights based on a distance measure (with no upper limit)"""
- distMeas = np.array(distMeas)
- n_points = distMeas.shape[0]
-
- critical_points = distMeas <= distMeas_lowerLimit
-
- if np.sum(critical_points): # at least one
- if np.sum(critical_points) == 1:
- w = critical_points * 1.0
- return w
- else:
- # TODO: continuous weighting function
- # warnings.warn("Implement continuity of weighting function.")
- w = critical_points * 1.0 / np.sum(critical_points)
- return w
-
- distMeas = distMeas - distMeas_lowerLimit
- w = (1 / distMeas) ** weightPow
- if np.sum(w) == 0:
- return w
- w = w / np.sum(w) # Normalization
- return w
-
- ### path_generator.py
-
- @staticmethod
- def pol2pos(path_pol, s):
- n_pol = len(path_pol) // 2 - 1
- return [
- sum(
- [
- path_pol[j * (n_pol + 1) + i] * s ** (n_pol - i)
- for i in range(n_pol + 1)
- ]
- )
- for j in range(2)
- ]
-
- @staticmethod
- def path_generator(
- r0,
- rg,
- obstacles,
- dp_max,
- N,
- dt,
- max_compute_time,
- n_pol,
- ds_decay_rate=0.5,
- ds_increase_rate=2.0,
- max_nr_steps=1000,
- convergence_tolerance=1e-5,
- P_prev=None,
- s_prev=None,
- reactivity=1.0,
- crep=1.0,
- tail_effect=False,
- reference_step_size=0.5,
- verbosity=0,
- ):
- """
- r0 : np.ndarray initial reference position
- rg : np.ndarray goal reference position
- obstacles : list[StarshapedObstacle] obstacles in the scene
- dp_max : float maximum position increment (i.e. vmax * dt)
- N : int ???
- dt : float time step
- max_compute_time : float timeout for computation
- n_pol : int degree of the polynomial that fits the reference ???
- ds_decay_rate : float ???
- ds_increase_rate : float ???
- max_nr_steps : int computation steps threshold
- convergence_tolerance : float ???
- P_prev : np.ndarray previous path ???
- s_prev : np.ndarray previous path time ???
- reactivity : float ???
- crep : float ???
- tail_effect : bool ???
- reference_step_size : float ???
- verbosity : int you guessed it...
- """
-
- t0 = tic()
-
- # Initialize
- ds = 1
- s = np.zeros(max_nr_steps)
- r = np.zeros((max_nr_steps, r0.size))
- if P_prev is not None:
- i = P_prev.shape[0]
- r[:i, :] = P_prev
- s[:i] = s_prev
- else:
- i = 1
- r[0, :] = r0
-
- while True:
- dist_to_goal = np.linalg.norm(r[i - 1, :] - rg)
- # Check exit conditions
- if dist_to_goal < convergence_tolerance:
- if verbosity > 1:
- print(
- f"[Path Generator]: Path converged. {int(100 * (s[i - 1] / N))}% of path completed."
- )
- break
- if s[i - 1] >= N:
- if verbosity > 1:
- print(
- f"[Path Generator]: Completed path length. {int(100 * (s[i - 1] / N))}% of path completed."
- )
- break
- if toc(t0) > max_compute_time:
- if verbosity > 1:
- print(
- f"[Path Generator]: Max compute time in path integrator. {int(100 * (s[i - 1] / N))}% of path completed."
- )
- break
- if i >= max_nr_steps:
- if verbosity > 1:
- print(
- f"[Path Generator]: Max steps taken in path integrator. {int(100 * (s[i - 1] / N))}% of path completed."
- )
- break
-
- # Movement using SOADS dynamics
- dr = min(dp_max, dist_to_goal) * PathGenerator.soads_f(
- r[i - 1, :],
- rg,
- obstacles,
- adapt_obstacle_velocity=False,
- unit_magnitude=True,
- crep=crep,
- reactivity=reactivity,
- tail_effect=tail_effect,
- convergence_tolerance=convergence_tolerance,
- )
-
- r[i, :] = r[i - 1, :] + dr * ds
-
- ri_in_obstacle = False
- while any([o.interior_point(r[i, :]) for o in obstacles]):
- if verbosity > 1:
- print(
- "[Path Generator]: Path inside obstacle. Reducing integration step from {:5f} to {:5f}.".format(
- ds, ds * ds_decay_rate
- )
- )
- ds *= ds_decay_rate
- r[i, :] = r[i - 1, :] + dr * ds
- # Additional compute time check
- if toc(t0) > max_compute_time:
- ri_in_obstacle = True
- break
- if ri_in_obstacle:
- continue
-
- # Update travelled distance
- s[i] = s[i - 1] + ds
- # Try to increase step rate again
- ds = min(ds_increase_rate * ds, 1)
- # Increase iteration counter
- i += 1
-
- r = r[:i, :]
- s = s[:i]
-
- # Evenly spaced path
- s_vec = np.arange(0, s[-1] + reference_step_size, reference_step_size)
- xs, ys = np.interp(s_vec, s, r[:, 0]), np.interp(s_vec, s, r[:, 1])
- # Append not finished path with fixed final position
- s_vec = np.append(
- s_vec,
- np.arange(
- s[-1] + reference_step_size,
- N + reference_step_size,
- reference_step_size,
- ),
- )
- xs = np.append(xs, xs[-1] * np.ones(len(s_vec) - len(xs)))
- ys = np.append(ys, ys[-1] * np.ones(len(s_vec) - len(ys)))
-
- reference_path = [el for p in zip(xs, ys) for el in p] # [x0 y0 x1 y1 ...]
-
- # TODO: Fix when close to goal
- # TODO: Adjust for short arc length, skip higher order terms..
- path_pol = (
- np.polyfit(s_vec, reference_path[::2], n_pol).tolist()
- + np.polyfit(s_vec, reference_path[1::2], n_pol).tolist()
- ) # [px0 px1 ... pxn py0 py1 ... pyn]
- # Force init position to be correct
- path_pol[n_pol] = reference_path[0]
- path_pol[-1] = reference_path[1]
-
- # Compute polyfit approximation error
- epsilon = [
- np.linalg.norm(
- np.array(reference_path[2 * i : 2 * (i + 1)])
- - np.array(PathGenerator.pol2pos(path_pol, s_vec[i]))
- )
- for i in range(N + 1)
- ]
-
- compute_time = toc(t0)
-
- """
- path_pol : np.ndarray the polynomial approximation of `reference_path`
- epsilon : [float] approximation error between the polynomial fit and the actual path
- reference_path : list the actual path (used for P_prev later on) in [x1, y1, x2, y2, ...] format
- compute_time : float overall timing of this function
- """
- return path_pol, epsilon, reference_path, compute_time
-
- def prepare_prev(
- self, p: np.ndarray, rho: float, obstacles_star: List[StarshapedObstacle]
- ):
- P_prev = np.array([self.target_path[::2], self.target_path[1::2]]).T
- # Shift path to start at point closest to robot position
- P_prev = P_prev[np.argmin(np.linalg.norm(p - P_prev, axis=1)) :, :]
- # P_prev[0, :] = self.r0
- if np.linalg.norm(p - P_prev[0, :]) > rho:
- if self.verbosity > 0:
- print(
- "[Path Generator]: No reuse of previous path. Path not within distance rho from robot."
- )
- P_prev = None
- else:
- for r in P_prev:
- if any([o.interior_point(r) for o in obstacles_star]):
- if self.verbosity > 0:
- print(
- "[Path Generator]: No reuse of previous path. Path not collision-free."
- )
- P_prev = None
-
- if P_prev is not None:
- # Cut off stand still padding in previous path
- P_prev_stepsize = np.linalg.norm(np.diff(P_prev, axis=0), axis=1)
- s_prev = np.hstack((0, np.cumsum(P_prev_stepsize) / self.params["dp_max"]))
- P_prev_mask = [True] + (P_prev_stepsize > 1e-8).tolist()
- P_prev = P_prev[P_prev_mask, :]
- s_prev = s_prev[P_prev_mask]
- else:
- s_prev = None
-
- return P_prev, s_prev
-
- def update(
- self,
- p: np.ndarray,
- r0: np.ndarray,
- rg: np.ndarray,
- rho: float,
- obstacles_star: List[StarshapedObstacle],
- ) -> tuple[List[float], float]:
- # Buffer previous target path
- if self.params["buffer"] and self.target_path:
- P_prev, s_prev = self.prepare_prev(p, rho, obstacles_star)
- else:
- P_prev, s_prev = None, None
- # Generate the new path
- path_pol, epsilon, self.target_path, _ = PathGenerator.path_generator(
- r0,
- rg,
- obstacles_star,
- self.params["dp_max"],
- self.params["N"],
- self.params["dt"],
- self.params["max_compute_time"],
- self.params["n_pol"],
- ds_decay_rate=0.5,
- ds_increase_rate=2.0,
- max_nr_steps=1000,
- P_prev=P_prev,
- s_prev=s_prev,
- reactivity=self.params["reactivity"],
- crep=self.params["crep"],
- convergence_tolerance=self.params["convergence_tolerance"],
- verbosity=self.verbosity,
- )
- return path_pol, epsilon
-
-
-def createMap():
- """
- createMap
- ---------
- return obstacles that define the 2D map
- """
- # [lower_left, lower_right, top_right, top_left]
- map_as_list = [
- [[2, 2], [8, 2], [8, 3], [2, 3]],
- [[2, 3], [3, 3], [3, 4.25], [2, 4.25]],
- [[2, 5], [8, 5], [8, 6], [2, 6]],
- [[2, 8], [8, 8], [8, 9], [2, 9]],
- ]
-
- obstacles = []
- for map_element in map_as_list:
- obstacles.append(StarshapedPolygon(map_element))
- return obstacles, map_as_list
-
-
-def pathVisualizer(x0, goal, map_as_list, positions):
- # plotting
- fig = plt.figure()
- handle_goal = plt.plot(*pg, c="g")[0]
- handle_init = plt.plot(*x0[:2], c="b")[0]
- handle_curr = plt.plot(
- *x0[:2], c="r", marker=(3, 0, np.rad2deg(x0[2] - np.pi / 2)), markersize=10
- )[0]
- handle_curr_dir = plt.plot(
- 0, 0, marker=(2, 0, np.rad2deg(0)), markersize=5, color="w"
- )[0]
- handle_path = plt.plot([], [], c="k")[0]
- coll = []
- for map_element in map_as_list:
- coll.append(plt_col.PolyCollection(np.array(map_element)))
- plt.gca().add_collection(coll)
- handle_title = plt.text(
- 5, 9.5, "", bbox={"facecolor": "w", "alpha": 0.5, "pad": 5}, ha="center"
- )
- plt.gca().set_aspect("equal")
- plt.xlim([0, 10])
- plt.ylim([0, 10])
- plt.draw()
-
- # do the updating plotting
- for x in positions:
- handle_curr.set_data([x[0]], [x[1]])
- handle_curr.set_marker((3, 0, np.rad2deg(x[2] - np.pi / 2)))
- handle_curr_dir.set_data([x[0]], [x[1]])
- handle_curr_dir.set_marker((2, 0, np.rad2deg(x[2] - np.pi / 2)))
- handle_path.set_data([path_gen.target_path[::2], path_gen.target_path[1::2]])
- handle_title.set_text(f"{t:5.3f}")
- fig.canvas.draw()
- plt.pause(0.005)
- plt.show()
-
-
-# stupid function for stupid data re-assembly
-# def path2D_to_SE2(path2D : list):
-# path2D = np.array(path2D)
-# # create (N,2) path for list [x0,y0,x1,y1,...]
-# # of course it shouldn't be like that to begin with but
-# # i have no time for that
-# path2D = path2D.reshape((-1, 2))
-# # since it's a pairwise operation it can't be done on the last point
-# x_i = path2D[:,0][:-1] # no last element
-# y_i = path2D[:,1][:-1] # no last element
-# x_i_plus_1 = path2D[:,0][1:] # no first element
-# y_i_plus_1 = path2D[:,1][1:] # no first element
-# # elementwise arctan2
-# thetas = np.arctan2(x_i_plus_1 - x_i, y_i_plus_1 - y_i)
-# thetas = thetas.reshape((-1, 1))
-# path_SE2 = np.hstack((path2D, thetas))
-# return path_SE2
-
-
def pathPointFromPathParam(n_pol, path_dim, path_pol, s):
return [
np.polyval(path_pol[j * (n_pol + 1) : (j + 1) * (n_pol + 1)], s)
@@ -650,141 +53,6 @@ def pathPointFromPathParam(n_pol, path_dim, path_pol, s):
]
-def path2D_timed(args, path2D_untimed, max_base_v):
- """
- path2D_timed
- ---------------------
- we have a 2D path of (N,2) shape as reference.
- it times it as this is what the current ocp formulation needs:
- there should be a timing associated with the path,
- because defining the cost function as the fit of the rollout to the path
- is complicated to do software-wise.
- as it is now, we're pre-selecting points of the path, and associating those
- with rollout states at a set time step between the states in the rollout.
- this isn't a problem if we have an idea of how fast the robot can go,
- which gives us a heuristic of how to select the points (if you went at maximum
- speed, this is how far you'd go in this time, so this is your point).
- thankfully this does not need to be exact because we just care about the distance
- between the current point and the point on the path, so if it's further out
- that just means the error is larger, and that doesn't necessarily correspond
- to a different action.
- NOTE: we are constructing a possibly bullshit
- trajectory here, it's a man-made heuristic,
- and we should leave that to the MPC,
- but that requires too much coding and there is no time rn.
- the idea is to use compute the tangent of the path,
- and use that to make a 2D frenet frame.
- this is the put to some height, making it SE3.
- i.e. roll and pitch are fixed to be 0,
- but you could make them some other constant
- """
-
- ####################################################
- # getting a timed 2D trajectory from a heuristic #
- ####################################################
- # the strategy is somewhat reasonable at least:
- # assume we're moving at 90% max velocity in the base,
- # and use that.
- perc_of_max_v = 0.9
- base_v = perc_of_max_v * max_base_v
-
- # 1) the length of the path divided by 0.9 * max_vel
- # gives us the total time of the trajectory,
- # so we first compute that
- # easiest possible way to get approximate path length
- # (yes it should be from the polynomial approximation but that takes time to write)
- x_i = path2D_untimed[:, 0][:-1] # no last element
- y_i = path2D_untimed[:, 1][:-1] # no last element
- x_i_plus_1 = path2D_untimed[:, 0][1:] # no first element
- y_i_plus_1 = path2D_untimed[:, 1][1:] # no first element
- x_diff = x_i_plus_1 - x_i
- x_diff = x_diff.reshape((-1, 1))
- y_diff = y_i_plus_1 - y_i
- y_diff = y_diff.reshape((-1, 1))
- path_length = np.sum(np.linalg.norm(np.hstack((x_diff, y_diff)), axis=1))
- total_time = path_length / base_v
- # 2) we find the correspondence between s and time
- # TODO: read from where it should be, but seba checked that it's 5 for some reason
- # TODO THIS IS N IN PATH PLANNING, MAKE THIS A SHARED ARGUMENT
- max_s = 5
- t_to_s = max_s / total_time
- # 3) construct the ocp-timed 2D path
- # TODO MAKE REFERENCE_STEP_SIZE A SHARED ARGUMENT
- # TODO: we should know max s a priori
- reference_step_size = 0.5
- s_vec = np.arange(0, len(path2D_untimed)) / reference_step_size
-
- path2D = []
- # time is i * args.ocp_dt
- for i in range(args.n_knots + 1):
- # what it should be but gets stuck if we're not yet on path
- # s = (i * args.ocp_dt) * t_to_s
- # full path
- # NOTE: this should be wrong, and ocp_dt correct,
- # but it works better for some reason xd
- s = i * (max_s / args.n_knots)
- path2D.append(
- np.array(
- [
- np.interp(s, s_vec, path2D_untimed[:, 0]),
- np.interp(s, s_vec, path2D_untimed[:, 1]),
- ]
- )
- )
- path2D = np.array(path2D)
- return path2D
-
-
-def path2D_to_SE3(path2D, path_height):
- """
- path2D_SE3
- ----------
- we have a 2D path of (N,2) shape as reference
- the OCP accepts SE3 (it could just rotations or translation too),
- so this function constructs it from the 2D path.
- """
- #########################
- # path2D into pathSE2 #
- #########################
- # construct theta
- # since it's a pairwise operation it can't be done on the last point
- x_i = path2D[:, 0][:-1] # no last element
- y_i = path2D[:, 1][:-1] # no last element
- x_i_plus_1 = path2D[:, 0][1:] # no first element
- y_i_plus_1 = path2D[:, 1][1:] # no first element
- x_diff = x_i_plus_1 - x_i
- y_diff = y_i_plus_1 - y_i
- # elementwise arctan2
- thetas = np.arctan2(x_diff, y_diff)
-
- ######################################
- # construct SE3 from SE2 reference #
- ######################################
- # the plan is parallel to the ground because it's a mobile
- # manipulation task
- pathSE3 = []
- for i in range(len(path2D) - 1):
- # first set the x axis to be in the theta direction
- # TODO: make sure this one makes sense
- rotation = np.array(
- [
- [-np.cos(thetas[i]), np.sin(thetas[i]), 0.0],
- [-np.sin(thetas[i]), -np.cos(thetas[i]), 0.0],
- [0.0, 0.0, -1.0],
- ]
- )
- # rotation = pin.rpy.rpyToMatrix(0.0, 0.0, thetas[i])
- # rotation = pin.rpy.rpyToMatrix(np.pi/2, np.pi/2,0.0) @ rotation
- translation = np.array([path2D[i][0], path2D[i][1], path_height])
- pathSE3.append(pin.SE3(rotation, translation))
- pathSE3.append(pin.SE3(rotation, translation))
- return pathSE3
-
-
-def path2D_to_timed_SE3(todo):
- pass
-
-
def starPlanner(goal, args, init_cmd, shm_name, lock: Lock, shm_data):
"""
starPlanner
@@ -804,7 +72,8 @@ def starPlanner(goal, args, init_cmd, shm_name, lock: Lock, shm_data):
p_shared = np.ndarray((2,), dtype=np.float64, buffer=shm.buf)
p = np.zeros(2)
# environment
- obstacles, _ = createMap()
+ # TODO: DIRTY HACK YOU CAN'T JUST HAVE THIS HERE !!!!!!!!!!!!!!!!
+ obstacles, _ = createSampleStaticMap()
robot_type = "Unicycle"
with open(args.planning_robot_params_file) as f:
@@ -854,9 +123,6 @@ def starPlanner(goal, args, init_cmd, shm_name, lock: Lock, shm_data):
r0, rg, rho, obstacles_star = scene_updater.update(p, goal, obstacles)
# compute the path
path_pol, epsilon = path_gen.update(p, r0, rg, rho, obstacles_star)
- # TODO: this is stupid, just used shared memory bro
- # if data_queue.qsize() < 1:
- # data_queue.put((path_pol, path_gen.target_path))
data_pickled = pickle.dumps((path_pol, path_gen.target_path))
lock.acquire()
shm_data.buf[: len(data_pickled)] = data_pickled
diff --git a/python/smc/path_generation/scene_updater.py b/python/smc/path_generation/scene_updater.py
new file mode 100644
index 0000000..a6db75f
--- /dev/null
+++ b/python/smc/path_generation/scene_updater.py
@@ -0,0 +1,142 @@
+from smc.path_generation.starworlds.utils.misc import tic, toc
+from smc.path_generation.starworlds.obstacles import StarshapedObstacle
+from smc.path_generation.starworlds.starshaped_hull import (
+ cluster_and_starify,
+ ObstacleCluster,
+)
+
+import numpy as np
+import shapely
+from typing import List
+
+
+class SceneUpdater:
+ def __init__(self, params: dict, verbosity=0):
+ self.params = params
+ self.verbosity = verbosity
+ self.reset()
+
+ def reset(self):
+ self.obstacle_clusters: List[ObstacleCluster] = None
+ self.free_star = None
+
+ # TODO: Check why computational time varies so much for same static scene
+ @staticmethod
+ def workspace_modification(
+ obstacles,
+ p,
+ pg,
+ rho0,
+ max_compute_time,
+ hull_epsilon,
+ gamma=0.5,
+ make_convex=0,
+ obstacle_clusters_prev=None,
+ free_star_prev=None,
+ verbosity=0,
+ ):
+
+ # Clearance variable initialization
+ rho = rho0 / gamma # First rho should be rho0
+ t_init = tic()
+
+ while True:
+ if toc(t_init) > max_compute_time:
+ if verbosity > 0:
+ print(
+ "[Workspace modification]: Max compute time in rho iteration."
+ )
+ break
+
+ # Reduce rho
+ rho *= gamma
+
+ # Pad obstacles with rho
+ obstacles_rho = [
+ o.dilated_obstacle(padding=rho, id="duplicate") for o in obstacles
+ ]
+
+ # TODO: Fix boundaries
+ free_rho = shapely.geometry.box(-20, -20, 20, 20)
+ for o in obstacles_rho:
+ free_rho = free_rho.difference(o.polygon())
+
+ # TODO: Check buffering fix
+ # Find P0
+ Bp = shapely.geometry.Point(p).buffer(0.95 * rho)
+ initial_reference_set = Bp.intersection(free_rho.buffer(-0.1 * rho))
+
+ if not initial_reference_set.is_empty:
+ break
+
+ # Initial and goal reference position selection
+ r0_sh, _ = shapely.ops.nearest_points(
+ initial_reference_set, shapely.geometry.Point(p)
+ )
+ r0 = np.array(r0_sh.coords[0])
+ rg_sh, _ = shapely.ops.nearest_points(free_rho, shapely.geometry.Point(pg))
+ rg = np.array(rg_sh.coords[0])
+
+ # TODO: Check more thoroughly
+ if free_star_prev is not None:
+ free_star_prev = free_star_prev.buffer(-1e-4)
+ if (
+ free_star_prev is not None
+ and free_star_prev.contains(r0_sh)
+ and free_star_prev.contains(rg_sh)
+ and free_rho.contains(free_star_prev)
+ ): # not any([free_star_prev.covers(o.polygon()) for o in obstacles_rho]):
+ if verbosity > 1:
+ print(
+ "[Workspace modification]: Reuse workspace from previous time step."
+ )
+ obstacle_clusters = obstacle_clusters_prev
+ exit_flag = 10
+ else:
+ # Apply cluster and starify
+ obstacle_clusters, obstacle_timing, exit_flag, n_iter = cluster_and_starify(
+ obstacles_rho,
+ r0,
+ rg,
+ hull_epsilon,
+ max_compute_time=max_compute_time - toc(t_init),
+ previous_clusters=obstacle_clusters_prev,
+ make_convex=make_convex,
+ verbose=verbosity,
+ )
+
+ free_star = shapely.geometry.box(-20, -20, 20, 20)
+ for o in obstacle_clusters:
+ free_star = free_star.difference(o.polygon())
+
+ compute_time = toc(t_init)
+ return obstacle_clusters, r0, rg, rho, free_star, compute_time, exit_flag
+
+ def update(
+ self, p: np.ndarray, pg: np.ndarray, obstacles
+ ) -> tuple[np.ndarray, np.ndarray, float, list[StarshapedObstacle]]:
+ # Update obstacles
+ if not self.params["use_prev_workspace"]:
+ self.free_star = None
+ self.obstacle_clusters, r0, rg, rho, self.free_star, _, _ = (
+ SceneUpdater.workspace_modification(
+ obstacles,
+ p,
+ pg,
+ self.params["rho0"],
+ self.params["max_obs_compute_time"],
+ self.params["hull_epsilon"],
+ self.params["gamma"],
+ make_convex=self.params["make_convex"],
+ obstacle_clusters_prev=self.obstacle_clusters,
+ free_star_prev=self.free_star,
+ verbosity=self.verbosity,
+ )
+ )
+ obstacles_star: List[StarshapedObstacle] = [
+ o.cluster_obstacle for o in self.obstacle_clusters
+ ]
+ # Make sure all polygon representations are computed
+ [o._compute_polygon_representation() for o in obstacles_star]
+
+ return r0, rg, rho, obstacles_star
diff --git a/python/smc/robots/implementations/heron.py b/python/smc/robots/implementations/heron.py
index 025c50f..9054445 100644
--- a/python/smc/robots/implementations/heron.py
+++ b/python/smc/robots/implementations/heron.py
@@ -1,12 +1,17 @@
-import time
-from smc.robots.robotmanager_abstract import AbstractRobotManager
+from smc.robots.abstract_simulated_robot import AbstractSimulatedRobotManager
+from smc.robots.interfaces.force_torque_sensor_interface import (
+ ForceTorqueOnSingleArmWrist,
+)
+from smc.robots.interfaces.mobile_base_interface import MobileBaseInterface
+from smc.robots.implementations.ur5e import get_model
+import time
+import numpy as np
+import pinocchio as pin
+import hppfcl as fcl
-class RobotManagerHeron(AbstractRobotManager):
- @property
- def args(self):
- return self._args
+class RobotManagerHeron(ForceTorqueOnSingleArmWrist, MobileBaseInterface):
@property
def model(self):
return self._model
@@ -24,17 +29,66 @@ class RobotManagerHeron(AbstractRobotManager):
return self._collision_model
def __init__(self, args):
- self._args = args
+ if args.debug_prints:
+ print("RobotManagerHeron init")
self._model, self._collision_model, self._visual_model, self._data = (
heron_approximation()
)
- self.ee_frame_id = self.model.getFrameId("tool0")
+ self._ee_frame_id = self.model.getFrameId("tool0")
+ self._base_frame_id = self.model.getFrameId("mobile_base")
+ # TODO: CHANGE THIS TO REAL VALUES
+ self._MAX_ACCELERATION = 1.7 # const
+ self._MAX_QD = 3.14 # const
+ super().__init__(args)
+
+
+class SimulatedHeronRobotManager(AbstractSimulatedRobotManager, RobotManagerHeron):
+ def __init__(self, args):
+ if args.debug_prints:
+ print("SimulatedRobotManagerHeron init")
+ super().__init__(args)
+
+ # NOTE: overriding wrench stuff here
+ # there can be a debated whether there should be a simulated forcetorquesensorinterface,
+ # but it's annoying as hell and there is no immediate benefit in solving this problem
+ def _updateWrench(self):
+ self._wrench_base = np.random.random(6)
+ # NOTE: create a robot_math module, make this mapping a function called
+ # mapse3ToDifferent frame or something like that
+ mapping = np.zeros((6, 6))
+ mapping[0:3, 0:3] = self._T_w_e.rotation
+ mapping[3:6, 3:6] = self._T_w_e.rotation
+ self._wrench = mapping.T @ self._wrench_base
+
+ def zeroFtSensor(self) -> None:
+ self._wrench_bias = np.zeros(6)
class RealHeronRobotManager(RobotManagerHeron):
def __init__(self, args):
+ pass
+
+
+"""
+TODO: finish
+ self._speed_slider = 1.0 # const
+ self._rtde_control: RTDEControlInterface
+ self._rtde_receive: RTDEReceiveInterface
+ self._rtde_io: RTDEIOInterface
super().__init__(args)
+
+ def connectToGripper(self):
+ if (self.args.gripper == "none") or not self.args.real:
+ self.gripper = None
+ return
+ if self.args.gripper == "robotiq":
+ self.gripper = RobotiqGripper()
+ self.gripper.connect(self.args.robot_ip, 63352)
+ self.gripper.activate()
+ if self.args.gripper == "onrobot":
+ self.gripper = TwoFG()
+
# TODO: here assert you need to have ros2 installed to run on real heron
# and then set all this bullshit here instead of elsewhere
def set_publisher_vel_base(self, publisher_vel_base):
@@ -81,6 +135,22 @@ class RealHeronRobotManager(RobotManagerHeron):
# NOTE: only works for the arm
self._rtde_control.endFreedriveMode()
+ def _updateWrench(self):
+ if not self.args.real:
+ self._wrench_base = np.random.random(6)
+ else:
+ # NOTE: UR5e's ft-sensors gives readings in robot's base frame
+ self._wrench_base = (
+ np.array(self._rtde_receive.getActualTCPForce()) - self._wrench_bias
+ )
+ # NOTE: we define the default wrench to be given in the end-effector frame
+ mapping = np.zeros((6, 6))
+ mapping[0:3, 0:3] = self._T_w_e.rotation
+ mapping[3:6, 3:6] = self._T_w_e.rotation
+ self._wrench = mapping.T @ self._wrench_base
+
+"""
+
def heron_approximation():
# arm + gripper
diff --git a/python/smc/robots/interfaces/mobile_base_interface.py b/python/smc/robots/interfaces/mobile_base_interface.py
index e69de29..8c2e904 100644
--- a/python/smc/robots/interfaces/mobile_base_interface.py
+++ b/python/smc/robots/interfaces/mobile_base_interface.py
@@ -0,0 +1,78 @@
+import numpy as np
+import pinocchio as pin
+from smc.robots.robotmanager_abstract import AbstractRobotManager
+
+
+class MobileBaseInterface(AbstractRobotManager):
+ """
+ MobileBaseInterface
+ -------------------
+ This interface assumes that the first joint in the kinematic chain will be a planarJoint,
+ modelling the mobile base of the robot.
+ This does not exactly work for underactuated bases like differential drive,
+ as some control inputs shouldn't even exist for this to be modelled properly.
+ As it currently stands, the underactuation is enforced at the control level
+ instead of the modelling level.
+ One day this might be a "fully actuated base" class,
+ implementing an abstract mobile base interface.
+ """
+
+ def __init__(self, args):
+ if args.debug_prints:
+ print("MobileBase init")
+ self._base_frame_id: int
+ self._T_w_b: pin.SE3
+ super().__init__(args)
+
+ @property
+ def base_position2D(self):
+ return self._q[:2]
+
+ @property
+ def base_position3D(self):
+ return np.array(list(self._q[:2]) + [0.0])
+
+ @property
+ def base_SE2_pose(self):
+ # NOTE: idk if this is the best way to calculate theta
+ # _q[:4] = [x, y, cos(theta), sin(theta)]
+ theta = np.arccos(self._q[2])
+ return np.array(list(self._q[:2]) + [theta])
+
+ @property
+ def base_frame_id(self):
+ return self._base_frame_id
+
+ @property
+ def T_w_b(self):
+ return self._T_w_b.copy()
+
+ def computeT_w_b(self, q) -> pin.SE3:
+ assert type(q) is np.ndarray
+ pin.forwardKinematics(
+ self.model,
+ self.data,
+ q,
+ )
+ # alternative if you want all frames
+ # pin.updateFramePlacements(self.model, self.data)
+ pin.updateFramePlacement(self.model, self.data, self._base_frame_id)
+ return self.data.oMf[self._base_frame_id].copy()
+
+ # TODO: make use of this for a standalone robot like the omnibot
+ # just driving around with a full-body robot without using hands.
+ # not prio right now and I don't want to deal with
+ # how this interacts with other interfaces
+ # def forwardKinematics(self):
+ # pin.forwardKinematics(
+ # self.model,
+ # self.data,
+ # self._q,
+ # )
+ # pin.updateFramePlacement(self.model, self.data, self._ee_frame_id)
+ # self._T_w_e = self.data.oMf[self._ee_frame_id].copy()
+
+ # def _step(self):
+ # self._updateQ()
+ # self._updateV()
+ # self.forwardKinematics()
diff --git a/python/smc/robots/robotmanager_abstract.py b/python/smc/robots/robotmanager_abstract.py
index 5f59cde..0e6cee6 100644
--- a/python/smc/robots/robotmanager_abstract.py
+++ b/python/smc/robots/robotmanager_abstract.py
@@ -96,8 +96,9 @@ class AbstractRobotManager(abc.ABC):
self._log_manager = Logger(args)
# since there is only one robot and one visualizer, this is robotmanager's job
+ # TODO: this should probably be transferred to a multiprocess manager,
+ # or in whatever way be detangled from robots
self.visualizer_manager: ProcessManager
- # TODO: this should be transferred to an multiprocess manager
if self.args.visualizer:
side_function = partial(
manipulatorVisualizer,
@@ -286,6 +287,10 @@ class AbstractRobotManager(abc.ABC):
else:
print("not implemented yet, so nothing is going to happen!")
+ ########################################################################################
+ # visualizer management. ideally transferred elsewhere
+ ###################################################################################
+
def killManipulatorVisualizer(self):
"""
killManipulatorVisualizer
@@ -314,6 +319,25 @@ class AbstractRobotManager(abc.ABC):
if self.args.debug_prints:
print("you didn't select viz")
+ def sendRectangular2DMapToVisualizer(self, map_as_list):
+ for obstacle in map_as_list:
+ length = obstacle[1][0] - obstacle[0][0]
+ width = obstacle[3][1] - obstacle[0][1]
+ height = 0.4 # doesn't matter because plan because planning is 2D
+ pose = pin.SE3(
+ np.eye(3),
+ np.array(
+ [
+ obstacle[0][0] + (obstacle[1][0] - obstacle[0][0]) / 2,
+ obstacle[0][1] + (obstacle[3][1] - obstacle[0][1]) / 2,
+ 0.0,
+ ]
+ ),
+ )
+ dims = [length, width, height]
+ command = {"obstacle_box": [pose, dims]}
+ self.visualizer_manager.sendCommand(command)
+
class AbstractRealRobotManager(AbstractRobotManager, abc.ABC):
"""
diff --git a/python/smc/robots/utils.py b/python/smc/robots/utils.py
index dbe9508..d185741 100644
--- a/python/smc/robots/utils.py
+++ b/python/smc/robots/utils.py
@@ -248,10 +248,15 @@ def getRobotFromArgs(args: argparse.Namespace) -> AbstractRobotManager:
return RealUR5eRobotManager(args)
else:
return SimulatedUR5eRobotManager(args)
+ if args.robot == "heron":
+ if args.real:
+ pass
+ # TODO: finish it
+ # return RealHeronRobotManager(args)
+ else:
+ return SimulatedHeronRobotManager(args)
-# if args.robot == "heron":
-# return RealUR5eRobotManager(args)
# if args.robot == "mir":
# return RealUR5eRobotManager(args)
# if args.robot == "yumi":
diff --git a/python/smc/visualization/meshcat_viewer_wrapper/visualizer.py b/python/smc/visualization/meshcat_viewer_wrapper/visualizer.py
index bdd34e3..45b02cc 100644
--- a/python/smc/visualization/meshcat_viewer_wrapper/visualizer.py
+++ b/python/smc/visualization/meshcat_viewer_wrapper/visualizer.py
@@ -79,7 +79,7 @@ class MeshcatVisualizer(PMV):
def addBox(self, name, dims, color):
material = materialFromColor(color)
self.viewer[name].set_object(meshcat.geometry.Box(dims), material)
-
+
def addBoxObstacle(self, pose, dims):
color = [0.5, 0.5, 0.5, 0.8]
obstacle_name = f"world/obstacle_{self.n_obstacles}"
@@ -96,18 +96,17 @@ class MeshcatVisualizer(PMV):
self.applyConfiguration(obstacle_name, pose)
self.n_obstacles += 1
-
def addEllipsoid(self, name, dims, color):
material = materialFromColor(color)
self.viewer[name].set_object(meshcat.geometry.Ellipsoid(dims), material)
- def addPoint(self, point : pin.SE3, radius=5e-3, color=[1, 0, 0, 0.8]):
+ def addPoint(self, point: pin.SE3, radius=5e-3, color=[1, 0, 0, 0.8]):
point_name = f"world/point_{self.n_points}"
self.addSphere(point_name, radius, color)
self.applyConfiguration(point_name, point)
self.n_points += 1
-
- def addPath(self, name, path : list[pin.SE3], radius=5e-3, color=[1, 0, 0, 0.8]):
+
+ def addPath(self, name, path: list[pin.SE3], radius=5e-3, color=[1, 0, 0, 0.8]):
# who cares about the name
name = "path"
if type(path) == np.ndarray:
@@ -120,22 +119,25 @@ class MeshcatVisualizer(PMV):
self.applyConfiguration(f"world/path_{name}_point_{i}", point)
self.n_path_points = max(i, self.n_path_points)
- def addFramePath(self, name, path : list[pin.SE3], radius=5e-3, color=[1, 0, 0, 0.8]):
+ def addFramePath(
+ self, name, path: list[pin.SE3], radius=5e-3, color=[1, 0, 0, 0.8]
+ ):
# who cares about the name
name = "frame_path"
for i, point in enumerate(path):
if i < self.n_frame_path_points:
- meshcat_shapes.frame(self.viewer[f"world/frame_path_{name}_point_{i}"], opacity=0.3)
+ meshcat_shapes.frame(
+ self.viewer[f"world/frame_path_{name}_point_{i}"], opacity=0.3
+ )
self.applyConfiguration(f"world/frame_path_{name}_point_{i}", point)
self.n_frame_path_points = max(i, self.n_frame_path_points)
-
def applyConfiguration(self, name, placement):
if isinstance(placement, list) or isinstance(placement, tuple):
placement = np.array(placement)
if isinstance(placement, pin.SE3):
- #R, p = placement.rotation, placement.translation
- #T = np.r_[np.c_[R, p], [[0, 0, 0, 1]]]
+ # R, p = placement.rotation, placement.translation
+ # T = np.r_[np.c_[R, p], [[0, 0, 0, 1]]]
T = placement.homogeneous
elif isinstance(placement, np.ndarray):
if placement.shape == (7,): # XYZ-quat
diff --git a/python/smc/visualization/path_visualizer.py b/python/smc/visualization/path_visualizer.py
new file mode 100644
index 0000000..86669c3
--- /dev/null
+++ b/python/smc/visualization/path_visualizer.py
@@ -0,0 +1,40 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.collections as plt_col
+
+
+def pathVisualizer(x0, goal, map_as_list, positions, path_gen):
+ # plotting
+ fig = plt.figure()
+ handle_goal = plt.plot(*pg, c="g")[0]
+ handle_init = plt.plot(*x0[:2], c="b")[0]
+ handle_curr = plt.plot(
+ *x0[:2], c="r", marker=(3, 0, np.rad2deg(x0[2] - np.pi / 2)), markersize=10
+ )[0]
+ handle_curr_dir = plt.plot(
+ 0, 0, marker=(2, 0, np.rad2deg(0)), markersize=5, color="w"
+ )[0]
+ handle_path = plt.plot([], [], c="k")[0]
+ coll = []
+ for map_element in map_as_list:
+ coll.append(plt_col.PolyCollection(np.array(map_element)))
+ plt.gca().add_collection(coll)
+ handle_title = plt.text(
+ 5, 9.5, "", bbox={"facecolor": "w", "alpha": 0.5, "pad": 5}, ha="center"
+ )
+ plt.gca().set_aspect("equal")
+ plt.xlim([0, 10])
+ plt.ylim([0, 10])
+ plt.draw()
+
+ # do the updating plotting
+ for x in positions:
+ handle_curr.set_data([x[0]], [x[1]])
+ handle_curr.set_marker((3, 0, np.rad2deg(x[2] - np.pi / 2)))
+ handle_curr_dir.set_data([x[0]], [x[1]])
+ handle_curr_dir.set_marker((2, 0, np.rad2deg(x[2] - np.pi / 2)))
+ handle_path.set_data([path_gen.target_path[::2], path_gen.target_path[1::2]])
+ handle_title.set_text(f"{t:5.3f}")
+ fig.canvas.draw()
+ plt.pause(0.005)
+ plt.show()
--
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