From ebae23bb5074d405a32b2046f18f62d631b4ddd1 Mon Sep 17 00:00:00 2001
From: m-guberina <gubi.guberina@gmail.com>
Date: Sat, 23 Nov 2024 12:37:08 +0100
Subject: [PATCH] unified multiprocessing API, cleaned up code. not perfect
because the communication paradigm is not enforced, but then again the
communication is so simple and straightforward that this is perfectly usable
as-is right now. with this i wrote a 'ProcessManager' for camera input, and a
ControlLoopManager to go along with it to test that it works
---
.gitignore | 2 +-
Dockerfile | 2 +-
TODOS | 5 +
extensions_for_fun.md | 16 +
python/examples/camera_no_lag.py | 97 +-
.../casadi_ocp_collision_avoidance.py | 15 +-
python/examples/clik.py | 2 +
python/examples/crocoddyl_mpc.py | 1 +
python/examples/crocoddyl_ocp_clik.py | 19 +-
python/examples/drawing_from_input_drawing.py | 1 +
python/examples/joint_trajectory.csv | 719 ++++----------
python/examples/path_in_pixels.csv | 922 ++++--------------
python/examples/point_force_control.py | 9 +
python/ur_simple_control/__init__.py | 2 +-
.../__pycache__/__init__.cpython-312.pyc | Bin 391 -> 399 bytes
.../__pycache__/managers.cpython-312.pyc | Bin 48338 -> 49235 bytes
.../basics/__pycache__/basics.cpython-312.pyc | Bin 12728 -> 12790 bytes
python/ur_simple_control/basics/basics.py | 7 +-
python/ur_simple_control/clik/clik.py | 3 +
python/ur_simple_control/managers.py | 208 ++--
.../create_pinocchio_casadi_ocp.py | 216 +++-
.../util/encapsulating_ellipses.py | 299 ++++--
python/ur_simple_control/vision/vision.py | 32 +
.../__pycache__/visualize.cpython-312.pyc | Bin 16912 -> 16540 bytes
.../ur_simple_control/visualize/visualize.py | 19 +-
25 files changed, 1005 insertions(+), 1591 deletions(-)
create mode 100644 extensions_for_fun.md
create mode 100644 python/examples/point_force_control.py
create mode 100644 python/ur_simple_control/vision/vision.py
diff --git a/.gitignore b/.gitignore
index af1c190..9f12f4e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,4 +5,4 @@ build/
**__pycache__
.vscode/
**.pickle
-*.csv
+**.csv
diff --git a/Dockerfile b/Dockerfile
index 485f285..a3af123 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -76,7 +76,7 @@ RUN pip install -e ./SimpleManipulatorControl/python/
RUN conda config --add channels conda-forge
#RUN conda install --solver=classic conda-forge::conda-libmamba-solver conda-forge::libmamba conda-forge::libmambapy conda-forge::libarchive
RUN conda install -y casadi opencv
-RUN conda install --solver=classic -y pinocchio -c conda-forge
+RUN conda install --solver=classic -y pinocchio crocoddyl -c conda-forge
RUN pip install matplotlib meshcat ur_rtde argcomplete \
qpsolvers ecos example_robot_data meshcat_shapes \
pyqt6
diff --git a/TODOS b/TODOS
index 2b541ce..419550a 100644
--- a/TODOS
+++ b/TODOS
@@ -1,3 +1,6 @@
+START THE REAL-TIME PLOT CAUSES A DELAY BECAUSE YOU WAIT ON IT
+LET IT START ITSELF, DON'T GIVE THE SUCCESS FLAG BACK!!!!!!!!!
+
goal 1: starting points for student projects
---------------------------------------------
1. PUSHING GROUP: some multiprocessing to get camera outputs
@@ -152,6 +155,8 @@ goal 4: more controllers
goal 5: more convenience - set gripper payload etc automatically
------------------------
+1. test whether meshcat.display(q) is a blocking or a non-blocking call.
+ if it's a non-blocking call then you don't need to run a separate process to command the visualization
# TODO and NOTE the weight, TCP and inertial matrix needs to be set on the robot
# you already found an API in rtde_control for this, just put it in initialization
# under using/not-using gripper parameters
diff --git a/extensions_for_fun.md b/extensions_for_fun.md
new file mode 100644
index 0000000..dd27d2c
--- /dev/null
+++ b/extensions_for_fun.md
@@ -0,0 +1,16 @@
+1. easy to use DMPs
+--------------------
+
+1.1. cartesian space dmp.
+----------------------------
+the current one unnecessarily assumes joint space,
+but if you rewrite the code it could do either based on an argument.
+as far as the dmp is concerned only the dimensions are different,
+and you can just ik the cartesian reference into joint space
+
+1.2. using multiple trajectories for a fit
+---------------------------------------------
+the fitting is already there, it just assumes a single trajectory (indirectly,
+the math is the same either way).
+adding more should literally just amount to verifying the dimensions
+in the math.
diff --git a/python/examples/camera_no_lag.py b/python/examples/camera_no_lag.py
index 122f086..9b84d9a 100644
--- a/python/examples/camera_no_lag.py
+++ b/python/examples/camera_no_lag.py
@@ -1,22 +1,75 @@
-import cv2
-
-#camera = cv2.VideoCapture(0)
-# if you have ip_webcam set-up you can do this (but you have put in the correct ip)
-camera = cv2.VideoCapture("http://192.168.219.153:8080/video")
-
-while True:
- (grabbed, frame) = camera.read()
-# frame = cv2.flip(frame, 1)
- scale_percent = 100 # percent of original size
- width = int(frame.shape[1] * scale_percent / 100)
- height = int(frame.shape[0] * scale_percent / 100)
- dim = (width, height)
-
- # resize image
- frame = cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)
-# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
- cv2.imshow("camera", frame)
- keypress = cv2.waitKey(1) & 0xFF
-
- if keypress == ord("q"):
- break
+from ur_simple_control.vision.vision import processCamera
+from ur_simple_control.managers import getMinimalArgParser, ControlLoopManager, RobotManager, ProcessManager
+import argcomplete, argparse
+import numpy as np
+import time
+import pinocchio as pin
+from functools import partial
+
+
+def get_args():
+ parser = getMinimalArgParser()
+ parser.description = 'dummy camera output, but being processed \
+ in a different process'
+ argcomplete.autocomplete(parser)
+ args = parser.parse_args()
+ return args
+
+def controlLoopWithCamera(camera_manager : ProcessManager, args, robot : RobotManager, i, past_data):
+ """
+ controlLoopWithCamera
+ -----------------------------
+ do nothing while getting dummy camera input
+ """
+ breakFlag = False
+ log_item = {}
+ save_past_dict = {}
+ q = robot.getQ()
+
+ camera_output = camera_manager.getData()
+ #print(camera_output)
+
+ qd_cmd = np.zeros(robot.model.nv)
+
+ robot.sendQd(qd_cmd)
+
+ log_item['qs'] = q.reshape((robot.model.nq,))
+ log_item['dqs'] = robot.getQd().reshape((robot.model.nv,))
+ log_item['camera_output'] = np.array([camera_output['x'],
+ camera_output['y']])
+ return breakFlag, save_past_dict, log_item
+
+if __name__ == "__main__":
+ args = get_args()
+ robot = RobotManager(args)
+
+ # cv2 camera device 0
+ device = 0
+ side_function = partial(processCamera, device)
+ init_value = {"x" : np.random.randint(0, 10),
+ "y" : np.random.randint(0, 10)}
+ camera_manager = ProcessManager(args, side_function, {}, 1, init_value=init_value)
+
+
+ log_item = {}
+ log_item['qs'] = np.zeros((robot.model.nq,))
+ log_item['dqs'] = np.zeros((robot.model.nv,))
+ log_item['camera_output'] = np.zeros(2)
+ controlLoop = partial(controlLoopWithCamera, camera_manager, args, robot)
+ loop_manager = ControlLoopManager(robot, controlLoop, args, {}, log_item)
+ loop_manager.run()
+
+
+
+ # get expected behaviour here (library can't know what the end is - you have to do this here)
+ if not args.pinocchio_only:
+ robot.stopRobot()
+
+ if args.save_log:
+ robot.log_manager.plotAllControlLoops()
+
+ if args.visualize_manipulator:
+ robot.killManipulatorVisualizer()
+
+ if args.save_log:
+ robot.log_manager.saveLog()
diff --git a/python/examples/casadi_ocp_collision_avoidance.py b/python/examples/casadi_ocp_collision_avoidance.py
index dd2ef0b..d2a3c10 100644
--- a/python/examples/casadi_ocp_collision_avoidance.py
+++ b/python/examples/casadi_ocp_collision_avoidance.py
@@ -5,10 +5,14 @@ import time
import argparse
from functools import partial
from ur_simple_control.managers import getMinimalArgParser, ControlLoopManager, RobotManager
+from ur_simple_control.optimal_control.create_pinocchio_casadi_ocp import createCasadiIKObstacleAvoidanceOCP
from ur_simple_control.optimal_control.get_ocp_args import get_OCP_args
+from ur_simple_control.basics.basics import followKinematicJointTrajP
+import argcomplete
def get_args():
parser = getMinimalArgParser()
+ parser = get_OCP_args(parser)
parser.description = 'optimal control in pinocchio.casadi for obstacle avoidance'
# add more arguments here from different Simple Manipulator Control modules
argcomplete.autocomplete(parser)
@@ -18,7 +22,15 @@ def get_args():
if __name__ == "__main__":
args = get_args()
robot = RobotManager(args)
-
+ #T_goal = robot.defineGoalPointCLI()
+ #T_goal = pin.SE3.Random()
+ T_goal = robot.defineGoalPointCLI()
+ T_goal.rotation = robot.getT_w_e().rotation
+ if args.visualize_manipulator:
+ robot.manipulator_visualizer_queue.put(
+ {"Mgoal" : T_goal})
+ reference, opti = createCasadiIKObstacleAvoidanceOCP(args, robot, T_goal)
+ followKinematicJointTrajP(args, robot, reference)
# get expected behaviour here (library can't know what the end is - you have to do this here)
@@ -33,4 +45,3 @@ if __name__ == "__main__":
if args.save_log:
robot.log_manager.saveLog()
- #loop_manager.stopHandler(None, None)
diff --git a/python/examples/clik.py b/python/examples/clik.py
index 0ccf6c8..cd07a47 100644
--- a/python/examples/clik.py
+++ b/python/examples/clik.py
@@ -1,6 +1,7 @@
# PYTHON_ARGCOMPLETE_OK
import numpy as np
import time
+import pinocchio as pin
import argcomplete, argparse
from functools import partial
from ur_simple_control.managers import getMinimalArgParser, ControlLoopManager, RobotManager
@@ -19,6 +20,7 @@ def get_args():
if __name__ == "__main__":
args = get_args()
robot = RobotManager(args)
+ print(robot.getT_w_e())
Mgoal = robot.defineGoalPointCLI()
compliantMoveL(args, robot, Mgoal)
robot.closeGripper()
diff --git a/python/examples/crocoddyl_mpc.py b/python/examples/crocoddyl_mpc.py
index 556f71a..8b95290 100644
--- a/python/examples/crocoddyl_mpc.py
+++ b/python/examples/crocoddyl_mpc.py
@@ -14,6 +14,7 @@ from ur_simple_control.clik.clik import getClikArgs
import pinocchio as pin
import crocoddyl
import mim_solvers
+import argcomplete
def get_args():
diff --git a/python/examples/crocoddyl_ocp_clik.py b/python/examples/crocoddyl_ocp_clik.py
index c062fce..71cc5bf 100644
--- a/python/examples/crocoddyl_ocp_clik.py
+++ b/python/examples/crocoddyl_ocp_clik.py
@@ -11,6 +11,7 @@ from ur_simple_control.util.logging_utils import LogManager
from ur_simple_control.visualize.visualize import plotFromDict
import pinocchio as pin
import crocoddyl
+import argcomplete
def get_args():
@@ -27,6 +28,12 @@ if __name__ == "__main__":
# TODO: put this back for nicer demos
#Mgoal = robot.defineGoalPointCLI()
Mgoal = pin.SE3.Random()
+
+ if args.visualize_manipulator:
+ # TODO document this somewhere
+ robot.manipulator_visualizer_queue.put(
+ {"Mgoal" : Mgoal})
+
# 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),
@@ -36,9 +43,12 @@ if __name__ == "__main__":
problem = createCrocoIKOCP(args, robot, x0, Mgoal)
# this shouldn't really depend on x0 but i can't be bothered
reference, solver = solveCrocoOCP(args, robot, problem, x0)
- if args.solver == "boxfddp":
- log = solver.getCallbacks()[1]
- crocoddyl.plotOCSolution(log.xs, log.us, figIndex=1, show=True)
+
+# NOTE: IF YOU PLOT SOMETHING OTHER THAN REAL-TIME PLOTTING FIRST IT BREAKS EVERYTHING
+# if args.solver == "boxfddp":
+# log = solver.getCallbacks()[1]
+# crocoddyl.plotOCSolution(log.xs, log.us, figIndex=1, show=True)
+
# we need a way to follow the reference trajectory,
# both because there can be disturbances,
# and because it is sampled at a much lower frequency
@@ -47,7 +57,6 @@ if __name__ == "__main__":
print("final position:")
print(robot.getT_w_e())
- robot.log_manager.plotAllControlLoops()
if not args.pinocchio_only:
robot.stopRobot()
@@ -57,5 +66,5 @@ if __name__ == "__main__":
if args.save_log:
robot.log_manager.saveLog()
- #loop_manager.stopHandler(None, None)
+ robot.log_manager.plotAllControlLoops()
diff --git a/python/examples/drawing_from_input_drawing.py b/python/examples/drawing_from_input_drawing.py
index 5c77507..0b12e57 100644
--- a/python/examples/drawing_from_input_drawing.py
+++ b/python/examples/drawing_from_input_drawing.py
@@ -1,4 +1,5 @@
# PYTHON_ARGCOMPLETE_OK
+# TODO: make 1/beta = 5
import pinocchio as pin
import numpy as np
import matplotlib.pyplot as plt
diff --git a/python/examples/joint_trajectory.csv b/python/examples/joint_trajectory.csv
index ede219b..1bbd90f 100644
--- a/python/examples/joint_trajectory.csv
+++ b/python/examples/joint_trajectory.csv
@@ -1,546 +1,173 @@
-0.00000,1.26610,-1.48461,-1.32203,-1.03743,1.81923,-0.19287
-0.03670,1.26607,-1.48462,-1.32205,-1.03740,1.81924,-0.19288
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diff --git a/python/examples/path_in_pixels.csv b/python/examples/path_in_pixels.csv
index 24c30ee..8fb43e1 100644
--- a/python/examples/path_in_pixels.csv
+++ b/python/examples/path_in_pixels.csv
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diff --git a/python/examples/point_force_control.py b/python/examples/point_force_control.py
new file mode 100644
index 0000000..56ff417
--- /dev/null
+++ b/python/examples/point_force_control.py
@@ -0,0 +1,9 @@
+"""
+idea
+----
+do nothing until you feel a force of some number of newtons.
+then apply 2N in gripper z direction. and try to maintain it
+if this works try writing (wiping first) with a force reference instead of
+compliance.
+other than the force, maintaing being in whatever z axis is in cartesian space.
+"""
diff --git a/python/ur_simple_control/__init__.py b/python/ur_simple_control/__init__.py
index 5cb8d28..75986aa 100644
--- a/python/ur_simple_control/__init__.py
+++ b/python/ur_simple_control/__init__.py
@@ -1,4 +1,4 @@
from ur_simple_control import clik, dmp, robot_descriptions, util, visualize
__all__ = [
-"clik", "dmp", "robot_descriptions", "util", "visualize", "basics"
+"clik", "dmp", "robot_descriptions", "util", "visualize", "basics", "vision"
]
diff --git a/python/ur_simple_control/__pycache__/__init__.cpython-312.pyc b/python/ur_simple_control/__pycache__/__init__.cpython-312.pyc
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diff --git a/python/ur_simple_control/basics/basics.py b/python/ur_simple_control/basics/basics.py
index 17e58a4..baf51a2 100644
--- a/python/ur_simple_control/basics/basics.py
+++ b/python/ur_simple_control/basics/basics.py
@@ -92,7 +92,7 @@ def followKinematicJointTrajPControlLoop(stop_at_final : bool, robot: RobotManag
t_index_upper = len(reference['qs']) - 1
q_ref = reference['qs'][t_index_upper]
if (t_index_upper == len(reference['qs']) - 1) and stop_at_final:
- v_ref = np.zeros(len(reference['vs'][t_index_upper]))
+ v_ref = np.zeros(robot.model.nv)
else:
v_ref = reference['vs'][t_index_upper]
@@ -115,6 +115,7 @@ def followKinematicJointTrajPControlLoop(stop_at_final : bool, robot: RobotManag
q_ref = pin.integrate(robot.model, reference['qs'][t_index_lower], 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":
@@ -152,10 +153,8 @@ def followKinematicJointTrajP(args, robot, reference):
}
loop_manager = ControlLoopManager(robot, controlLoop, args, {}, log_item)
loop_manager.run()
- # TODO: remove, this isn't doing anything
- #time.sleep(0.01)
if args.debug_prints:
- print("MoveJP done: convergence achieved, reached destionation!")
+ print("followKinematicJointTrajP done: reached path destionation!")
diff --git a/python/ur_simple_control/clik/clik.py b/python/ur_simple_control/clik/clik.py
index 1e511a5..5ec984d 100644
--- a/python/ur_simple_control/clik/clik.py
+++ b/python/ur_simple_control/clik/clik.py
@@ -401,6 +401,9 @@ def clikCartesianPathIntoJointPath(args, robot, path, \
# TODO handle saving more consistently/intentionally
# (although this definitely works right now and isn't bad, just mid)
# os.makedir -p a data dir and save there, this is ugly
+ # TODO: check if we actually need this and if not remove the saving
+ # whatever code uses this is responsible to log it if it wants it,
+ # let's not have random files around.
np.savetxt("./joint_trajectory.csv", joint_trajectory, delimiter=',', fmt='%.5f')
return joint_trajectory
diff --git a/python/ur_simple_control/managers.py b/python/ur_simple_control/managers.py
index 898d1e5..28f10d3 100644
--- a/python/ur_simple_control/managers.py
+++ b/python/ur_simple_control/managers.py
@@ -1,6 +1,8 @@
# PYTHON_ARGCOMPLETE_OK
# TODO rename all private variables to start with '_'
# TODO: make importing nicer with __init__.py files
+# put all managers into a managers folder,
+# and have each manager in a separate file, this is getting ridiculous
import pinocchio as pin
import numpy as np
import time
@@ -22,6 +24,7 @@ import argcomplete, argparse
from sys import exc_info
from types import NoneType
from os import getpid
+from functools import partial
"""
general notes
@@ -223,22 +226,9 @@ class ControlLoopManager:
self.log_dict[key] = []
if self.args.real_time_plotting:
- self.plotter_queue = Queue()
- if self.args.debug_prints:
- print("CONTROL_LOOP_MANAGER", self.controlLoop, ": i created queue for real time plotting:", self.plotter_queue)
- print("CONTROL_LOOP_MANAGER: i am creating and starting the real-time-plotter process")
- self.real_time_plotter_process = Process(target=realTimePlotter,
- args=(self.args, self.plotter_queue, ))
+ self.plotter_manager = ProcessManager(args, realTimePlotter, log_item, 0)
# give real-time plotter some time to set itself up
- self.real_time_plotter_process.start()
- if self.args.debug_prints:
- print("CONTROL_LOOP_MANAGER: real_time_plotter_process started")
- # wait for feedback that the thing has started
- self.plotter_queue.get()
- self.plotter_queue.put(log_item)
- if self.args.debug_prints:
- print("CONTROL_LOOP_MANAGER: i managed to put initializing log_item to queue")
-
+ #self.plotter_manager.sendCommand(log_item)
def run(self):
"""
@@ -285,16 +275,16 @@ class ControlLoopManager:
if i % 20 == 0:
# don't send what wasn't ready
if self.args.visualize_manipulator:
- if self.robot_manager.manipulator_visualizer_queue.qsize() < 5:
- self.robot_manager.updateViz({"q" : self.robot_manager.q,
- "T_w_e" : self.robot_manager.getT_w_e()})
+ self.robot_manager.visualizer_manager.sendCommand({"q" : self.robot_manager.q,
+ "T_w_e" : self.robot_manager.getT_w_e()})
+ #if self.robot_manager.manipulator_visualizer_queue.qsize() < 5:
+ # self.robot_manager.updateViz({"q" : self.robot_manager.q,
+ # "T_w_e" : self.robot_manager.getT_w_e()})
if self.args.real_time_plotting:
# don't put new stuff in if it didn't handle the previous stuff.
# it's a plotter, who cares if it's late.
# the number 5 is arbitrary
- if self.plotter_queue.qsize() < 5:
- self.plotter_queue.put_nowait(log_item)
- #print("plotter_queue size status:", self.plotter_queue.qsize())
+ self.plotter_manager.sendCommand(log_item)
# break if done
if breakFlag:
@@ -318,16 +308,7 @@ class ControlLoopManager:
# for over
######################################################################
if self.args.real_time_plotting:
- if self.args.debug_prints:
- print("i am putting befree in plotter_queue to stop the real time visualizer")
- self.plotter_queue.put_nowait("befree")
- try:
- self.real_time_plotter_process.terminate()
- if self.args.debug_prints:
- print("terminated real_time_plotter_process")
- except AttributeError:
- if self.args.debug_prints:
- print("real-time-plotter is dead already")
+ self.plotter_manager.terminateProcess()
if self.args.save_log:
self.robot_manager.log_manager.storeControlLoopRun(self.log_dict, self.controlLoop.func.__name__, self.final_iteration)
if i < self.max_iterations -1:
@@ -357,6 +338,7 @@ class ControlLoopManager:
for i in range(300):
vel_cmd = np.zeros(self.robot_manager.model.nv)
self.robot_manager.sendQd(vel_cmd)
+
# hopefully this actually stops it
if not self.args.pinocchio_only:
self.robot_manager.rtde_control.speedStop(1)
@@ -371,48 +353,11 @@ class ControlLoopManager:
self.robot_manager.log_manager.storeControlLoopRun(self.log_dict, self.controlLoop.func.__name__, self.final_iteration)
self.robot_manager.log_manager.saveLog()
- ####################################
- # kill real-time-plotter process #
- ####################################
if self.args.real_time_plotting:
- if self.args.debug_prints:
- print("i am putting befree in plotter_queue to stop the real time visualizer")
- self.plotter_queue.put_nowait("befree")
- # for good measure
- time.sleep(0.1)
- #if type(self.real_time_plotter_process) != NoneType:
- try:
- self.real_time_plotter_process.terminate()
- if self.args.debug_prints:
- print("terminated real_time_plotter_process")
- except AttributeError:
- if self.args.debug_prints:
- print("real-time-plotter is dead already")
+ self.plotter_manager.terminateProcess()
- #############################
- # kill visualizer process #
- #############################
if self.args.visualize_manipulator:
- if self.args.debug_prints:
- print("i am putting befree in manipulator to stop the manipulator visualizer")
- self.robot_manager.manipulator_visualizer_queue.put_nowait({"befree" : "befree"})
- # for good measure
- time.sleep(0.1)
- #if type(self.robot_manager.manipulator_visualizer_process) != NoneType:
- try:
- self.robot_manager.manipulator_visualizer_process.terminate()
- if self.args.debug_prints:
- print("terminated manipulator_visualizer_process")
- except AttributeError:
- if self.args.debug_prints:
- print("real-time-plotter is dead already")
-
- # let's plot the loop you stopped.
- # --> but then we stop here and can't kill the program with ctrl-c
- # and that is insufferable. if you want the plot open the log
- #for key in self.log_dict:
- # self.log_dict[key] = np.array(self.log_dict[key])
- #plotFromDict(self.log_dict, self.final_iteration, self.args)
+ self.robot_manager.visualizer_manager.terminateProcess()
if not self.args.pinocchio_only:
self.robot_manager.rtde_control.endFreedriveMode()
@@ -468,20 +413,8 @@ class RobotManager:
# start visualize manipulator process if selected.
# has to be started here because it lives throughout the whole run
if args.visualize_manipulator:
- self.manipulator_visualizer_queue = Queue()
- if args.debug_prints:
- print("ROBOT_MANAGER: i created queue for manipulator visualization:", self.manipulator_visualizer_queue)
- print("ROBOT_MANAGER: i am creating and starting the manipulator visualizer process")
- self.manipulator_visualizer_process = Process(target=manipulatorVisualizer,
- args=(self.args, self.model, self.collision_model, self.visual_model, self.manipulator_visualizer_queue, ))
- self.manipulator_visualizer_process.start()
- # give it time to start
- time.sleep(2)
- if args.debug_prints:
- print("ROBOT_MANAGER: manipulator_visualizer_process started")
- self.manipulator_visualizer_queue.put(np.zeros(self.model.nq))
- if args.debug_prints:
- print("ROBOT_MANAGER: i managed to put initializing q to manipulator_visualizer_queue")
+ side_function = partial(manipulatorVisualizer, self.model, self.collision_model, self.visual_model)
+ self.visualizer_manager = ProcessManager(args, side_function, {"q" : np.zeros(self.model.nq)}, 0)
# create log manager if we're saving logs
if args.save_log:
@@ -571,7 +504,7 @@ class RobotManager:
q = np.array(q)
self.q = q
if args.visualize_manipulator:
- self.manipulator_visualizer_queue.put({"q" : q})
+ self.visualizer_manager.sendCommand({"q" : q})
# do it once to get T_w_e
@@ -673,7 +606,6 @@ class RobotManager:
# computeAllTerms is certainly not necessary btw
# but if it runs on time, does it matter? it makes everything available...
# (includes forward kinematics, all jacobians, all dynamics terms, energies)
- # NOTE: it's too slow -> has to have pinocchio with multithreading
#pin.computeAllTerms(self.model, self.data, self.q, self.v_q)
pin.forwardKinematics(self.model, self.data, self.q, self.v_q)
pin.updateFramePlacement(self.model, self.data, self.ee_frame_id)
@@ -976,7 +908,7 @@ class RobotManager:
self.Mgoal = Mgoal
if self.args.visualize_manipulator:
# TODO document this somewhere
- self.manipulator_visualizer_queue.put(
+ self.visualizer_manager.sendCommand(
{"Mgoal" : Mgoal})
return Mgoal
@@ -992,14 +924,7 @@ class RobotManager:
RobotManager has to handle the meshcat server.
and in this case the user needs to say when the tasks are done.
"""
- if self.args.debug_prints:
- print("i am putting befree in plotter_queue to stop the manipulator visualizer")
- # putting this command tells our process to kill the meshcat zmq server process
- self.manipulator_visualizer_queue.put_nowait({"befree" : "befree"})
- time.sleep(0.1)
- self.manipulator_visualizer_process.terminate()
- if self.args.debug_prints:
- print("terminated manipulator_visualizer_process")
+ self.visualizer_manager.terminateProcess()
def stopRobot(self):
if not self.args.pinocchio_only:
@@ -1029,7 +954,98 @@ class RobotManager:
because it shouldn't - it should only update the visualizer
"""
if self.args.visualize_manipulator:
- self.manipulator_visualizer_queue.put_nowait(viz_dict)
+ self.visualizer_manager.sendCommand(viz_dict)
else:
if self.args.debug_prints:
print("you didn't select viz")
+
+class ProcessManager:
+ """
+ ProcessManager
+ --------------
+ A way to do processing in a thread (process because GIL) different
+ from the main one which is reserved
+ for ControlLoopManager.
+ The primary goal is to process visual input
+ from the camera without slowing down control.
+ TODO: once finished, map real-time-plotting and
+ visualization with this (it already exists, just not in a manager).
+ What this should do is hide the code for starting a process,
+ and to enforce the communication defined by the user.
+ To make life simpler, all communication is done with Queues.
+ There are two Queues - one for commands,
+ and the other for receiving the process' output.
+ Do note that this is obviously not the silver bullet for every
+ possible inter-process communication scenario,
+ but the aim of this library is to be as simple as possible,
+ not as most performant as possible.
+ NOTE: the maximum number of things in the command queue is arbitrarily
+ set to 5. if you need to get the result immediately,
+ calculate whatever you need in main, not in a side process.
+ this is meant to be used for operations that take a long time
+ and aren't critical, like reading for a camera.
+ """
+ # NOTE: theoretically we could pass existing queues so that we don't
+ # need to create new ones, but i won't optimize in advance
+ def __init__(self, args, side_function, init_command, comm_direction, init_value=None):
+ self.args = args
+
+ if comm_direction == 0:
+ self.command_queue = Queue()
+ self.side_process = Process(target=side_function,
+ args=(args, init_command, self.command_queue,))
+ if comm_direction == 1:
+ self.data_queue = Queue()
+ self.side_process = Process(target=side_function,
+ args=(args, init_command, self.data_queue,))
+ if comm_direction == 2:
+ self.command_queue = Queue()
+ self.data_queue = Queue()
+ self.side_process = Process(target=side_function,
+ args=(args, init_command, self.command_queue, self.data_queue,))
+ if type(side_function) == partial:
+ self.side_process.name = side_function.func.__name__
+ else:
+ self.side_process.name = side_function.__name__ + "_process"
+ self.lastest_data = init_value
+ if self.args.debug_prints:
+ print(f"PROCESS_MANAGER: i created the command queue for {self.side_process.name}", self.command_queue)
+
+ self.side_process.start()
+ if self.args.debug_prints:
+ print("PROCESS_MANAGER: i am starting {self.side_process.name}")
+
+ # TODO: enforce that
+ # the key should be a string containing the command,
+ # and the value should be the data associated with the command,
+ # just to have some consistency
+ def sendCommand(self, command : dict):
+ """
+ sendCommand
+ ------------
+ assumes you're calling from controlLoop and that
+ you want a non-blocking call.
+ the maximum number of things in the command queue is arbitrarily
+ set to 5. if you need to get the result immediately,
+ calculate whatever you need in main, not in a side process.
+ """
+ if self.command_queue.qsize() < 5:
+ self.command_queue.put_nowait(command)
+
+ def getData(self):
+ if not self.data_queue.empty():
+ self.lastest_data = self.data_queue.get_nowait()
+ return self.lastest_data.copy()
+
+ def terminateProcess(self):
+ if self.args.debug_prints:
+ print(f"i am putting befree in {self.side_process.name}'s command queue to stop it")
+ self.command_queue.put_nowait("befree")
+ try:
+ self.side_process.terminate()
+ if self.args.debug_prints:
+ print(f"terminated {self.side_process.name}")
+ except AttributeError:
+ if self.args.debug_prints:
+ print(f"{self.side_process.name} is dead already")
+
diff --git a/python/ur_simple_control/optimal_control/create_pinocchio_casadi_ocp.py b/python/ur_simple_control/optimal_control/create_pinocchio_casadi_ocp.py
index d0e2717..a11cf65 100644
--- a/python/ur_simple_control/optimal_control/create_pinocchio_casadi_ocp.py
+++ b/python/ur_simple_control/optimal_control/create_pinocchio_casadi_ocp.py
@@ -3,8 +3,11 @@ import pinocchio as pin
from pinocchio import casadi as cpin
import numpy as np
from ur_simple_control.managers import RobotManager, ControlLoopManager
+from ur_simple_control.util.encapsulating_ellipses import computeEncapsulatingEllipses, visualizeEllipses
import pickle
from importlib.resources import files
+from types import SimpleNamespace
+from os import path
"""
CREDIT: jrnh2023 pinocchio.casadi tutorial
@@ -29,104 +32,208 @@ The following tools are used:
It assumes that the ellipses parameters are already computed, see ellipses.py for that.
"""
-def createCasadiIKObstacleAvoidanceOCP(args, robot : RobotManager, x0, goal: pin.SE3):
- # TODO have ellipses in robot.ellipses. this is loaded if you load whatever argument is responsible
- # or you load them here from a file in robot_descriptions
- # which you need to generate from generate_ellipses.py in utils
- # there should be one for each link more or less
- # TODO: make this for every robot.
- # TODO; calculate ellipses if they don't exist already
+# TODO: finish and verify
+# make a separate function for a single ellipse based on the namespace
+# if necessary save more info from solver
+# --> memory is cheaper than computation,
+# don't save something only if it makes the struct confusing
+def getSelfCollisionObstacles(args, robot : RobotManager):
ellipses_path = files('ur_simple_control.robot_descriptions.ellipses').joinpath("ur5e_robotiq_ellipses.pickle")
- file = open(ellipses_path, 'rb')
- ellipses = pickle.load(file)
- file.close()
+ if path.exists(ellipses_path):
+ file = open(ellipses_path, 'rb')
+ ellipses = pickle.load(file)
+ file.close()
+ else:
+ ellipses = computeEncapsulatingEllipses(args, robot)
for ellipse in ellipses:
- ellipse.id = model.getJointId(ellipse.name)
+ ellipse.id = robot.model.getJointId(ellipse.name)
l, P = np.linalg.eig(ellipse.A)
ellipse.radius = 1 / l**0.5
ellipse.rotation = P
- # TODO: use wrapped meshcat visualizer to have access to more nifty plotting
- #ellipse.placement = pin.SE3(P, ellipse.center)
- #viz.addEllipsoid(f"el_{e.name}", e.radius, [0.3, 0.9, 0.3, 0.3])
+
+ # TODO: move from here, you want this updated.
+ # so it should be handled by some
+ #if args.visualize_ellipses:
+ # visualizeEllipses(args, robot, ellipses)
+
+ return ellipses
+
+def createCasadiIKObstacleAvoidanceOCP(args, robot : RobotManager, T_goal: pin.SE3):
+ # casadi equivalents of core pinocchio classes (used for everything)
+ cmodel = cpin.Model(robot.model)
+ cdata = cmodel.createData()
+ nq = robot.model.nq
+ nv = robot.model.nv
+ q0 = robot.getQ()
+ v0 = robot.getQd()
+ # casadi symbolic variables for joint angles q
+ cq = casadi.SX.sym("q", robot.model.nq, 1)
+ cv = casadi.SX.sym("v", robot.model.nv, 1)
+ # generate forward kinematics in casadi
+ cpin.framesForwardKinematics(cmodel, cdata, cq)
+
+ # kinematics constraint
+ cnext = casadi.Function(
+ "next",
+ [cq, cv],
+ [cpin.integrate(cmodel, cq, cv * args.ocp_dt)]
+ )
+
+ # error to goal function
+ error6_tool = casadi.Function(
+ "etool",
+ [cq],
+ [cpin.log6(cdata.oMf[robot.ee_frame_id].inverse() * cpin.SE3(T_goal)).vector],
+ )
+
# TODO: obstacles
# here you need to define other obstacles, namely the table (floor)
# it's obviously going to be a plane
# alternatively just forbid z-axis of end-effector to be negative
obstacles = []
+# obstacles = [
+# SimpleNamespace(radius=0.01, pos=np.array([-0.4, 0.2 + s, 0.5]), name=f"obs_{i_s}")
+# for i_s, s in enumerate(np.arange(-0.5, 0.5, 0.25))
+# ]
+ # define the optimizer/solver
+ opti = casadi.Opti()
- cmodel = cpin.Model(robot.model)
- cdata = cmodel.createData()
+ # one set of decision variables per knot (both states and control input)
+ var_qs = [opti.variable(nq) for t in range(args.n_knots + 1)]
+ var_vs = [opti.variable(nv) for t in range(args.n_knots)]
+
+ # running costs - x**2 and u**2 - used more for regularization than anything else
+ totalcost = 0
+ for t in range(args.n_knots):
+ # running
+ totalcost += 1e-3 * args.ocp_dt * casadi.sumsqr(var_qs[t])
+ totalcost += 1e-4 * args.ocp_dt * casadi.sumsqr(var_vs[t])
+ # terminal cost
+ totalcost += 1e4 * casadi.sumsqr(error6_tool(var_qs[args.n_knots]))
- #######################################################################
- # kinematics level #
- #######################################################################
-
-# cq = casadi.SX.sym("q", robot.model.nq, 1)
-# cpin.framesForwardKinematics(cmodel, cdata, cq)
-# error6_tool = casadi.Function(
-# "etool",
-# [cq],
-# [cpin.log6(cdata.oMf[robot.ee_frame_id].inverse() * cpin.SE3(Mtarget)).vector],
-# )
+
+ # initial state constraints
+ # TODO: idk if you need to input x0 in this way, maybe robot.getQ(d) is better?
+ opti.subject_to(var_qs[0] == q0)
+ opti.subject_to(var_vs[0] == v0)
+ # kinematics constraints between every knot pair
+ for t in range(args.n_knots):
+ opti.subject_to(cnext(var_qs[t], var_vs[t]) == var_qs[t + 1])
+ opti.subject_to(var_vs[t] <= np.ones(robot.model.nv) * robot.max_qd)
+ opti.subject_to(var_vs[t] >= -1 * np.ones(robot.model.nv) * robot.max_qd)
+
+ # obstacle avoidance (hard) constraints: no ellipse should intersect any of the obstacles
+ ellipses = getSelfCollisionObstacles(args, robot)
+ cpos = casadi.SX.sym("p", 3) # obstacle position in ellipse frame
for ellipse in ellipses:
- # Position of the obstacle cpos in the ellipse frame.
ellipse.e_pos = casadi.Function(
- f"e{e.id}", [cq, cpos], [cdata.oMi[ellipse.id].inverse().act(casadi.SX(cpos))]
+ f"e{ellipse.name}", [cq, cpos], [cdata.oMi[ellipse.id].inverse().act(casadi.SX(cpos))]
)
- #######################################################################
- # dynamics level #
- #######################################################################
+ for obstacle in obstacles:
+ for q in var_qs:
+ # obstacle position in ellipsoid (joint) frame
+ #e_pos = e.e_pos(var_q, o.pos)
+ e_pos = ellipse.e_pos(q, obstacle.pos)
+ opti.subject_to((e_pos - ellipse.center).T @ ellipse.A @ (e_pos - ellipse.center) >= 1)
- # variables on a single time-step
+ # now that the ocp has been transcribed as nlp,
+ # solve it
+ opti.minimize(totalcost)
+ opti.minimize(totalcost)
+ p_opts = dict(print_time=False, verbose=False)
+ s_opts = dict(print_level=0)
+ opti.solver("ipopt") # set numerical backend
+ opti.set_initial(var_qs[0], q0)
+ try:
+ sol = opti.solve_limited()
+ #sol_q = opti.value(var_q)
+ sol_qs = [opti.value(var_q) for var_q in var_qs]
+ sol_vs = [opti.value(var_v) for var_v in var_vs]
+ except:
+ print("ERROR in convergence, plotting debug info.")
+ #sol_q = opti.debug.value(var_q)
+ sol_qs = [opti.debug.value(var_q) for var_q in var_qs]
+ sol_vs = [opti.debug.value(var_v) for var_v in var_vs]
+
+ reference = {'qs' : sol_qs, 'vs' : sol_vs, 'dt' : args.ocp_dt}
+ return reference, opti
+
+def createCasadiReachingObstacleAvoidanceDynamicsOCP(args, robot : RobotManager, x0, goal : pin.SE3):
+ # state = [joint_positions, joint_velocities], i.e. x = [q, v].
+ # the velocity dimension does not need to be the same as the positions,
+ # ex. consider a differential drive robot: it covers [x,y,theta], but has [vx, vtheta] as velocity
nx = robot.model.nq + robot.model.nv
ndx = 2 * robot.model.nv
+ x0 = np.concat((robot.getQ(), robot.getQd()))
cx = casadi.SX.sym("x", nx, 1)
+ # dx = [velocity, acceleration] = [v,a]
+ # acceleration is the same dimension as velocity
cdx = casadi.SX.sym("dx", robot.model.nv * 2, 1)
+ # separate state for less cumbersome expressions (they're still the same casadi variables)
cq = cx[:robot.model.nq]
cv = cx[robot.model.nq:]
+ # acceleration is the same dimension as velocity
caq = casadi.SX.sym("a", robot.model.nv, 1)
cpin.forwardKinematics(cmodel, cdata, cq, cv, caq)
cpin.updateFramePlacements(cmodel, cdata)
- # cost function - reaching goal
- error_tool = casadi.Function(
- "etool6", [cx], [cpin.log6(cdata.oMf[endEffector_ID].inverse() * cpin.SE3(Mtarget)).vector],)
-
- # nonlinear dynamics constraint
+ # dynamics constraint
cnext = casadi.Function(
"next",
[cx, caq],
[
casadi.vertcat(
- cpin.integrate(cmodel, cx[:robot.model.nq], cx[robot.model.nq:] * args.ocp_dt + caq * args.ocp_dt**2),
- cx[nq:] + caq * args.ocp_dt,
+ # TODO: i'm not sure i need to have the acceleration update for the position
+ # because i am integrating the velocity as well.
+ # i guess this is the way to go because we can't first integrate
+ # the velocity and then the position, but have to do both simultaneously
+ cpin.integrate(cmodel, cq, cv * args.ocp_dt + caq * args.ocp_dt**2),
+ cv + caq * args.ocp_dt
)
],
)
- opti = casadi.Opti()
+
+ # cost function - reaching goal
+ error6_tool = casadi.Function(
+ "etool6",
+ [cx],
+ [cpin.log6(cdata.oMf[endEffector_ID].inverse() * cpin.SE3(Mtarget)).vector],)
+
+ # TODO: obstacles
+ # here you need to define other obstacles, namely the table (floor)
+ # it's obviously going to be a plane
+ # alternatively just forbid z-axis of end-effector to be negative
+ obstacles = []
# one set of decision variables per knot (both states and control input)
var_xs = [opti.variable(nx) for t in range(args.n_knots + 1)]
var_as = [opti.variable(nv) for t in range(args.n_knots)]
- # running costs - x**2 and u**@ - used more for regularization than anything else
+ # running costs - x**2 and u**2 - used more for regularization than anything else
totalcost = 0
- for t in range(T):
+ for t in range(args.n_knots):
# running
- totalcost += 1e-3 * DT * casadi.sumsqr(var_xs[t][nq:])
- totalcost += 1e-4 * DT * casadi.sumsqr(var_as[t])
+ totalcost += 1e-3 * args.ocp_dt * casadi.sumsqr(var_xs[t][nq:])
+ totalcost += 1e-4 * args.ocp_dt * casadi.sumsqr(var_as[t])
# terminal cost
- totalcost += 1e4 * casadi.sumsqr(error_tool(var_xs[T]))
-
+ totalcost += 1e4 * casadi.sumsqr(error6_tool(var_xs[T]))
+
+ # now we combine the components into the OCP
+ opti = casadi.Opti()
- # collision avoidance (hard) constraints
+ # initial state constraints
# TODO: idk if you need to input x0 in this way, maybe robot.getQ(d) is better?
opti.subject_to(var_xs[0][:nq] == x0[:robot.model.nq])
opti.subject_to(var_xs[0][nq:] == x0[robot.model.nq:])
- for t in range(T):
+ # dynamics constraint between every knot pair
+ for t in range(args.n_knots):
opti.subject_to(cnext(var_xs[t], var_as[t]) == var_xs[t + 1])
+
+ # obstacle avoidance (hard) constraints: no ellipse should intersect any of the obstacles
+ ellipses = getSelfCollisionObstacles(args, robot)
+ cpos = casadi.SX.sym("p", 3)
for ellipse in ellipses:
for obstacle in obstacles:
for x in var_xs:
@@ -137,6 +244,12 @@ def createCasadiIKObstacleAvoidanceOCP(args, robot : RobotManager, x0, goal: pin
# now that the ocp has been transcribed as nlp,
# solve it
+ opti.minimize(totalcost)
+ opti.minimize(totalcost)
+ p_opts = dict(print_time=False, verbose=False)
+ s_opts = dict(print_level=0)
+ opti.solver("ipopt") # set numerical backend
+ opti.set_initial(var_qs[0], q0)
try:
sol = opti.solve_limited()
#sol_q = opti.value(var_q)
@@ -147,3 +260,8 @@ def createCasadiIKObstacleAvoidanceOCP(args, robot : RobotManager, x0, goal: pin
#sol_q = opti.debug.value(var_q)
sol_xs = [opti.debug.value(var_x) for var_x in var_xs]
sol_as = [opti.debug.value(var_a) for var_a in var_as]
+ # TODO: extract qs and vs from xs, put in as
+ # TODO: write dynamic trajectory following
+ reference = {'qs' : sol_qs, 'vs' : sol_vs, 'dt' : args.ocp_dt}
+ return reference, opti
+ return sol_xs, sol_as
diff --git a/python/ur_simple_control/util/encapsulating_ellipses.py b/python/ur_simple_control/util/encapsulating_ellipses.py
index 3e20424..7515bfc 100644
--- a/python/ur_simple_control/util/encapsulating_ellipses.py
+++ b/python/ur_simple_control/util/encapsulating_ellipses.py
@@ -6,36 +6,46 @@ Used to construct ellipses around manipulator links to get convex self-collision
NOTE: probably cylinders are better but let's run with this.
decide:
- w in so3: ellipse orientation
- - r in r3: ellipse main dimensions
+ - r in R^3: ellipse main dimensions
minimizing:
- r1*r2*r3 the volum of the ellipse
+ r1*r2*r3 the volum of the ellipse -> possibly not really (can calculate but eh) but it leads to the same result
so that:
r>=0
- for all points pk in a list, pk in ellipse: (pk-c)@A@pk-c)<=1
+ for all points x in a list, x in ellipse: (x - c) @ A @ (x - c) <= 1
with A,c the matrix representation of the ellipsoid A=exp(w)@diag(1/r**2)@exp(w).T
-Once solved, this can be stored instead of recomputed all the time.
+Once solved, this is stored so it doesn't need to be recomputed every the time.
"""
+# BIG TODO: make gripper one big ellipse
+# going through every link is both wrong and pointlessly complex
+# also TODO: try fitting cyllinders instead
import casadi
import numpy as np
import pinocchio as pin
from pinocchio import casadi as cpin
from ur_simple_control.visualize.meshcat_viewer_wrapper.visualizer import MeshcatVisualizer
+from ur_simple_control.managers import RobotManager
from ur_simple_control.util.get_model import get_model, get_heron_model
from types import SimpleNamespace
import time
import pickle
from importlib.resources import files
-# plotting the ellipse in matplotlib for verification
-def plotEllipse(ax, opti_A, opti_c):
+
+import pinocchio as pin
+import numpy as np
+import time
+import argparse
+from functools import partial
+from ur_simple_control.managers import getMinimalArgParser, ControlLoopManager, RobotManager
+
+def plotEllipseMatplotlib(ax, opti_A, opti_c):
# find the rotation matrix and radii of the axes
U, s, rotation = np.linalg.svd(opti_A)
radii = 1.0 / np.sqrt(s)
- # now carry on with EOL's answer
u = np.linspace(0.0, 2.0 * np.pi, 100)
v = np.linspace(0.0, np.pi, 100)
x = radii[0] * np.outer(np.cos(u), np.sin(v))
@@ -47,9 +57,19 @@ def plotEllipse(ax, opti_A, opti_c):
np.dot([x[i, j], y[i, j], z[i, j]], rotation) + opti_c
)
- # plot
ax.plot_wireframe(x, y, z, rstride=4, cstride=4, color="b", alpha=0.2)
+# TODO: finish and verify
+# make a separate function for a single ellipse based on the namespace
+def visualizeEllipses(args, robot : RobotManager, ellipses):
+ e, P = np.linalg.eig()
+ ellipse_placement = pin.SE3(P, ellipse.center)
+ #ellipse_placement = data.oMf[geom.parentFrame].act(geom.placement.act(ellipse_placement))
+ ellipse_placement = data.oMf[geom.parentFrame].act(ellipse_placement)
+ # TODO: add to MeshcatVisualizerWrapper,
+ # add queue handling in visualize function
+ viz.addEllipsoid(f"el_{ellipse.name}", sol_r, [0.3, 0.9, 0.3, 0.3])
+ viz.applyConfiguration(f"el_{ellipse.name}", ellipse_placement)
# plotting the vertices in the ellipse in matplotlib for verification
def plotVertices(ax, vertices, nsubsample):
@@ -78,108 +98,173 @@ def plotVertices(ax, vertices, nsubsample):
bottom=plot_center[2] - plot_length, top=plot_center[2] + plot_length
)
-model, collision_model, visual_model, data = get_model()
-#viz = MeshcatVisualizer(model=model, collision_model=collision_model, visual_model=visual_model)
-#viz.display(np.zeros(model.nq))
+def visualizeVertices(args, robot : RobotManager):
-ellipses = []
-for geom in collision_model.geometryObjects:
- vertices = geom.geometry.vertices()
+ for i, geom in enumerate(robot.collision_model.geometryObjects):
+ vertices = geom.geometry.vertices()
+
+ for i in np.arange(0, vertices.shape[0]):
+ viz.addSphere(f"world/point_{i}", 5e-3, [1, 0, 0, 0.8])
+ vertix_pose = pin.SE3.Identity()
+ vertix_pose.translation = vertices[i]
+ #vertix_pose = data.oMi[geom.parentJoint].act(vertix_pose)
+ vertix_pose = data.oMf[geom.parentFrame].act(geom.placement.act(vertix_pose))
+ #viz.applyConfiguration(f"world/point_{i}", np.array(vertices[i].tolist() + [1, 0, 0, 0]))
+ viz.applyConfiguration(f"world/point_{i}", vertix_pose)
-# for i in np.arange(0, vertices.shape[0]):
-# viz.addSphere(f"world/point_{i}", 5e-3, [1, 0, 0, 0.8])
-# viz.applyConfiguration(f"world/point_{i}", vertices[i].tolist() + [1, 0, 0, 0])
- cw = casadi.SX.sym("w", 3)
- exp = casadi.Function("exp3", [cw], [cpin.exp3(cw)])
+# TODO: make this for every robot.
+def computeEncapsulatingEllipses(args, robot : RobotManager):
+ model, collision_model, visual_model, data = (robot.model, robot.collision_model, robot.visual_model, robot.data)
+ viz = MeshcatVisualizer(model=model, collision_model=collision_model, visual_model=visual_model)
+ #q = np.zeros(model.nq)
+ q = pin.randomConfiguration(model)
+ print(q)
+ viz.display(q)
+ pin.forwardKinematics(model, data, q)
+ pin.updateFramePlacements(model, data)
+ pin.updateGlobalPlacements(model,data)
+ pin.computeAllTerms(model,data,q,np.zeros(model.nv))
+ time.sleep(3)
+
+ ellipses = []
+ for i, geom in enumerate(collision_model.geometryObjects):
+ vertices = geom.geometry.vertices()
+
+ for i in np.arange(0, vertices.shape[0]):
+ viz.addSphere(f"world/point_{i}", 5e-3, [1, 0, 0, 0.8])
+ vertix_pose = pin.SE3.Identity()
+ vertix_pose.translation = vertices[i]
+ #vertix_pose = data.oMi[geom.parentJoint].act(vertix_pose)
+ vertix_pose = data.oMf[geom.parentFrame].act(geom.placement.act(vertix_pose))
+ #viz.applyConfiguration(f"world/point_{i}", np.array(vertices[i].tolist() + [1, 0, 0, 0]))
+ viz.applyConfiguration(f"world/point_{i}", vertix_pose)
+
+ cw = casadi.SX.sym("w", 3)
+ exp = casadi.Function("exp3", [cw], [cpin.exp3(cw)])
+
+ """
+ decide
+ - w in so3: ellipse orientation
+ - r in r3: ellipse main dimensions
+ minimizing:
+ r1*r2*r3 the volum of the ellipse
+ so that:
+ r>=0
+ for all points pk in a list, pk in ellipse
+
+ """
+ opti = casadi.Opti()
+ var_w = opti.variable(3)
+ var_r = opti.variable(3)
+ var_c = opti.variable(3)
+
+ # The ellipsoid matrix is represented by w=log3(R),diag(P) with R,P=eig(A)
+ R = exp(var_w)
+ A = R @ casadi.diag(1 / var_r**2) @ R.T
+
+ totalcost = var_r[0] * var_r[1] * var_r[2]
+
+ opti.subject_to(var_r >= 0)
+
+ for g_v in vertices:
+ # g_v is the vertex v expressed in the geometry frame.
+ # Convert point from geometry frame to joint frame
+ j_v = geom.placement.act(g_v)
+ # Constraint the ellipsoid to be including the point
+ opti.subject_to((j_v - var_c).T @ A @ (j_v - var_c) <= 1)
+
+ ### SOLVE
+ opti.minimize(totalcost)
+ opti.solver("ipopt") # set numerical backend
+ opti.set_initial(var_r, 10)
+
+ sol = opti.solve_limited()
+
+ sol_r = opti.value(var_r)
+ sol_A = opti.value(A)
+ sol_c = opti.value(var_c)
+ sol_R = opti.value(exp(var_w))
+
+ # TODO: add placement=pin.SE3(P, ellipse.center) and id=robot.model.getJointId(e.name)
+ ellipse = SimpleNamespace(
+ name=model.names[geom.parentJoint],
+ A=sol_A,
+ center=sol_c)
+ ellipses.append(ellipse)
+ e, P = np.linalg.eig(sol_A)
+ ellipse_placement = pin.SE3(P, ellipse.center)
+ #ellipse_placement = data.oMf[geom.parentFrame].act(geom.placement.act(ellipse_placement))
+ ellipse_placement = data.oMf[geom.parentFrame].act(ellipse_placement)
+ viz.addEllipsoid(f"el_{ellipse.name}", sol_r, [0.3, 0.9, 0.3, 0.3])
+ viz.applyConfiguration(f"el_{ellipse.name}", ellipse_placement)
+ print(ellipse)
+
+ ellipses_path = files('ur_simple_control.robot_descriptions.ellipses').joinpath("ur5e_robotiq_ellipses.pickle")
+ file = open(ellipses_path, 'wb')
+ pickle.dump(ellipses, file)
+ file.close()
+
+ # Recover r,R from A (for fun)
+ # e, P = np.linalg.eig(sol_A)
+ # recons_r = 1 / e**0.5
+ # recons_R = P
+ #
+ # # Build the ellipsoid 3d shape
+ # # Ellipsoid in meshcat
+ # viz.addEllipsoid("el", sol_r, [0.3, 0.9, 0.3, 0.3])
+ # # jMel is the placement of the ellipsoid in the joint frame
+ # jMel = pin.SE3(sol_R, sol_c)
+ #
+ # # Place the body, the vertices and the ellispod at a random configuration oMj_rand
+ # oMj_rand = pin.SE3.Random()
+ # viz.applyConfiguration(viz.getViewerNodeName(geom, pin.VISUAL), oMj_rand)
+ # for i in np.arange(0, vertices.shape[0]):
+ # viz.applyConfiguration(
+ # f"world/point_{i}", oMj_rand.act(vertices[i]).tolist() + [1, 0, 0, 0]
+ # )
+ # viz.applyConfiguration("el", oMj_rand * jMel)
+ #
+ # print(
+ # f'SimpleNamespace(name="{model.names[geom.parentJoint]}",\n'
+ # + f" A=np.{repr(sol_A)},\n"
+ # + f" center=np.{repr(sol_c)})"
+ # )
+ # time.sleep(5)
+ # Matplotlib (for fun)
+ #import matplotlib.pyplot as plt
+ #
+ #plt.ion()
+ #from utils.plot_ellipse import plotEllipse, plotVertices
+
+ #fig, ax = plt.subplots(1, subplot_kw={"projection": "3d"})
+ #plotEllipse(ax, sol_A, sol_c)
+ #plotVertices(ax, np.vstack([geom.placement.act(p) for p in vertices]), 1)
- """
- decide
- - w in so3: ellipse orientation
- - r in r3: ellipse main dimensions
- minimizing:
- r1*r2*r3 the volum of the ellipse
- so that:
- r>=0
- for all points pk in a list, pk in ellipse
- """
- opti = casadi.Opti()
- var_w = opti.variable(3)
- var_r = opti.variable(3)
- var_c = opti.variable(3)
-
- # The ellipsoid matrix is represented by w=log3(R),diag(P) with R,P=eig(A)
- R = exp(var_w)
- A = R @ casadi.diag(1 / var_r**2) @ R.T
-
- totalcost = var_r[0] * var_r[1] * var_r[2]
-
- opti.subject_to(var_r >= 0)
-
- for g_v in vertices:
- # g_v is the vertex v expressed in the geometry frame.
- # Convert point from geometry frame to joint frame
- j_v = geom.placement.act(g_v)
- # Constraint the ellipsoid to be including the point
- opti.subject_to((j_v - var_c).T @ A @ (j_v - var_c) <= 1)
-
- ### SOLVE
- opti.minimize(totalcost)
- opti.solver("ipopt") # set numerical backend
- opti.set_initial(var_r, 10)
-
- sol = opti.solve_limited()
-
- sol_r = opti.value(var_r)
- sol_A = opti.value(A)
- sol_c = opti.value(var_c)
- sol_R = opti.value(exp(var_w))
-
- ellipse = SimpleNamespace(
- name=model.names[geom.parentJoint],
- A=sol_A,
- center=sol_c)
- ellipses.append(ellipse)
- print(ellipse)
-
-ellipses_path = files('ur_simple_control.robot_descriptions.ellipses').joinpath("ur5e_robotiq_ellipses.pickle")
-file = open(ellipses_path, 'wb')
-pickle.dump(ellipses, file)
-file.close()
-
- # Recover r,R from A (for fun)
-# e, P = np.linalg.eig(sol_A)
-# recons_r = 1 / e**0.5
-# recons_R = P
-#
-# # Build the ellipsoid 3d shape
-# # Ellipsoid in meshcat
-# viz.addEllipsoid("el", sol_r, [0.3, 0.9, 0.3, 0.3])
-# # jMel is the placement of the ellipsoid in the joint frame
-# jMel = pin.SE3(sol_R, sol_c)
-#
-# # Place the body, the vertices and the ellispod at a random configuration oMj_rand
-# oMj_rand = pin.SE3.Random()
-# viz.applyConfiguration(viz.getViewerNodeName(geom, pin.VISUAL), oMj_rand)
-# for i in np.arange(0, vertices.shape[0]):
-# viz.applyConfiguration(
-# f"world/point_{i}", oMj_rand.act(vertices[i]).tolist() + [1, 0, 0, 0]
-# )
-# viz.applyConfiguration("el", oMj_rand * jMel)
-#
-# print(
-# f'SimpleNamespace(name="{model.names[geom.parentJoint]}",\n'
-# + f" A=np.{repr(sol_A)},\n"
-# + f" center=np.{repr(sol_c)})"
-# )
-# time.sleep(5)
- # Matplotlib (for fun)
- #import matplotlib.pyplot as plt
- #
- #plt.ion()
- #from utils.plot_ellipse import plotEllipse, plotVertices
+def get_args():
+ parser = getMinimalArgParser()
+ parser.description = 'describe this example'
+ # add more arguments here from different Simple Manipulator Control modules
+ args = parser.parse_args()
+ return args
+
+if __name__ == "__main__":
+ args = get_args()
+ robot = RobotManager(args)
+
+ computeEncapsulatingEllipses(args,robot)
+
+ # get expected behaviour here (library can't know what the end is - you have to do this here)
+ if not args.pinocchio_only:
+ robot.stopRobot()
+
+ if args.save_log:
+ robot.log_manager.plotAllControlLoops()
- #fig, ax = plt.subplots(1, subplot_kw={"projection": "3d"})
- #plotEllipse(ax, sol_A, sol_c)
- #plotVertices(ax, np.vstack([geom.placement.act(p) for p in vertices]), 1)
+ if args.visualize_manipulator:
+ robot.killManipulatorVisualizer()
+
+ if args.save_log:
+ robot.log_manager.saveLog()
+ #loop_manager.stopHandler(None, None)
diff --git a/python/ur_simple_control/vision/vision.py b/python/ur_simple_control/vision/vision.py
new file mode 100644
index 0000000..d2a0c20
--- /dev/null
+++ b/python/ur_simple_control/vision/vision.py
@@ -0,0 +1,32 @@
+import cv2
+from multiprocessing import Queue
+import numpy as np
+
+def processCamera(device, args, cmd, queue : Queue):
+ camera = cv2.VideoCapture(0)
+ # if you have ip_webcam set-up you can do this (but you have put in the correct ip)
+ #camera = cv2.VideoCapture("http://192.168.219.153:8080/video")
+
+ try:
+ while True:
+ (grabbed, frame) = camera.read()
+ # frame = cv2.flip(frame, 1)
+ scale_percent = 100 # percent of original size
+ width = int(frame.shape[1] * scale_percent / 100)
+ height = int(frame.shape[0] * scale_percent / 100)
+ dim = (width, height)
+
+ # resize image
+ frame = cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)
+ # frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+ cv2.imshow("camera", frame)
+ keypress = cv2.waitKey(1) & 0xFF
+
+ if keypress == ord("q"):
+ break
+
+ queue.put_nowait({"x" : np.random.randint(0, 10),
+ "y" : np.random.randint(0, 10)})
+ except KeyboardInterrupt:
+ if args.debug_prints:
+ print("PROCESS_CAMERA: caught KeyboardInterrupt, i'm out")
diff --git a/python/ur_simple_control/visualize/__pycache__/visualize.cpython-312.pyc b/python/ur_simple_control/visualize/__pycache__/visualize.cpython-312.pyc
index a5a1f53946b06f0ead307dfed8f5505c1e54eb9a..da6b4e16226e0ccf7592148fce5af4781ca85e75 100644
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diff --git a/python/ur_simple_control/visualize/visualize.py b/python/ur_simple_control/visualize/visualize.py
index 4cf8d13..f39cf81 100644
--- a/python/ur_simple_control/visualize/visualize.py
+++ b/python/ur_simple_control/visualize/visualize.py
@@ -64,7 +64,7 @@ def plotFromDict(plot_data, final_iteration, args, title="title"):
# STUPID MATPLOTLIB CAN'T HANDLE MULTIPLE FIGURES FROM DIFFERENT PROCESS
# LITERALLY REFUSES TO GET ME A FIGURE
-def realTimePlotter(args, queue):
+def realTimePlotter(args, log_item, queue):
"""
realTimePlotter
---------------
@@ -73,22 +73,25 @@ def realTimePlotter(args, queue):
"""
if args.debug_prints:
print("REAL_TIME_PLOTTER: i got this queue:", queue)
+ # NOTE: CAN'T PLOT ANYTHING BEFORE, THIS HANGS IF YOU HAD A PLOT BEFORE
plt.ion()
fig = plt.figure()
canvas = fig.canvas
- queue.put("success")
+# if args.debug_prints:
+# print("REAL_TIME_PLOTTER: putting success into queue")
+ #queue.put("success")
logs_deque = {}
logs_ndarrays = {}
AxisAndArtists = namedtuple("AxAndArtists", "ax artists")
axes_and_updating_artists = {}
- if args.debug_prints:
- print("REAL_TIME_PLOTTER: i am waiting for the first log_item to initialize myself")
- log_item = queue.get()
+ #if args.debug_prints:
+ # print("REAL_TIME_PLOTTER: i am waiting for the first log_item to initialize myself")
+ #log_item = queue.get()
if len(log_item) == 0:
print("you've send me nothing, so no real-time plotting for you")
return
- if args.debug_prints:
- print("REAL_TIME_PLOTTER: got log_item, i am initializing the desired plots")
+ #if args.debug_prints:
+ # print("REAL_TIME_PLOTTER: got log_item, i am initializing the desired plots")
ROLLING_BUFFER_SIZE = 100
t = np.arange(ROLLING_BUFFER_SIZE)
@@ -162,7 +165,7 @@ def realTimePlotter(args, queue):
plt.close(fig)
-def manipulatorVisualizer(args, model, collision_model, visual_model, queue):
+def manipulatorVisualizer(model, collision_model, visual_model, args, cmd, queue):
viz = MeshcatVisualizer(model=model, collision_model=collision_model, visual_model=visual_model)
viz.loadViewerModel()
# set shapes we know we'll use
--
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