From 89f42257b069201eb70b7ff26cf8b24fda050adf Mon Sep 17 00:00:00 2001
From: m-guberina <gubi.guberina@gmail.com>
Date: Fri, 3 Nov 2023 12:43:15 +0100
Subject: [PATCH] dmp stuff is happening
---
.gitignore | 1 +
README.md | 2 +
dmp/matlab/dmp_from_ogs/README.md | 33 +
dmp/matlab/dmp_from_ogs/code/dcp.m | 592 +
dmp/matlab/dmp_from_ogs/code/dmp_learn.m | 1 +
dmp/matlab/dmp_from_ogs/code/rcp.m | 396 +
dmp/matlab/dmp_from_ogs/code/rmp_learn.m | 1 +
dmp/matlab/dmp_from_ogs/code/transformation.m | 81 +
dmp/motion_planning/dmp/.dmp.py.swp | Bin 0 -> 16384 bytes
.../__pycache__/gmr_oa.cpython-310.pyc | Bin 0 -> 11623 bytes
dmp/my_sol/.run_dmp.py.swp | Bin 0 -> 12288 bytes
dmp/my_sol/.test_traj_w_speedj.py.swp | Bin 0 -> 12288 bytes
.../__pycache__/create_dmp.cpython-310.pyc | Bin 0 -> 4349 bytes
.../robotiq_gripper.cpython-310.pyc | Bin 0 -> 12229 bytes
dmp/my_sol/create_dmp.py | 147 +
dmp/my_sol/generate_traj_from_screen.py | 10 +
dmp/my_sol/robotiq_gripper.py | 291 +
dmp/my_sol/run_dmp.py | 117 +
dmp/my_sol/test_traj_w_movej.py | 35 +
dmp/my_sol/test_traj_w_speedj.py | 42 +
dmp/my_sol/ur10_omega_trajectory.csv | 10001 ++++++++++++++++
dmp/notes.md | 59 +
dmp/pdfs/neco_a_00393.pdf | Bin 0 -> 2628948 bytes
dmp/traj_scaling_node | 113 -
dmp/vel_ff_pos_fb_node | 105 +
docs/scriptManual-3.5.4.pdf | Bin 0 -> 457642 bytes
util/freedrive.py | 22 +
27 files changed, 11936 insertions(+), 113 deletions(-)
create mode 100644 .gitignore
create mode 100644 dmp/matlab/dmp_from_ogs/README.md
create mode 100644 dmp/matlab/dmp_from_ogs/code/dcp.m
create mode 100644 dmp/matlab/dmp_from_ogs/code/dmp_learn.m
create mode 100644 dmp/matlab/dmp_from_ogs/code/rcp.m
create mode 100644 dmp/matlab/dmp_from_ogs/code/rmp_learn.m
create mode 100644 dmp/matlab/dmp_from_ogs/code/transformation.m
create mode 100644 dmp/motion_planning/dmp/.dmp.py.swp
create mode 100644 dmp/motion_planning/online_traj_scaling/__pycache__/gmr_oa.cpython-310.pyc
create mode 100644 dmp/my_sol/.run_dmp.py.swp
create mode 100644 dmp/my_sol/.test_traj_w_speedj.py.swp
create mode 100644 dmp/my_sol/__pycache__/create_dmp.cpython-310.pyc
create mode 100644 dmp/my_sol/__pycache__/robotiq_gripper.cpython-310.pyc
create mode 100644 dmp/my_sol/create_dmp.py
create mode 100644 dmp/my_sol/generate_traj_from_screen.py
create mode 100644 dmp/my_sol/robotiq_gripper.py
create mode 100644 dmp/my_sol/run_dmp.py
create mode 100644 dmp/my_sol/test_traj_w_movej.py
create mode 100644 dmp/my_sol/test_traj_w_speedj.py
create mode 100644 dmp/my_sol/ur10_omega_trajectory.csv
create mode 100644 dmp/notes.md
create mode 100644 dmp/pdfs/neco_a_00393.pdf
delete mode 100755 dmp/traj_scaling_node
create mode 100755 dmp/vel_ff_pos_fb_node
create mode 100644 docs/scriptManual-3.5.4.pdf
create mode 100644 util/freedrive.py
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..0471fce
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+clik/build
diff --git a/README.md b/README.md
index 697b748..5da62e2 100644
--- a/README.md
+++ b/README.md
@@ -54,3 +54,5 @@ Here is a checklist of what to do together with the key remarks.
and see whether the code works. the simulator accepts the same commands as the robot.
the only TODO is to add the gripper to the simulator somehow, but you can also just
skip using it when simulating.
+
+TODO:: explain hom transf mat
diff --git a/dmp/matlab/dmp_from_ogs/README.md b/dmp/matlab/dmp_from_ogs/README.md
new file mode 100644
index 0000000..f3c4daa
--- /dev/null
+++ b/dmp/matlab/dmp_from_ogs/README.md
@@ -0,0 +1,33 @@
+
+# Matlab Code for Dynamic Movement Primitives
+
+## Overview
+
+**Authors:** Stefan Schaal, Auke Ijspeert, and Heiko Hoffmann
+
+**Keywords:** dynamic movement primitives
+
+This code has been tested under **Matlab2019a**.
+
+This is research code, expect that it changes often and any fitness for a particular purpose is disclaimed.
+
+### Publications
+
+For more details on type-1 fuzzy logic controllers, please refer to:
+
+* Ijspeert A, Nakanishi J, Schaal S, "Learning attractor landscapes for learning motor primitives," in *Advances in Neural Information Processing Systems 15*, MIT Press, 2003. ([pdf](http://www-clmc.usc.edu/publications/I/ijspeert-NIPS2002.pdf)).
+
+## Usage
+
+For learning discrete movement primitives (DMPs) run `dmp_learn.m`.
+
+For learning rhythmic movement primitives (RMPs) run `rmp_learn.m`.
+
+## Licence
+
+The source code is released under [GPLv3](http://www.gnu.org/licenses/) license.
+
+## Notes
+
+ - This code is written for research purpose and has not been fully optimized. In the future I will add more functionalities and improve efficiency, and also add more comment.
+ - Please report bugs and request features using the [Issue Tracker](https://github.com/andriyukr/controllers/issues).
diff --git a/dmp/matlab/dmp_from_ogs/code/dcp.m b/dmp/matlab/dmp_from_ogs/code/dcp.m
new file mode 100644
index 0000000..36248f5
--- /dev/null
+++ b/dmp/matlab/dmp_from_ogs/code/dcp.m
@@ -0,0 +1,592 @@
+function [varargout] = dcp(action,varargin)
+% A discrete movement primitive (DCP) inspired by
+% Ijspeert A, Nakanishi J, Schaal S (2003) Learning attractor landscapes for
+% learning motor primitives. In: Becker S, Thrun S, Obermayer K (eds) Advances
+% in Neural Information Processing Systems 15. MIT Press, Cambridge, MA.
+% http://www-clmc.usc.edu/publications/I/ijspeert-NIPS2002.pdf. This
+% version adds several new features, including that the primitive is
+% formulated as acceleration, and that the canonical system is normalized.
+% Additinally, a new scale parameter for the nonlinear function allows a larger
+% spectrum of modeling options with the primitives
+%
+% Copyright July, 2007 by
+% Stefan Schaal, Auke Ijspeert, and Heiko Hoffmann
+%
+% --------------- Different Actions of the program ------------------------
+%
+% Initialize a DCP model:
+% FORMAT dcp('Init',ID,n_rfs,name,flag)
+% ID : desired ID of model
+% n_rfs : number of local linear models
+% name : a name for the model
+% flag : flag=1 use 2nd order canonical system, flag=0 use 1st order
+%
+% alternatively, the function is called as
+%
+% FORMAT dcp('Init',ID,d,)
+% d : a complete data structure of a dcp model
+%
+% returns nothing
+% -------------------------------------------------------------------------
+%
+% Set the goal state:
+% FORMAT dcp('Set_Goal',ID,g,flag)
+% ID : ID of model
+% g : the new goal
+% flag : flag=1: update x0 with current state, flag=0: don't update x0
+%
+% returns nothing
+% -------------------------------------------------------------------------
+%
+% Set the scale factor of the movement:
+% FORMAT dcp('Set_Scale',ID,s,flag)
+% ID : ID of model
+% s : the new scale
+%
+% returns nothing
+% -------------------------------------------------------------------------
+%
+% Run the dcps:
+% FORMAT [y,yd,ydd]=dcp('Run',ID,tau,dt,ct,cc)
+% ID : ID of model
+% tau : global time constant to scale speed of system
+% dt : integration time step
+% ct : coupling term for transformation system (optional)
+% cc : coupling term for canonical system (optional)
+% ct_tau : coupling term for transformation system's time constant (optional)
+% cc_tau : coupling term for canonical system's time constant (optional)
+% cw : additive coupling term for parameters (optional)
+%
+% returns y,yd,ydd, i.e., current pos,vel,acc, of the dcp
+% -------------------------------------------------------------------------
+%
+% Change values of a dcp:
+% FORMAT dcp('Change',ID,pname,value)
+% ID : ID of model
+% pname : parameter name
+% value : value to be assigned to parameter
+%
+% returns nothing
+% -------------------------------------------------------------------------
+%
+% Run the dcps:
+% FORMAT dcp('Run',ID,tau,dt,t,td)
+% ID : ID of model
+% tau : time constant to scale speed, tau is roughly movement
+% time until convergence
+% dt : integration time step
+%
+% returns y,yd,ydd, i.e., current pos,vel,acc, of the dcp
+% -------------------------------------------------------------------------
+%
+% Run the dcp and update the weights:
+% FORMAT dcp('Run_Fit',ID,tau,dt,t,td,tdd)
+% ID : ID of model
+% tau : time constant to scale speed, tau is roughly movement
+% time until convergence
+% dt : integration time step
+% t : target for y
+% td : target for yd
+% tdd : target for ydd
+%
+% returns y,yd,ydd, i.e., current pos,vel,acc, of the dcp
+% -------------------------------------------------------------------------
+%
+% Fit the dcp to a complete trajectory in batch mode:
+% FORMAT dcp('Batch_Fit',ID,tau,dt,T,Td,Tdd)
+% ID : ID of model
+% tau : time constant to scale speed, tau is roughly movement
+% time until convergence the goal
+% dt : somple time step in given trajectory
+% T : target trajectory for y
+% Td : target trajectory for yd (optional, will be generated
+% as dT/dt otherwise
+% Tdd : target trajectory for ydd (optional, will be generated
+% as dTd/dt otherwise
+%
+% returns y,yd,ydd, i.e., current pos,vel,acc, of the dcp
+% -------------------------------------------------------------------------
+%
+% Return the data structure of a dcp model
+% FORMAT [d] = dcp('Structure',ID)
+% ID : desired ID of model
+%
+% returns the complete data structure of a dcp model, e.g., for saving or
+% inspecting it
+% -------------------------------------------------------------------------
+%
+% Reset the states of a dcp model to zero (or a given state)
+% FORMAT [d] = dcp('Reset_State',ID)
+% ID : desired ID of model
+% y : the state to which the primitive is set (optional)
+%
+% returns nothing
+% -------------------------------------------------------------------------
+%
+% Clear the data structure of a LWPR model
+% FORMAT dcp('Clear',ID)
+% ID : ID of model
+%
+% returns nothing
+% -------------------------------------------------------------------------
+%
+% Initializes the dcp with a minimum jerk trajectory
+% FORMAT dcp('MinJerk',ID)
+% ID : ID of model
+%
+% returns nothing
+% -------------------------------------------------------------------------
+
+% the global structure to store all dcps
+global dcps;
+global min_y;
+global max_y;
+
+% at least two arguments are needed
+if nargin < 2,
+ error('Incorrect call to dcp');
+end
+
+switch lower(action),
+% .........................................................................
+ case 'init'
+ if (nargin == 3)
+ ID = varargin{1};
+ dcps(ID) = varargin{2};
+ else
+ % this initialization is good for 0.5 seconds movement for tau=0.5
+ ID = varargin{1};
+ n_rfs = varargin{2};
+ dcps(ID).name = varargin{3};
+ dcps(ID).c_order = 0;
+ if (nargin == 5)
+ dcps(ID).c_order = varargin{4};
+ end
+ % the time constants for chosen for critical damping
+ dcps(ID).alpha_z = 25;
+ dcps(ID).beta_z = dcps(ID).alpha_z/4;
+ dcps(ID).alpha_g = dcps(ID).alpha_z/2;
+ dcps(ID).alpha_x = dcps(ID).alpha_z/3;
+ dcps(ID).alpha_v = dcps(ID).alpha_z;
+ dcps(ID).beta_v = dcps(ID).beta_z;
+ % initialize the state variables
+ dcps(ID).z = 0;
+ dcps(ID).y = 0;
+ dcps(ID).x = 0;
+ dcps(ID).v = 0;
+ dcps(ID).zd = 0;
+ dcps(ID).yd = 0;
+ dcps(ID).xd = 0;
+ dcps(ID).vd = 0;
+ dcps(ID).ydd = 0;
+ % the current goal state
+ dcps(ID).g = 0;
+ dcps(ID).gd = 0;
+ dcps(ID).G = 0;
+ % the current start state of the primitive
+ dcps(ID).y0 = 0;
+ % the orginal amplitude (max(y)-min(y)) when the primitive was fit
+ dcps(ID).A = 0;
+ % the original goal amplitude (G-y0) when the primitive was fit
+ dcps(ID).dG = 0;
+ % the scale factor for the nonlinear function
+ dcps(ID).s = 1;
+
+ t = (0:1/(n_rfs-1):1)'*0.5;
+ if (dcps(ID).c_order == 1)
+ % the local models, spaced on a grid in time by applying the
+ % anaytical solutions x(t) = 1-(1+alpha/2*t)*exp(-alpha/2*t)
+ dcps(ID).c = (1+dcps(ID).alpha_z/2*t).*exp(-dcps(ID).alpha_z/2*t);
+ % we also store the phase velocity at the centers which is used by some
+ % applications: xd(t) = (-alpha/2)*x(t) + alpha/2*exp(-alpha/2*t)
+ dcps(ID).cd = dcps(ID).c*(-dcps(ID).alpha_z/2) + dcps(ID).alpha_z/2*exp(-dcps(ID).alpha_z/2*t);
+ else
+ % the local models, spaced on a grid in time by applying the
+ % anaytical solutions x(t) = exp(-alpha*t)
+ dcps(ID).c = exp(-dcps(ID).alpha_x*t);
+ % we also store the phase velocity at the centers which is used by some
+ % applications: xd(t) = x(t)*(-dcps(ID).alpha_x);
+ dcps(ID).cd = dcps(ID).c*(-dcps(ID).alpha_x);
+ end
+
+ dcps(ID).psi = zeros(n_rfs,1);
+ dcps(ID).w = zeros(n_rfs,1);
+ dcps(ID).sx2 = zeros(n_rfs,1);
+ dcps(ID).sxtd = zeros(n_rfs,1);
+ dcps(ID).D = (diff(dcps(ID).c)*0.55).^2;
+ dcps(ID).D = 1./[dcps(ID).D;dcps(ID).D(end)];
+ dcps(ID).lambda = 1;
+
+ end
+
+% .........................................................................
+ case 'reset_state'
+ ID = varargin{1};
+ if nargin > 2,
+ y = varargin{2};
+ else
+ y = 0;
+ end
+ % initialize the state variables
+ dcps(ID).z = 0;
+ dcps(ID).y = y;
+ dcps(ID).x = 0;
+ dcps(ID).v = 0;
+ dcps(ID).zd = 0;
+ dcps(ID).yd = 0;
+ dcps(ID).xd = 0;
+ dcps(ID).vd = 0;
+ dcps(ID).ydd = 0;
+ % the goal state
+ dcps(ID).G = y;
+ dcps(ID).g = y;
+ dcps(ID).gd = 0;
+ dcps(ID).y0 = y;
+ dcps(ID).s = 1;
+
+% .........................................................................
+ case 'set_goal'
+ ID = varargin{1};
+ dcps(ID).G = varargin{2};
+ if (dcps(ID).c_order == 0)
+ dcps(ID).g = dcps(ID).G;
+ end
+ flag = varargin{3};
+ if (flag),
+ dcps(ID).x = 1;
+ dcps(ID).y0 = dcps(ID).y;
+ end
+ if (dcps(ID).A ~= 0) % check whether dcp has been fit
+ if (dcps(ID).A/(abs(dcps(ID).dG)+1.e-10) > 2.0)
+ % amplitude-based scaling needs to be set explicity
+ else
+ % dG based scaling cab work automatically
+ dcps(ID).s = (dcps(ID).G-dcps(ID).y0)/dcps(ID).dG;
+ end
+ end
+
+% .........................................................................
+ case 'set_scale'
+ ID = varargin{1};
+ dcps(ID).s = varargin{2};
+
+% .........................................................................
+ case 'run'
+ ID = varargin{1};
+ tau = 0.5/varargin{2}; % tau is relative to 0.5 seconds nominal movement time
+ dt = varargin{3};
+
+ if nargin > 4,
+ ct = varargin{4};
+ else
+ ct = 0;
+ end
+
+ if nargin > 5,
+ cc = varargin{5};
+ else
+ cc = 0;
+ end
+
+ if nargin > 6,
+ ct_tau = varargin{6};
+ else
+ ct_tau = 1;
+ end
+
+ if nargin > 7,
+ cc_tau = varargin{7};
+ else
+ cc_tau = 1;
+ end
+
+ if nargin > 8,
+ cw = varargin{8};
+ else
+ cw = 0;
+ end
+
+ % the weighted sum of the locally weighted regression models
+ dcps(ID).psi = exp(-0.5*((dcps(ID).x-dcps(ID).c).^2).*dcps(ID).D);
+ amp = dcps(ID).s;
+ if (dcps(ID).c_order == 1)
+ in = dcps(ID).v;
+ else
+ in = dcps(ID).x;
+ end
+ f = sum(in*(dcps(ID).w+cw).*dcps(ID).psi)/sum(dcps(ID).psi+1.e-10) * amp;
+
+ if (dcps(ID).c_order == 1),
+ dcps(ID).vd = (dcps(ID).alpha_v*(dcps(ID).beta_v*(0-dcps(ID).x)-dcps(ID).v)+cc)*tau*cc_tau;
+ dcps(ID).xd = dcps(ID).v*tau*cc_tau;
+ else
+ dcps(ID).vd = 0;
+ dcps(ID).xd = (dcps(ID).alpha_x*(0-dcps(ID).x)+cc)*tau*cc_tau;
+ end
+
+ dcps(ID).zd = (dcps(ID).alpha_z*(dcps(ID).beta_z*(dcps(ID).g-dcps(ID).y)-dcps(ID).z)+f+ct)*tau*ct_tau;
+ dcps(ID).yd = dcps(ID).z*tau*ct_tau;
+ dcps(ID).ydd= dcps(ID).zd*tau*ct_tau;
+
+ dcps(ID).gd = dcps(ID).alpha_g*(dcps(ID).G-dcps(ID).g);
+
+ dcps(ID).x = dcps(ID).xd*dt+dcps(ID).x;
+ dcps(ID).v = dcps(ID).vd*dt+dcps(ID).v;
+
+
+ dcps(ID).z = dcps(ID).zd*dt+dcps(ID).z;
+ dcps(ID).y = dcps(ID).yd*dt+dcps(ID).y;
+
+ dcps(ID).g = dcps(ID).gd*dt+dcps(ID).g;
+
+
+ varargout(1) = {dcps(ID).y};
+ varargout(2) = {dcps(ID).yd};
+ varargout(3) = {dcps(ID).ydd};
+ varargout(4) = {dcps(ID).psi*in/sum(dcps(ID).psi+1.e-10) * amp};
+
+% .........................................................................
+ case 'change'
+ ID = varargin{1};
+ command = sprintf('dcps(%d).%s = varargin{3};',ID,varargin{2});
+ eval(command);
+
+% .........................................................................
+ case 'run_fit'
+ ID = varargin{1};
+ tau = 0.5/varargin{2}; % tau is relative to 0.5 seconds nominal movement time
+ dt = varargin{3};
+ t = varargin{4};
+ td = varargin{5};
+ tdd = varargin{6};
+
+ % check whether this is the first time the primitive is fit, and record the
+ % amplitude and dG information
+ if (dcps(ID).A == 0)
+ dcps(ID).dG = dcps(ID).G - dcps(ID).y0;
+ if (dcps(ID).x == 1),
+ min_y = +1.e10;
+ max_y = -1.e10;
+ dcps(ID).s = 1;
+ end
+ end
+
+ % the regression target
+ amp = dcps(ID).s;
+ ft = (tdd/tau^2-dcps(ID).alpha_z*(dcps(ID).beta_z*(dcps(ID).g-t)-td/tau))/amp;
+
+ % the weighted sum of the locally weighted regression models
+ dcps(ID).psi = exp(-0.5*((dcps(ID).x-dcps(ID).c).^2).*dcps(ID).D);
+
+ % update the regression
+ if (dcps(ID).c_order == 1),
+ dcps(ID).sx2 = dcps(ID).sx2*dcps(ID).lambda + dcps(ID).psi*dcps(ID).v^2;
+ dcps(ID).sxtd = dcps(ID).sxtd*dcps(ID).lambda + dcps(ID).psi*dcps(ID).v*ft;
+ dcps(ID).w = dcps(ID).sxtd./(dcps(ID).sx2+1.e-10);
+ else
+ dcps(ID).sx2 = dcps(ID).sx2*dcps(ID).lambda + dcps(ID).psi*dcps(ID).x^2;
+ dcps(ID).sxtd = dcps(ID).sxtd*dcps(ID).lambda + dcps(ID).psi*dcps(ID).x*ft;
+ dcps(ID).w = dcps(ID).sxtd./(dcps(ID).sx2+1.e-10);
+ end
+
+ % compute nonlinearity
+ if (dcps(ID).c_order == 1)
+ in = dcps(ID).v;
+ else
+ in = dcps(ID).x;
+ end
+ f = sum(in*dcps(ID).w.*dcps(ID).psi)/sum(dcps(ID).psi+1.e-10) * amp;
+
+ % integrate
+ if (dcps(ID).c_order == 1),
+ dcps(ID).vd = (dcps(ID).alpha_v*(dcps(ID).beta_v*(0-dcps(ID).x)-dcps(ID).v))*tau;
+ dcps(ID).xd = dcps(ID).v*tau;
+ else
+ dcps(ID).vd = 0;
+ dcps(ID).xd = dcps(ID).alpha_x*(0-dcps(ID).x)*tau;
+ end
+
+ % note that yd = td = z*tau ==> z=td/tau; the first equation means
+ % simply dcps(ID).zd = tdd
+ dcps(ID).zd = (dcps(ID).alpha_z*(dcps(ID).beta_z*(dcps(ID).g-dcps(ID).y)-dcps(ID).z)+f)*tau;
+ dcps(ID).yd = dcps(ID).z*tau;
+ dcps(ID).ydd= dcps(ID).zd*tau;
+
+ dcps(ID).gd = dcps(ID).alpha_g*(dcps(ID).G-dcps(ID).g);
+
+ dcps(ID).x = dcps(ID).xd*dt+dcps(ID).x;
+ dcps(ID).v = dcps(ID).vd*dt+dcps(ID).v;
+
+ dcps(ID).z = dcps(ID).zd*dt+dcps(ID).z;
+ dcps(ID).y = dcps(ID).yd*dt+dcps(ID).y;
+
+ dcps(ID).g = dcps(ID).gd*dt+dcps(ID).g;
+
+ varargout(1) = {dcps(ID).y};
+ varargout(2) = {dcps(ID).yd};
+ varargout(3) = {dcps(ID).ydd};
+
+ if (dcps(ID).A == 0)
+ max_y = max(max_y,dcps(ID).y);
+ min_y = min(min_y,dcps(ID).y);
+ if (dcps(ID).x < 0.0001)
+ dcps(ID).A = max_y - min_y;
+ end
+ end
+
+% .........................................................................
+ case 'batch_fit'
+
+ ID = varargin{1};
+ tau = 0.5/varargin{2}; % tau is relative to 0.5 seconds nominal movement time
+ dt = varargin{3};
+ T = varargin{4};
+ if (nargin > 5)
+ Td = varargin{5};
+ else
+ Td = diffnc(T,dt);
+ end
+ if (nargin > 6)
+ Tdd = varargin{6};
+ else
+ Tdd = diffnc(Td,dt);
+ end
+
+ % the start state is the first state in the trajectory
+ y0 = T(1);
+ g = y0;
+
+ % the goal is the last state in the trajectory
+ goal = T(end);
+ if (dcps(ID).c_order == 0)
+ g = goal;
+ end
+
+ % the amplitude is the max(T)-min(T)
+ A = max(T)-min(T);
+
+ % compute the hidden states
+ X = zeros(size(T));
+ V = zeros(size(T));
+ G = zeros(size(T));
+ x = 1;
+ v = 0;
+
+ for i=1:length(T),
+
+ X(i) = x;
+ V(i) = v;
+ G(i) = g;
+
+ if (dcps(ID).c_order == 1)
+ vd = dcps(ID).alpha_v*(dcps(ID).beta_v*(0-x)-v)*tau;
+ xd = v*tau;
+ else
+ vd = 0;
+ xd = dcps(ID).alpha_x*(0-x)*tau;
+ end
+ gd = (goal - g) * dcps(ID).alpha_g;
+
+ x = xd*dt+x;
+ v = vd*dt+v;
+ g = gd*dt+g;
+
+ end
+
+ % the regression target
+ dcps(ID).dG = goal - y0;
+ dcps(ID).A = max(T)-min(T);
+ dcps(ID).s = 1; % for fitting a new primitive, the scale factor is always equal to one
+
+ amp = dcps(ID).s;
+ Ft = (Tdd/tau^2-dcps(ID).alpha_z*(dcps(ID).beta_z*(G-T)-Td/tau)) / amp;
+
+ % compute the weights for each local model along the trajectory
+ PSI = exp(-0.5*((X*ones(1,length(dcps(ID).c))-ones(length(T),1)*dcps(ID).c').^2).*(ones(length(T),1)*dcps(ID).D'));
+
+ % compute the regression
+ if (dcps(ID).c_order == 1)
+ dcps(ID).sx2 = sum(((V.^2)*ones(1,length(dcps(ID).c))).*PSI,1)';
+ dcps(ID).sxtd = sum(((V.*Ft)*ones(1,length(dcps(ID).c))).*PSI,1)';
+ dcps(ID).w = dcps(ID).sxtd./(dcps(ID).sx2+1.e-10);
+ else
+ dcps(ID).sx2 = sum(((X.^2)*ones(1,length(dcps(ID).c))).*PSI,1)';
+ dcps(ID).sxtd = sum(((X.*Ft)*ones(1,length(dcps(ID).c))).*PSI,1)';
+ dcps(ID).w = dcps(ID).sxtd./(dcps(ID).sx2+1.e-10);
+ end
+
+ % compute the prediction
+ if (dcps(ID).c_order == 1)
+ F = sum((V*dcps(ID).w').*PSI,2)./sum(PSI,2) * amp;
+ else
+ F = sum((X*dcps(ID).w').*PSI,2)./sum(PSI,2) * amp;
+ end
+ z = 0;
+ zd = 0;
+ y = y0;
+ Y = zeros(size(T));
+ Yd = zeros(size(T));
+ Ydd = zeros(size(T));
+
+ for i=1:length(T),
+
+ Ydd(i) = zd*tau;
+ Yd(i) = z;
+ Y(i) = y;
+
+ zd = (dcps(ID).alpha_z*(dcps(ID).beta_z*(G(i)-y)-z)+F(i))*tau;
+ yd = z;
+
+ z = zd*dt+z;
+ y = yd*dt+y;
+
+ end
+
+ varargout(1) = {Y};
+ varargout(2) = {Yd};
+ varargout(3) = {Ydd};
+
+% .........................................................................
+ case 'structure'
+ ID = varargin{1};
+ varargout(1) = {dcps(ID)};
+
+% .........................................................................
+ case 'clear'
+ ID = varargin{1};
+ if exist('dcps')
+ if length(dcps) >= ID,
+ dcps(ID) = [];
+ end
+ end
+
+% .........................................................................
+ case 'minjerk'
+ ID = varargin{1};
+
+ % generate the minimum jerk trajectory as target to learn from
+ t=0;
+ td=0;
+ tdd=0;
+ goal = 1;
+
+ dcp('reset_state',ID);
+ dcp('set_goal',ID,goal,1);
+ tau = 0.5;
+ dt = 0.001;
+ T=zeros(2*tau/dt,3);
+
+ for i=0:2*tau/dt,
+ [t,td,tdd]=min_jerk_step(t,td,tdd,goal,tau-i*dt,dt);
+ T(i+1,:) = [t td tdd];
+ end;
+
+ % batch fitting
+ i = round(2*tau/dt); % only fit the part of the trajectory with the signal
+ [Yp,Ypd,Ypdd]=dcp('batch_fit',ID,tau,dt,T(1:i,1),T(1:i,2),T(1:i,3));
+
+% .........................................................................
+ otherwise
+ error('unknown action');
+
+end
\ No newline at end of file
diff --git a/dmp/matlab/dmp_from_ogs/code/dmp_learn.m b/dmp/matlab/dmp_from_ogs/code/dmp_learn.m
new file mode 100644
index 0000000..8355cec
--- /dev/null
+++ b/dmp/matlab/dmp_from_ogs/code/dmp_learn.m
@@ -0,0 +1 @@
+% function to learn a DCP in batch mode using a minimum jerk trajectory as
% template
global dcps;
% general parameters
dt = 0.001;
goal = 1;
tau = 0.5;
n_rfs = 10;
ID = 1;
% initialize DMP
dcp('clear',ID);
dcp('init',ID,n_rfs,'minJerk_dcp',1);
% initialize some variables for plotting
Z=zeros(floor(2*tau/dt+1),2);
X=Z;
V=Z;
T=zeros(floor(2*tau/dt+1),3);
Y=T;
PSI=zeros(floor(2*tau/dt+1),n_rfs);
W=PSI;
% generate the minimum jerk trajectory
t=0;
td=0;
tdd=0;
for i=0:2*tau/dt,
[t,td,tdd]=min_jerk_step(t,td,tdd,goal,tau-i*dt,dt);
T(i+1,:) = [t td tdd];
end;
% use the in-built function to initialize the dcp with minjerk
dcp('Batch_Fit',ID,tau,dt,T(:,1),T(:,2),T(:,3));
% test the fit
dcp('reset_state',ID);
dcp('set_goal',ID,goal,1);
for i=0:2*tau/dt,
[y,yd,ydd]=dcp('run',ID,tau,dt);
Z(i+1,:) = [dcps(ID).z dcps(ID).zd];
Y(i+1,:) = [y yd ydd];
X(i+1,:) = [dcps(ID).x dcps(ID).xd];
V(i+1,:) = [dcps(ID).v dcps(ID).vd];
PSI(i+1,:) = dcps(ID).psi';
W(i+1,:) = dcps(ID).w';
end;
% plotting
time = (0:dt:tau*2)';
figure(1);
clf;
% plot position, velocity, acceleration vs. target
subplot(431);
plot(time,[Y(:,1) T(:,1)]);
title('y');
aa=axis;
axis([min(time) max(time) aa(3:4)]);
subplot(432);
plot(time,[Y(:,2) T(:,2)]);
title('yd');
aa=axis;
axis([min(time) max(time) aa(3:4)]);
subplot(433);
plot(time,[Y(:,3) T(:,3)]);
title('ydd');
aa=axis;
axis([min(time) max(time) aa(3:4)]);
% plot internal states
subplot(434);
plot(time,Z(:,1));
title('z');
aa=axis;
axis([min(time) max(time) aa(3:4)]);
subplot(435);
plot(time,Z(:,2));
title('zd');
aa=axis;
axis([min(time) max(time) aa(3:4)]);
subplot(436);
plot(time,PSI);
title('Weighting Kernels');
aa=axis;
axis([min(time) max(time) aa(3:4)]);
subplot(437);
plot(time,V(:,1));
title('v');
aa=axis;
axis([min(time) max(time) aa(3:4)]);
subplot(438);
plot(time,V(:,2));
title('vd');
aa=axis;
axis([min(time) max(time) aa(3:4)]);
subplot(439);
plot(time,W);
title('Linear Model Weights over Time');
aa=axis;
axis([min(time) max(time) aa(3:4)]);
subplot(4,3,10);
plot(time,X(:,1));
title('x');
aa=axis;
axis([min(time) max(time) aa(3:4)]);
subplot(4,3,11);
plot(time,X(:,2));
title('xd');
aa=axis;
axis([min(time) max(time) aa(3:4)]);
subplot(4,3,12);
plot(W(end,:));
title('Weights');
xlabel(sprintf('tau=%f',tau));
drawnow;
\ No newline at end of file
diff --git a/dmp/matlab/dmp_from_ogs/code/rcp.m b/dmp/matlab/dmp_from_ogs/code/rcp.m
new file mode 100644
index 0000000..b8005ee
--- /dev/null
+++ b/dmp/matlab/dmp_from_ogs/code/rcp.m
@@ -0,0 +1,396 @@
+function [varargout] = rcp(action,varargin)
+% A rhythmic movement primitive (RCP) as suggested in
+% Ijspeert A, Nakanishi J, Schaal S (2003) Learning attractor landscapes for
+% learning motor primitives. In: Becker S, Thrun S, Obermayer K (eds) Advances
+% in Neural Information Processing Systems 15. MIT Press, Cambridge, MA.
+% http://www-clmc.usc.edu/publications/I/ijspeert-NIPS2002.pdf
+% This version simplfies the primitive and only uses a one dimensional
+% function fitting.
+%
+% Copyright June, 2006 by
+% Stefan Schaal and Auke Ijspeert
+%
+% --------------- Different Actions of the program ------------------------
+%
+% Initialize a rcp model:
+% FORMAT rcp('Init',ID,n_rfs,name)
+% ID : desired ID of model
+% n_rfs : number of local linear models
+% name : a name for the model
+%
+% alternatively, the function is called as
+%
+% FORMAT rcp('Init',ID,d,)
+% d : a complete data structure of a rcp model
+%
+% returns nothing
+% -------------------------------------------------------------------------
+%
+% Set the amplitude:
+% FORMAT rcp('Set_Amplitude',ID,A)
+% ID : ID of model
+% A : the new amplitude
+%
+% returns nothing
+% -------------------------------------------------------------------------
+%
+% Set the baseline:
+% FORMAT rcp('Set_Baseline',ID,ym)
+% ID : ID of model
+% ym : the new baseline for the oscillation
+%
+% returns nothing
+% -------------------------------------------------------------------------
+%
+% Run the rcp:
+% FORMAT [[y,yd,ydd]=rcp('Run',ID,tau,dt)
+% ID : ID of model
+% tau : global time constant to scale speed of system
+% dt : integration time step
+%
+% returns y,yd,ydd, i.e., current pos,vel,acc, of the rcp
+% -------------------------------------------------------------------------
+%
+% Change values of a rcp:
+% FORMAT rcp('Change',ID,pname,value)
+% ID : ID of model
+% pname : parameter name
+% value : value to be assigned to parameter
+%
+% returns nothing
+% -------------------------------------------------------------------------
+%
+% Run the rcps:
+% FORMAT rcp('Run',ID,tau,dt,t,td)
+% ID : ID of model
+% tau : time constant to scale speed, roughly equivalent to
+% period of movement
+% dt : integration time step
+%
+% returns y,yd,ydd, i.e., current pos,vel,acc, of the rcp
+% -------------------------------------------------------------------------
+%
+% Run the rcp and update the weights:
+% FORMAT rcp('Run_Fit',ID,tau,dt,t,td,tdd)
+% ID : ID of model
+% tau : time constant to scale speed
+% dt : integration time step
+% t : target for y
+% td : target for yd
+% tdd : target for ydd
+%
+% returns y,yd,ydd, i.e., current pos,vel,acc, of the rcp
+% -------------------------------------------------------------------------
+%
+% Fit the rcp to a complete trajectory in batch mode:
+% FORMAT rcp('Batch_Fit',ID,tau,dt,T,Td)
+% ID : ID of model
+% tau : time constant to scale speed, tau is roughly movement
+% time until convergence the goal
+% dt : somple time step in given trajectory
+% T : target trajectory for y
+% Td : target trajectory for yd (optional, will be generated
+% as dT/dt otherwise
+% Tdd : target trajectory for ydd (optional, will be generated
+% as dTd/dt otherwise
+%
+% returns y,yd,ydd, i.e., current pos,vel,acc, of the rcp
+% -------------------------------------------------------------------------
+%
+% Return the data structure of a rcp model
+% FORMAT [d] = rcps('Structure',ID)
+% ID : desired ID of model
+%
+% returns the complete data structure of a rcp model, e.g., for saving or
+% inspecting it
+% -------------------------------------------------------------------------
+%
+% Reset the states of a rcp model to zero
+% FORMAT [d] = rcps('Reset_State',ID)
+% ID : desired ID of model
+%
+% returns nothing
+% -------------------------------------------------------------------------
+%
+% Clear the data structure of a LWPR model
+% FORMAT rcps('Clear',ID)
+% ID : ID of model
+%
+% returns nothing
+% -------------------------------------------------------------------------
+%
+% Initializes the rcp with a sine trajectory
+% FORMAT rcps('Sine',ID)
+% ID : ID of model
+%
+% returns nothing
+% -------------------------------------------------------------------------
+
+% the global structure to store all rcps
+global rcps;
+
+% at least two arguments are needed
+if nargin < 2,
+ error('Incorrect call to rcp');
+end
+
+switch lower(action),
+% .........................................................................
+ case 'init'
+ if (nargin == 3)
+ ID = varargin{1};
+ rcps(ID) = varargin{2};
+ else
+ % this initialization is good for period 1 second movement for tau=1
+ ID = varargin{1};
+ n_rfs = varargin{2};
+ rcps(ID).name = varargin{3};
+ % the time constants for chosen for critical damping
+ rcps(ID).alpha_z = 25;
+ rcps(ID).beta_z = rcps(ID).alpha_z/4;
+ rcps(ID).omega = 0.01;%2*pi;
+ % initialize the state variables
+ rcps(ID).z = 0;
+ rcps(ID).y = 0;
+ rcps(ID).p = 0;
+ rcps(ID).zd = 0;
+ rcps(ID).yd = 0;
+ rcps(ID).pd = 0;
+ rcps(ID).ydd = 0;
+ % baseline and amplitude of oscillation
+ rcps(ID).ym = 0;
+ rcps(ID).A = 0;
+
+ % the local models, spaced on grid in phase space
+ rcps(ID).c = 2*pi*(1/n_rfs:1/n_rfs:1)';
+ rcps(ID).psi = zeros(n_rfs,1);
+ rcps(ID).w = zeros(n_rfs,1);
+ rcps(ID).sp2 = zeros(n_rfs,1);
+ rcps(ID).sptd = zeros(n_rfs,1);
+ rcps(ID).D = ones(n_rfs,1)*.1*n_rfs^2;
+ rcps(ID).lambda = 1;
+ end
+
+% .........................................................................
+ case 'reset_state'
+ ID = varargin{1};
+ % initialize the state variables
+ rcps(ID).z = 0;
+ rcps(ID).y = 0;
+ rcps(ID).p = 0;
+ rcps(ID).zd = 0;
+ rcps(ID).yd = 0;
+ rcps(ID).pd = 0;
+ rcps(ID).ydd = 0;
+ % the baseline
+ rcps(ID).ym = 0;
+
+% .........................................................................
+ case 'set_baseline'
+ ID = varargin{1};
+ rcps(ID).ym = varargin{2};
+
+% .........................................................................
+ case 'set_amplitude'
+ ID = varargin{1};
+ rcps(ID).A = varargin{2};
+
+% .........................................................................
+ case 'run'
+ ID = varargin{1};
+ tau = varargin{2};
+ dt = varargin{3};
+ if nargin > 4,
+ random_value = varargin{4};
+ else
+ random_value = 0;
+ end
+
+ % the weighted sum of the locally weighted regression models
+ rcps(ID).psi = exp(rcps(ID).D.*(cos(rcps(ID).p-rcps(ID).c)-1));
+ amp = rcps(ID).A;
+ f = sum(rcps(ID).w.*rcps(ID).psi)/sum(rcps(ID).psi+1.e-10) * amp;
+
+ rcps(ID).pd = rcps(ID).omega*tau;
+
+ rcps(ID).zd = (rcps(ID).alpha_z*(rcps(ID).beta_z*(rcps(ID).ym-rcps(ID).y)-rcps(ID).z)+f)*tau ...
+ + random_value;
+ rcps(ID).yd = rcps(ID).z*tau;
+ rcps(ID).ydd= rcps(ID).zd*tau;
+
+ rcps(ID).p = rcps(ID).pd*dt+rcps(ID).p;
+
+ rcps(ID).z = rcps(ID).zd*dt+rcps(ID).z;
+ rcps(ID).y = rcps(ID).yd*dt+rcps(ID).y;
+
+ varargout(1) = {rcps(ID).y};
+ varargout(2) = {rcps(ID).yd};
+ varargout(3) = {rcps(ID).ydd};
+ %varargout(4) = {rcps(ID).psi*rcps(ID).v/sum(rcps(ID).psi)};
+ varargout(4) = {amp*rcps(ID).psi};
+
+% .........................................................................
+ case 'change'
+ ID = varargin{1};
+ command = sprintf('rcps(%d).%s = varargin{3};',ID,varargin{2});
+ eval(command);
+
+% .........................................................................
+ case 'run_fit'
+ ID = varargin{1};
+ tau = varargin{2};
+ dt = varargin{3};
+ t = varargin{4};
+ td = varargin{5};
+ tdd = varargin{6};
+
+ % the regression target
+ amp = rcps(ID).A;
+ ft = (tdd/tau^2-rcps(ID).alpha_z*(rcps(ID).beta_z*(rcps(ID).ym-t)-td/tau))/amp;
+
+ % the weighted sum of the locally weighted regression models
+ rcps(ID).psi = exp(rcps(ID).D.*(cos(rcps(ID).p-rcps(ID).c)-1));
+
+ % update the regression
+ rcps(ID).sp2 = rcps(ID).sp2*rcps(ID).lambda + rcps(ID).psi;
+ rcps(ID).sptd = rcps(ID).sptd*rcps(ID).lambda + rcps(ID).psi*ft;
+ rcps(ID).w = rcps(ID).sptd./(rcps(ID).sp2+1.e-10);
+
+ % compute nonlinearity
+ f = sum(rcps(ID).w.*rcps(ID).psi)/sum(rcps(ID).psi+1.e-10) * amp;
+
+ % integrate
+ rcps(ID).pd = rcps(ID).omega*tau;
+
+ % note that yd = td = z*tau ==> z=td/tau; the first equation means
+ % simply rcps(ID).zd = tdd
+ rcps(ID).zd = (rcps(ID).alpha_z*(rcps(ID).beta_z*(rcps(ID).ym-rcps(ID).y)-rcps(ID).z)+f)*tau;
+ rcps(ID).yd = rcps(ID).z*tau;
+ rcps(ID).ydd= rcps(ID).zd*tau;
+
+ rcps(ID).p = rcps(ID).pd*dt+rcps(ID).p;
+
+ rcps(ID).z = rcps(ID).zd*dt+rcps(ID).z;
+ rcps(ID).y = rcps(ID).yd*dt+rcps(ID).y;
+
+ varargout(1) = {rcps(ID).y};
+ varargout(2) = {rcps(ID).yd};
+ varargout(3) = {rcps(ID).ydd};
+
+% .........................................................................
+ case 'batch_fit'
+
+ ID = varargin{1};
+ tau = varargin{2};
+ dt = varargin{3};
+ T = varargin{4};
+ if (nargin > 4)
+ Td = varargin{5};
+ else
+ Td = diffnc(T,dt);
+ end
+ if (nargin > 5)
+ Tdd = varargin{6};
+ else
+ Tdd = diffnc(Td,dt);
+ end
+
+ % the amplitude
+ A = rcps(ID).A;
+
+ % the baseline
+ ym = rcps(ID).ym;
+
+ % compute the hidden states
+ P = zeros(size(T));
+ p = 0;
+
+ for i=1:length(T),
+ P(i) = p;
+ pd = rcps(ID).omega*tau;
+ p = pd*dt+p;
+ end
+
+ % the regression target
+ amp = A;
+ Ft = (Tdd/tau^2-rcps(ID).alpha_z*(rcps(ID).beta_z*(ym-T)-Td/tau)) / amp;
+
+ % compute the weights for each local model along the trajectory
+ PSI = exp((cos(P*ones(1,length(rcps(ID).c))-ones(length(T),1)*rcps(ID).c')-1).*(ones(length(T),1)*rcps(ID).D'));
+
+
+ % compute the regression
+ rcps(ID).sp2 = sum((ones(length(P),length(rcps(ID).c))).*PSI,1)';
+ rcps(ID).sptd = sum((Ft*ones(1,length(rcps(ID).c))).*PSI,1)';
+ rcps(ID).w = rcps(ID).sptd./(rcps(ID).sp2+1.e-10);
+
+
+ % compute the prediction
+ F = sum((ones(length(P),1)*rcps(ID).w').*PSI,2)./sum(PSI,2) * amp;
+ z = 0;
+ zd = 0;
+ y = T(1);
+ Y = zeros(size(T));
+ Yd = zeros(size(T));
+ Ydd = zeros(size(T));
+
+ for i=1:length(T),
+
+ Ydd(i) = zd*tau;
+ Yd(i) = z;
+ Y(i) = y;
+
+ zd = (rcps(ID).alpha_z*(rcps(ID).beta_z*(ym-y)-z)+F(i))*tau;
+ yd = z;
+
+ z = zd*dt+z;
+ y = yd*dt+y;
+
+ end
+
+ varargout(1) = {Y};
+ varargout(2) = {Yd};
+ varargout(3) = {Ydd};
+
+% .........................................................................
+ case 'structure'
+ ID = varargin{1};
+ varargout(1) = {rcps(ID)};
+
+% .........................................................................
+ case 'clear'
+ ID = varargin{1};
+ rcps(ID) = [];
+
+% .........................................................................
+ case 'sine'
+ ID = varargin{1};
+
+ % generate the minimum jerk trajectory as target to learn from
+ t=0;
+ td=0;
+ tdd=0;
+ A = 1;
+ ym = 0;
+
+ rcp('reset_state',ID);
+ rcp('set_amplitude',ID,A);
+ rcp('set_baseline',ID,ym);
+ tau = 1;
+ dt = 0.001;
+ T=zeros(2*tau/dt,3);
+
+ for i=0:2*tau/dt,
+ t = i*dt;
+ T(i+1,:) = [A*sin(2*pi/tau*t) A*(2*pi/tau)*cos(2*pi/tau*t) -A*(2*pi/tau)^2*sin(2*pi/tau*t)];
+ end;
+
+ % batch fitting
+ i = round(2*tau/dt); % only fit the part of the trajectory with the signal
+ [Yp,Ypd,Ypdd]=rcp('batch_fit',ID,tau,dt,T(1:i,1),T(1:i,2),T(1:i,3));
+
+% .........................................................................
+ otherwise
+ error('unknown action');
+
+end
\ No newline at end of file
diff --git a/dmp/matlab/dmp_from_ogs/code/rmp_learn.m b/dmp/matlab/dmp_from_ogs/code/rmp_learn.m
new file mode 100644
index 0000000..42ebf0d
--- /dev/null
+++ b/dmp/matlab/dmp_from_ogs/code/rmp_learn.m
@@ -0,0 +1 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Learns a rhythmic movement primitive (RCP) in batch mode %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clc;
close all;
clear all;
%% Define parameters
% global variable
global rcps;
% symbolic varible
syms t; %time
% general parameters
BATCH = 1; % 0 - incremental learning, 1 - batch learning
dt = 0.01; %
ym = 0; %
A = 1; %
n_rfs = 1000; % 50
tau = 1; %
omega = 0.01; % frequency
duration = 10; % experiment duration (in sec)
dimentions = 2; % number of conrolled axis
% variables arrays for data monitoring
time = []; % time
time_end = []; % end of each experiment
%D = zeros(round(duration*tau/dt+1),3,dimentions); % data: (D = [[t(0) t(1) ...] [position velocity acceleration] [x-axis y-axis]])
D = zeros(0,3,dimentions); %
%% Generate data
% for i=0:10*tau/dt
% d(1) = 1 - (2*2^(1/2)*cos(2*pi*dt*i))/(cos(4*pi*dt*i) - 3);
% dd(1) = (4*2^(1/2)*pi*sin(2*pi*dt*i))/(cos(4*pi*dt*i) - 3) - (8*2^(1/2)*pi*cos(2*pi*dt*i)*sin(4*pi*dt*i))/(cos(4*dt*i*pi) - 3)^2;
% ddd(1) = (8*2^(1/2)*pi^2*cos(2*pi*dt*i))/(cos(4*pi*dt*i) - 3) - (64*2^(1/2)*pi^2*cos(2*pi*dt*i)*sin(4*pi*dt*i)^2)/(cos(4*dt*i*pi) - 3)^3 - (32*2^(1/2)*pi^2*cos(2*pi*dt*i)*cos(4*pi*dt*i))/(cos(4*dt*i*pi) - 3)^2 + (32*2^(1/2)*pi^2*sin(2*pi*dt*i)*sin(4*pi*dt*i))/(cos(4*dt*i*pi) - 3)^2;
% d(2) = 2 - (2*sin(4*pi*dt*i))/(cos(4*pi*dt*i) - 3);
% dd(2) = - (8*pi*cos(4*pi*dt*i))/(cos(4*pi*dt*i) - 3) - (8*pi*sin(4*pi*dt*i)^2)/(cos(4*dt*i*pi) - 3)^2;
% ddd(2) = (32*pi^2*sin(4*pi*dt*i))/(cos(4*pi*dt*i) - 3) - (64*pi^2*sin(4*pi*dt*i)^3)/(cos(4*dt*i*pi) - 3)^3 - (96*pi^2*cos(4*pi*dt*i)*sin(4*pi*dt*i))/(cos(4*dt*i*pi) - 3)^2;
% D(i+1,:,1) = [d(1) dd(1) ddd(1)];
% D(i+1,:,2) = [d(2) dd(2) ddd(2)];
% end
% time = (0:dt:tau*10)';
%
% % add some gausian white noise
% D(:,1,:) = D(:,1,:) + randn(size(D(:,1,:))) .* (max(D(:,1,:)) - min(D(:,1,:))) / 50;
% D(:,2,:) = D(:,2,:) + randn(size(D(:,2,:))) .* (max(D(:,2,:)) - min(D(:,2,:))) / 50;
% D(:,3,:) = D(:,3,:) + randn(size(D(:,3,:))) .* (max(D(:,3,:)) - min(D(:,3,:))) / 50;
%% Load data
indx_Time = 1;
indx_RawH1 = 2:3;
indx_RawH2 = 4:5;
indx_PosH1 = 6:7;
indx_PosH2 = 8:9;
indx_VelH1 = 10:11;
indx_VelH2 = 12:13;
indx_AccH1 = 14:15;
indx_AccH2 = 16:17;
indx_PosO1 = 18:19;
indx_PosO2 = 20:21;
indx_VelO1 = 22:23;
indx_VelO2 = 24:25;
indx_AccO1 = 26:27;
indx_AccO2 = 28:29;
indx_K1 = 37; %% Siffness
indx_K2 = 38;
indx_B1 = 39; %% Damping
indx_B2 = 40;
indx_A0 = 41; %% admittance Object
pathname = [pwd '\MATLAB\data\Chris_Davide\session_1\HMan1_Intermit\'];
file_list = dir([pathname '*.mat']);
for i = 1:1%length(file_list)
filename = file_list(i).name
load([pathname filename]);
data = data';
d1 = [data(:, indx_PosH1(1)) data(:, indx_VelH1(1)) data(:, indx_AccH1(1))];
d2 = [data(:, indx_PosH1(2)) data(:, indx_VelH1(2)) data(:, indx_AccH1(2))];
l = length(D);
D(l+1:l+length(data),:,1) = d1;
D(l+1:l+length(data),:,2) = d2;
if isempty(time)
time = data(:, indx_Time);
else
time = [time; data(:, indx_Time) + time(end)];
end
time_end = [time_end, time(end)];
max(time)
end
% numerical derivation of velocity and acceleration
D(:,2,1) = [0; diff(D(:,1,1))]/dt;
D(:,3,1) = [0; diff(D(:,2,1))]/dt;
D(:,2,2) = [0; diff(D(:,1,2))]/dt;
D(:,3,2) = [0; diff(D(:,2,2))]/dt;
%% Alocate variables for monitoring
P = zeros(length(D),2,dimentions); %
Z = zeros(length(D),2,dimentions); %
Y = zeros(length(D),3,dimentions); %
PSI = zeros(length(D),n_rfs,dimentions); %
W = zeros(length(D),n_rfs,dimentions); %
F = zeros(length(D),3,dimentions); % fit result: (F = [[t(0) t(1) ...] [position velocity acceleration] [x-axis y-axis]])
%% Initialize RCPs
for j = 1:dimentions
rcp('init',j,n_rfs,['axis', num2str(j)]);
rcp('reset_state',j);
rcp('set_baseline',j,ym);
rcp('set_amplitude',j,A);
end
%% Batch fitting
if BATCH == 1
for j = 1:dimentions
[F(:,1,j), F(:,2,j), F(:,3,j)] = rcp('batch_fit', j, tau, dt, D(:,1,j), D(:,2,j), D(:,3,j));
end
%% Incremental fitting
else
for j = 1:dimentions
for i = 1:length(time)
[F(i,1,j), F(i,2,j), F(i,3,j)] = rcp('run_fit', j, tau, dt, D(i,1,j), D(i,2,j), D(i,3,j));
end
end
end
%% Reset RCP
for j = 1:dimentions
rcp('reset_state',j);
rcp('set_baseline',j,ym);
rcp('set_amplitude',j,A);
end
%% Run RCP
for j = 1:dimentions
for i = 1:length(time)
[y,yd,ydd] = rcp('run',j,tau,dt);
P(i,:,j) = [rcps(j).p rcps(j).pd];
Z(i,:,j) = [rcps(j).z rcps(j).zd];
Y(i,:,j) = [y yd ydd];
PSI(i,:,j) = rcps(j).psi';
W(i,:,j) = rcps(j).w';
disp([num2str(((j - 1)/dimentions + i/length(time)/dimentions)*100),'%'])
end
end
%% Plot
% plot position, velocity, acceleration vs. target
figure(1);
clf;
hold on;
grid on;
scatter(D(:,1,1), D(:,1,2), 20, 'b.');
%plot(D(:,1,1), D(:,1,2), 'b:', 'linewidth', 2);
plot(F(:,1,1), F(:,1,2), 'g--', 'linewidth', 2);
plot(Y(:,1,1), Y(:,1,2), 'r-', 'linewidth', 2);
legend('data', 'fit', 'run', 'Orientation', 'horizontal', 'FontSize', 11, 'Position', [0.21, 0.95, 0.58, 0.04]);
set(gca, 'fontsize', 11);
set(gca, 'TickLabelInterpreter', 'latex');
xlabel('$x$', 'interpreter', 'latex', 'fontsize', 11);
ylabel('$y$', 'interpreter', 'latex', 'fontsize', 11);
% figure(11);
% subplot(211);
% hold on;
% grid on;
% scatter(time, D(:,1,1), 20, 'b.');
% plot(time, F(:,1,1), 'g', 'linewidth', 2);
% for i = 1:length(time_end)
% xline(time_end(i), ':', 'linewidth', 1);
% end
% xlim([0 max(time)]);
% set(gca, 'fontsize', 11);
% set(gca, 'TickLabelInterpreter', 'latex')
% ylabel('$x$', 'interpreter', 'latex', 'fontsize', 11);
% subplot(212);
% hold on;
% grid on;
% h1 = scatter(time, D(:,1,2), 20, 'b.');
% h2 = plot(time, F(:,1,2), 'g', 'linewidth', 2);
% legend([h1, h2], {'data', 'fit'}, 'Orientation', 'horizontal', 'FontSize', 11, 'Position', [0.21, 0.95, 0.58, 0.04]);
% for i = 1:length(time_end)
% h_xline = xline(time_end(i), ':', 'linewidth', 1, 'HandleVisibility', 'off');
% end
% xlim([0 max(time)]);
% set(gca, 'fontsize', 11);
% set(gca, 'TickLabelInterpreter', 'latex')
% xlabel('$t$', 'interpreter', 'latex', 'fontsize', 11);
% ylabel('$y$', 'interpreter', 'latex', 'fontsize', 11);
% plot position, velocity, acceleration vs. target
for j = 1:dimentions
figure(j+1);
Dd = [0; diff(D(:,1,j))]/dt;
Ddd1 = [0; diff(Dd)]/dt;
Ddd2 = [0; diff(D(:,2,j))]/dt;
subplot(311);
hold on;
grid on;
scatter(time, D(:,1,j), 20, 'b.');
plot(time, F(:,1,j), 'g--', 'linewidth', 2);
plot(time, Y(:,1,j), 'r-', 'linewidth', 2);
for i = 1:length(time_end)
xline(time_end(i), ':', 'linewidth', 1);
end
xlim([0 max(time)]);
set(gca, 'fontsize', 11);
set(gca, 'TickLabelInterpreter', 'latex')
ylabel(['$y_', num2str(j),'$ [m]'], 'interpreter', 'latex', 'fontsize', 11);
subplot(312);
hold on;
grid on;
scatter(time, D(:,2,j), 20, 'b.');
% plot(time, Dd, 'c:', 'linewidth', 2);
plot(time, F(:,2,j), 'g--', 'linewidth', 2);
plot(time, Y(:,2,j), 'r-', 'linewidth', 2);
for i = 1:length(time_end)
xline(time_end(i), ':', 'linewidth', 1);
end
xlim([0 max(time)]);
set(gca, 'fontsize', 11);
set(gca, 'TickLabelInterpreter', 'latex')
ylabel(['$\dot{y}_', num2str(j),'$ [m/s]'], 'interpreter', 'latex', 'fontsize', 11);
subplot(313);
hold on;
grid on;
h1 = scatter(time, D(:,3,j), 20, 'b.');
% h2 = plot(time, Ddd1, 'c:', 'linewidth', 2);
% plot(time, Ddd2, 'm:', 'linewidth', 2);
h2 = plot(time, F(:,3,j), 'g--', 'linewidth', 2);
h3 = plot(time, Y(:,3,j), 'r-', 'linewidth', 2);
legend([h1, h2, h3], {'data', 'fit', 'run'}, 'Orientation', 'horizontal', 'FontSize', 11, 'Position', [0.21, 0.95, 0.58, 0.04]);
for i = 1:length(time_end)
h_xline = xline(time_end(i), ':', 'linewidth', 1, 'HandleVisibility', 'off');
end
xlim([0 max(time)]);
set(gca, 'fontsize', 11);
set(gca, 'TickLabelInterpreter', 'latex')
xlabel('$t$ [s]', 'interpreter', 'latex', 'fontsize', 11);
ylabel(['$\ddot{y}_', num2str(j),'$ [m/s$^2$]'], 'interpreter', 'latex', 'fontsize', 11);
end
% plot internal states
for j = 1:dimentions
figure(dimentions+1+j);
subplot(231);
plot(time,Z(:,1,j));
title('z');
aa=axis;
axis([min(time) max(time) aa(3:4)]);
subplot(232);
plot(time,Z(:,2,j));
title('zd');
aa=axis;
axis([min(time) max(time) aa(3:4)]);
subplot(233);
plot(time,PSI(:,:,j));
title('Weighting Kernels');
aa=axis;
axis([min(time) max(time) aa(3:4)]);
subplot(234);
plot(time,P(:,1,j));
title('p');
aa=axis;
axis([min(time) max(time) aa(3:4)]);
subplot(235);
plot(time,P(:,2,j));
title('pd');
aa=axis;
axis([min(time) max(time) aa(3:4)]);
subplot(236);
plot(W(end,:,j));
title('Weights');
xlabel(sprintf('tau=%f',tau));
end
for j = 1:dimentions
for h = 1:3
performance(j,h,1) = immse(Y(:,h,j), D(:,h,j));
% performance(j,h,2) = immse(F(:,h,j), D(:,h,j));
% performance(j,h,3) = immse(Y(:,h,j), F(:,h,j));
end
performance(j,:,:)
end
\ No newline at end of file
diff --git a/dmp/matlab/dmp_from_ogs/code/transformation.m b/dmp/matlab/dmp_from_ogs/code/transformation.m
new file mode 100644
index 0000000..f67e6e7
--- /dev/null
+++ b/dmp/matlab/dmp_from_ogs/code/transformation.m
@@ -0,0 +1,81 @@
+clc;
+close all;
+clear all;
+
+%% Load data
+
+indx_Time = 1;
+indx_RawH1 = 2:3;
+indx_RawH2 = 4:5;
+indx_PosH1 = 6:7;
+indx_PosH2 = 8:9;
+indx_VelH1 = 10:11;
+indx_VelH2 = 12:13;
+indx_AccH1 = 14:15;
+indx_AccH2 = 16:17;
+indx_PosO1 = 18:19;
+indx_PosO2 = 20:21;
+indx_VelO1 = 22:23;
+indx_VelO2 = 24:25;
+indx_AccO1 = 26:27;
+indx_AccO2 = 28:29;
+
+indx_K1 = 37; %% Siffness
+indx_K2 = 38;
+indx_B1 = 39; %% Damping
+indx_B2 = 40;
+indx_A0 = 41; %% admittance Object
+
+pathname = [pwd '\..\MATLAB\data\Chris_Davide\session_1\HMan1_Intermit\'];
+file_list = dir([pathname '*.mat']);
+
+for i = 10:10%length(file_list)
+ filename = file_list(i).name
+ load([pathname filename]);
+ data = data';
+
+ x = [data(:, indx_PosH1(1))];
+ wL = [data(:, indx_VelH1(1))];
+ aL = [data(:, indx_AccH1(1))];
+ y = [data(:, indx_PosH1(2))];
+ wR = [data(:, indx_VelH1(2))];
+ aR = [data(:, indx_AccH1(2))];
+
+ t = [data(:, indx_Time)];
+end
+
+dt = mean(diff(t));
+
+% numerical derivation of velocity and acceleration
+vx_numerical = [0; diff(x)]/dt;
+ax_numerical = [0; diff(vx_numerical)]/dt;
+vy_numerical = [0; diff(y)]/dt;
+ay_numerical = [0; diff(vy_numerical)]/dt;
+
+%
+J = [vx_numerical'; vy_numerical'] * pinv([wL'; wR'])
+
+rotm2eul([J [0; 0]; [0 0 1]])/pi*180
+
+% J = [0 -1; 1 0]
+
+rm = 2 * [vx_numerical' vy_numerical'] * pinv([J(1,1)*wL' + J(1,2)*wR' J(2,1)*wL' + J(2,2)*wR'])
+
+vx_measured = J(1,:)*[wL'; wR'];
+vy_measured = J(2,:)*[wL'; wR'];
+
+figure(2);
+hold on;
+grid on;
+plot(t, vx_numerical, 'r', 'linewidth', 2);
+plot(t, vy_numerical, 'g', 'linewidth', 2);
+% plot(t, wL, 'm--', 'linewidth', 2);
+% plot(t, -wR, 'c--', 'linewidth', 2);
+plot(t, vx_measured, 'y:', 'linewidth', 2);
+plot(t, vy_measured, 'b:', 'linewidth', 2);
+xlim([0 max(t)]);
+set(gca, 'fontsize', 11);
+set(gca, 'TickLabelInterpreter', 'latex');
+legend('$\dot{x}$', '$\dot{y}$', '$\tilde{v}_x$', '$\tilde{v}_y$', 'interpreter', 'latex', 'fontsize', 11);
+xlabel('$t$ [s]', 'interpreter', 'latex', 'fontsize', 11);
+ylabel(['$v$ [m/s]'], 'interpreter', 'latex', 'fontsize', 11);
\ No newline at end of file
diff --git a/dmp/motion_planning/dmp/.dmp.py.swp b/dmp/motion_planning/dmp/.dmp.py.swp
new file mode 100644
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diff --git a/dmp/my_sol/__pycache__/robotiq_gripper.cpython-310.pyc b/dmp/my_sol/__pycache__/robotiq_gripper.cpython-310.pyc
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diff --git a/dmp/my_sol/create_dmp.py b/dmp/my_sol/create_dmp.py
new file mode 100644
index 0000000..c524edc
--- /dev/null
+++ b/dmp/my_sol/create_dmp.py
@@ -0,0 +1,147 @@
+import numpy as np
+
+class DMP:
+ def __init__(self, k=100, d=20, a_s=1, n_bfs=100):
+ # parameters
+ self.k = k
+ self.d = d
+ self.a_s = a_s
+ self.n_bfs = n_bfs
+
+ # trajectory parameters
+ self.n = 0
+ self.y0 = None
+ self.tau0 = None
+ self.g = None
+ self.tau = None
+
+ # initialize basis functions for LWR
+ self.w = None
+ self.centers = None
+ self.widths = None
+
+ # state
+ self.x = None
+ self.theta = None
+ self.pos = None
+ self.vel = None
+ self.acc = None
+
+ # desired path
+ self.path = None
+
+ # actually init
+ # TODO pass other trajectories as arguments and etc to make this nice
+ self.load_trajectory()
+ self.fit()
+
+
+ # TODO pass other trajectories as arguments and etc to make this nice
+ def load_trajectory(self):
+ # load trajectory. the current one is something on ur10, but apart from scale it's the same robot
+ # and this is just joint positions
+ trajectory_loadpath = './ur10_omega_trajectory.csv'
+ data = np.genfromtxt(trajectory_loadpath, delimiter=',')
+ self.time = data[:, 0]
+ self.time = self.time.reshape(1, len(self.time))
+ self.y = np.array(data[:, 1:]).T
+
+ def reset(self):
+ self.x = np.vstack((np.zeros((self.n, 1)), self.y0, 1.))
+ self.tau = self.tau0
+ self.theta = 0
+ self.pos = self.y0
+ self.vel = 0 * self.y0
+ self.acc = 0 * self.y0
+
+ def z(self, x=None):
+ if x is None:
+ x = self.x
+ return x[0:self.n]
+
+ def y_fun(self, x=None):
+ if x is None:
+ x = self.x
+ return x[self.n:2 * self.n]
+
+ def s(self, x=None):
+ if x is None:
+ x = self.x
+ return x[2 * self.n]
+
+ def set_goal(self, g):
+ self.g = g
+
+ def set_tau(self, tau):
+ self.tau = tau
+
+ def psi(self, s, i=None):
+ if i is not None and len(s) == 1:
+ return np.exp(-1 / (2 * self.widths[i] ** 2) * (s - self.centers[i]) ** 2)
+ if i is None:
+ return np.exp(-1 / (2 * self.widths ** 2) * (s - self.centers) ** 2)
+
+ def h(self, x=None):
+ if x is None:
+ x = self.x
+ psi = self.psi(self.s(x)).reshape((self.n_bfs, 1))
+ v = (self.w.dot(psi)) / np.maximum(np.sum(psi), 1e-8) * (self.g - self.y0) * self.s(x)
+ h = self.k * (self.g - self.y_fun(x)) - self.d * self.z(x) + v
+ return h
+
+ def f(self, x=None):
+ if x is None:
+ x = self.x
+ return np.vstack((self.h(x), self.z(x), -self.a_s * self.s(x)))
+
+ def step(self, dt):
+ # Update state
+ self.x = self.x + self.f() / self.tau * dt
+ self.theta = self.theta + 1 / self.tau * dt
+
+ # Extract trajectory state
+ vel_prev = self.vel
+ self.pos = self.y_fun()
+ self.vel = self.z() / self.tau
+ self.acc = (self.vel - vel_prev) / dt
+
+# if you don't know what the letters mean,
+# look at the equation (which you need to know anyway)
+ def fit(self):
+ # Set target trajectory parameters
+ self.n = self.y.shape[0]
+ self.y0 = self.y[:, 0].reshape((self.n, 1))
+ self.g = self.y[:, -1].reshape((self.n, 1))
+ self.tau0 = self.time[0, -1]
+
+ # Set basis functions
+ t_centers = np.linspace(0, self.tau0, self.n_bfs, endpoint=True)
+ self.centers = np.exp(-self.a_s / self.tau0 * t_centers)
+ widths = np.abs((np.diff(self.centers)))
+ self.widths = np.concatenate((widths, [widths[-1]]))
+
+ # Calculate derivatives
+ yd_demo = (self.y[:, 1:] - self.y[:, :-1]) / (self.time[0, 1:] - self.time[0, :-1])
+ yd_demo = np.concatenate((yd_demo, np.zeros((self.n, 1))), axis=1)
+ ydd_demo = (yd_demo[:, 1:] - yd_demo[:, :-1]) / (self.time[0, 1:] - self.time[0, :-1])
+ ydd_demo = np.concatenate((ydd_demo, np.zeros((self.n, 1))), axis=1)
+
+ # Compute weights
+ s_seq = np.exp(-self.a_s / self.tau0 * self.time)
+ self.w = np.zeros((self.n, self.n_bfs))
+ for i in range(self.n):
+ if abs(self.g[i] - self.y0[i]) < 1e-5:
+ continue
+ f_gain = s_seq * (self.g[i] - self.y0[i])
+ f_target = self.tau0 ** 2 * ydd_demo[i, :] - self.k * (
+ self.g[i] - self.y[i, :]) + self.d * self.tau0 * yd_demo[i, :]
+ for j in range(self.n_bfs):
+ psi_j = self.psi(s_seq, j)
+ num = f_gain.dot((psi_j * f_target).T)
+ den = f_gain.dot((psi_j * f_gain).T)
+ if abs(den) < 1e-6:
+ continue
+ self.w[i, j] = num / den
+
+ # Reset state
+ self.reset()
diff --git a/dmp/my_sol/generate_traj_from_screen.py b/dmp/my_sol/generate_traj_from_screen.py
new file mode 100644
index 0000000..7e9b82a
--- /dev/null
+++ b/dmp/my_sol/generate_traj_from_screen.py
@@ -0,0 +1,10 @@
+# we're doing this last,
+# just run with a predefined trajectory until then.
+# afterward copy the functionality of the matlab part.
+# you can do it with one of the tkinter/matplotlib examples you found (and saved)
+# while working on the wasp_ivc project
+
+# the output will be a csv with timestamps and joint angles q
+# if required you can finite-difference the q into dq and ddq here
+# and shove that into the csv as well, doesn't really matter.
+# just comply with the example demo csv.
diff --git a/dmp/my_sol/robotiq_gripper.py b/dmp/my_sol/robotiq_gripper.py
new file mode 100644
index 0000000..a17ab18
--- /dev/null
+++ b/dmp/my_sol/robotiq_gripper.py
@@ -0,0 +1,291 @@
+"""Module to control Robotiq's grippers - tested with HAND-E"""
+
+import socket
+import threading
+import time
+from enum import Enum
+from typing import Union, Tuple, OrderedDict
+
+class RobotiqGripper:
+ """
+ Communicates with the gripper directly, via socket with string commands, leveraging string names for variables.
+ """
+ # WRITE VARIABLES (CAN ALSO READ)
+ ACT = 'ACT' # act : activate (1 while activated, can be reset to clear fault status)
+ GTO = 'GTO' # gto : go to (will perform go to with the actions set in pos, for, spe)
+ ATR = 'ATR' # atr : auto-release (emergency slow move)
+ ADR = 'ADR' # adr : auto-release direction (open(1) or close(0) during auto-release)
+ FOR = 'FOR' # for : force (0-255)
+ SPE = 'SPE' # spe : speed (0-255)
+ POS = 'POS' # pos : position (0-255), 0 = open
+ # READ VARIABLES
+ STA = 'STA' # status (0 = is reset, 1 = activating, 3 = active)
+ PRE = 'PRE' # position request (echo of last commanded position)
+ OBJ = 'OBJ' # object detection (0 = moving, 1 = outer grip, 2 = inner grip, 3 = no object at rest)
+ FLT = 'FLT' # fault (0=ok, see manual for errors if not zero)
+
+ ENCODING = 'UTF-8' # ASCII and UTF-8 both seem to work
+
+ class GripperStatus(Enum):
+ """Gripper status reported by the gripper. The integer values have to match what the gripper sends."""
+ RESET = 0
+ ACTIVATING = 1
+ # UNUSED = 2 # This value is currently not used by the gripper firmware
+ ACTIVE = 3
+
+ class ObjectStatus(Enum):
+ """Object status reported by the gripper. The integer values have to match what the gripper sends."""
+ MOVING = 0
+ STOPPED_OUTER_OBJECT = 1
+ STOPPED_INNER_OBJECT = 2
+ AT_DEST = 3
+
+ def __init__(self):
+ """Constructor."""
+ self.socket = None
+ self.command_lock = threading.Lock()
+ self._min_position = 0
+ self._max_position = 255
+ self._min_speed = 0
+ self._max_speed = 255
+ self._min_force = 0
+ self._max_force = 255
+
+ def connect(self, hostname: str, port: int, socket_timeout: float = 2.0) -> None:
+ """Connects to a gripper at the given address.
+ :param hostname: Hostname or ip.
+ :param port: Port.
+ :param socket_timeout: Timeout for blocking socket operations.
+ """
+ self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+ self.socket.connect((hostname, port))
+ self.socket.settimeout(socket_timeout)
+
+ def disconnect(self) -> None:
+ """Closes the connection with the gripper."""
+ self.socket.close()
+
+ def _set_vars(self, var_dict: OrderedDict[str, Union[int, float]]):
+ """Sends the appropriate command via socket to set the value of n variables, and waits for its 'ack' response.
+ :param var_dict: Dictionary of variables to set (variable_name, value).
+ :return: True on successful reception of ack, false if no ack was received, indicating the set may not
+ have been effective.
+ """
+ # construct unique command
+ cmd = "SET"
+ for variable, value in var_dict.items():
+ cmd += f" {variable} {str(value)}"
+ cmd += '\n' # new line is required for the command to finish
+ # atomic commands send/rcv
+ with self.command_lock:
+ self.socket.sendall(cmd.encode(self.ENCODING))
+ data = self.socket.recv(1024)
+ return self._is_ack(data)
+
+ def _set_var(self, variable: str, value: Union[int, float]):
+ """Sends the appropriate command via socket to set the value of a variable, and waits for its 'ack' response.
+ :param variable: Variable to set.
+ :param value: Value to set for the variable.
+ :return: True on successful reception of ack, false if no ack was received, indicating the set may not
+ have been effective.
+ """
+ return self._set_vars(OrderedDict([(variable, value)]))
+
+ def _get_var(self, variable: str):
+ """Sends the appropriate command to retrieve the value of a variable from the gripper, blocking until the
+ response is received or the socket times out.
+ :param variable: Name of the variable to retrieve.
+ :return: Value of the variable as integer.
+ """
+ # atomic commands send/rcv
+ with self.command_lock:
+ cmd = f"GET {variable}\n"
+ self.socket.sendall(cmd.encode(self.ENCODING))
+ data = self.socket.recv(1024)
+
+ # expect data of the form 'VAR x', where VAR is an echo of the variable name, and X the value
+ # note some special variables (like FLT) may send 2 bytes, instead of an integer. We assume integer here
+ var_name, value_str = data.decode(self.ENCODING).split()
+ if var_name != variable:
+ raise ValueError(f"Unexpected response {data} ({data.decode(self.ENCODING)}): does not match '{variable}'")
+ value = int(value_str)
+ return value
+
+ @staticmethod
+ def _is_ack(data: str):
+ return data == b'ack'
+
+ def _reset(self):
+ """
+ Reset the gripper.
+ The following code is executed in the corresponding script function
+ def rq_reset(gripper_socket="1"):
+ rq_set_var("ACT", 0, gripper_socket)
+ rq_set_var("ATR", 0, gripper_socket)
+
+ while(not rq_get_var("ACT", 1, gripper_socket) == 0 or not rq_get_var("STA", 1, gripper_socket) == 0):
+ rq_set_var("ACT", 0, gripper_socket)
+ rq_set_var("ATR", 0, gripper_socket)
+ sync()
+ end
+
+ sleep(0.5)
+ end
+ """
+ self._set_var(self.ACT, 0)
+ self._set_var(self.ATR, 0)
+ while (not self._get_var(self.ACT) == 0 or not self._get_var(self.STA) == 0):
+ self._set_var(self.ACT, 0)
+ self._set_var(self.ATR, 0)
+ time.sleep(0.5)
+
+
+ def activate(self, auto_calibrate: bool = True):
+ """Resets the activation flag in the gripper, and sets it back to one, clearing previous fault flags.
+ :param auto_calibrate: Whether to calibrate the minimum and maximum positions based on actual motion.
+ The following code is executed in the corresponding script function
+ def rq_activate(gripper_socket="1"):
+ if (not rq_is_gripper_activated(gripper_socket)):
+ rq_reset(gripper_socket)
+
+ while(not rq_get_var("ACT", 1, gripper_socket) == 0 or not rq_get_var("STA", 1, gripper_socket) == 0):
+ rq_reset(gripper_socket)
+ sync()
+ end
+
+ rq_set_var("ACT",1, gripper_socket)
+ end
+ end
+ def rq_activate_and_wait(gripper_socket="1"):
+ if (not rq_is_gripper_activated(gripper_socket)):
+ rq_activate(gripper_socket)
+ sleep(1.0)
+
+ while(not rq_get_var("ACT", 1, gripper_socket) == 1 or not rq_get_var("STA", 1, gripper_socket) == 3):
+ sleep(0.1)
+ end
+
+ sleep(0.5)
+ end
+ end
+ """
+ if not self.is_active():
+ self._reset()
+ while (not self._get_var(self.ACT) == 0 or not self._get_var(self.STA) == 0):
+ time.sleep(0.01)
+
+ self._set_var(self.ACT, 1)
+ time.sleep(1.0)
+ while (not self._get_var(self.ACT) == 1 or not self._get_var(self.STA) == 3):
+ time.sleep(0.01)
+
+ # auto-calibrate position range if desired
+ if auto_calibrate:
+ self.auto_calibrate()
+
+ def is_active(self):
+ """Returns whether the gripper is active."""
+ status = self._get_var(self.STA)
+ return RobotiqGripper.GripperStatus(status) == RobotiqGripper.GripperStatus.ACTIVE
+
+ def get_min_position(self) -> int:
+ """Returns the minimum position the gripper can reach (open position)."""
+ return self._min_position
+
+ def get_max_position(self) -> int:
+ """Returns the maximum position the gripper can reach (closed position)."""
+ return self._max_position
+
+ def get_open_position(self) -> int:
+ """Returns what is considered the open position for gripper (minimum position value)."""
+ return self.get_min_position()
+
+ def get_closed_position(self) -> int:
+ """Returns what is considered the closed position for gripper (maximum position value)."""
+ return self.get_max_position()
+
+ def is_open(self):
+ """Returns whether the current position is considered as being fully open."""
+ return self.get_current_position() <= self.get_open_position()
+
+ def is_closed(self):
+ """Returns whether the current position is considered as being fully closed."""
+ return self.get_current_position() >= self.get_closed_position()
+
+ def get_current_position(self) -> int:
+ """Returns the current position as returned by the physical hardware."""
+ return self._get_var(self.POS)
+
+ def auto_calibrate(self, log: bool = True) -> None:
+ """Attempts to calibrate the open and closed positions, by slowly closing and opening the gripper.
+ :param log: Whether to print the results to log.
+ """
+ # first try to open in case we are holding an object
+ (position, status) = self.move_and_wait_for_pos(self.get_open_position(), 64, 1)
+ if RobotiqGripper.ObjectStatus(status) != RobotiqGripper.ObjectStatus.AT_DEST:
+ raise RuntimeError(f"Calibration failed opening to start: {str(status)}")
+
+ # try to close as far as possible, and record the number
+ (position, status) = self.move_and_wait_for_pos(self.get_closed_position(), 64, 1)
+ if RobotiqGripper.ObjectStatus(status) != RobotiqGripper.ObjectStatus.AT_DEST:
+ raise RuntimeError(f"Calibration failed because of an object: {str(status)}")
+ assert position <= self._max_position
+ self._max_position = position
+
+ # try to open as far as possible, and record the number
+ (position, status) = self.move_and_wait_for_pos(self.get_open_position(), 64, 1)
+ if RobotiqGripper.ObjectStatus(status) != RobotiqGripper.ObjectStatus.AT_DEST:
+ raise RuntimeError(f"Calibration failed because of an object: {str(status)}")
+ assert position >= self._min_position
+ self._min_position = position
+
+ if log:
+ print(f"Gripper auto-calibrated to [{self.get_min_position()}, {self.get_max_position()}]")
+
+ def move(self, position: int, speed: int, force: int) -> Tuple[bool, int]:
+ """Sends commands to start moving towards the given position, with the specified speed and force.
+ :param position: Position to move to [min_position, max_position]
+ :param speed: Speed to move at [min_speed, max_speed]
+ :param force: Force to use [min_force, max_force]
+ :return: A tuple with a bool indicating whether the action it was successfully sent, and an integer with
+ the actual position that was requested, after being adjusted to the min/max calibrated range.
+ """
+
+ def clip_val(min_val, val, max_val):
+ return max(min_val, min(val, max_val))
+
+ clip_pos = clip_val(self._min_position, position, self._max_position)
+ clip_spe = clip_val(self._min_speed, speed, self._max_speed)
+ clip_for = clip_val(self._min_force, force, self._max_force)
+
+ # moves to the given position with the given speed and force
+ var_dict = OrderedDict([(self.POS, clip_pos), (self.SPE, clip_spe), (self.FOR, clip_for), (self.GTO, 1)])
+ return self._set_vars(var_dict), clip_pos
+
+ def move_and_wait_for_pos(self, position: int, speed: int, force: int) -> Tuple[int, ObjectStatus]: # noqa
+ """Sends commands to start moving towards the given position, with the specified speed and force, and
+ then waits for the move to complete.
+ :param position: Position to move to [min_position, max_position]
+ :param speed: Speed to move at [min_speed, max_speed]
+ :param force: Force to use [min_force, max_force]
+ :return: A tuple with an integer representing the last position returned by the gripper after it notified
+ that the move had completed, a status indicating how the move ended (see ObjectStatus enum for details). Note
+ that it is possible that the position was not reached, if an object was detected during motion.
+ """
+ set_ok, cmd_pos = self.move(position, speed, force)
+ if not set_ok:
+ raise RuntimeError("Failed to set variables for move.")
+
+ # wait until the gripper acknowledges that it will try to go to the requested position
+ while self._get_var(self.PRE) != cmd_pos:
+ time.sleep(0.001)
+
+ # wait until not moving
+ cur_obj = self._get_var(self.OBJ)
+ while RobotiqGripper.ObjectStatus(cur_obj) == RobotiqGripper.ObjectStatus.MOVING:
+ cur_obj = self._get_var(self.OBJ)
+
+ # report the actual position and the object status
+ final_pos = self._get_var(self.POS)
+ final_obj = cur_obj
+ return final_pos, RobotiqGripper.ObjectStatus(final_obj)
\ No newline at end of file
diff --git a/dmp/my_sol/run_dmp.py b/dmp/my_sol/run_dmp.py
new file mode 100644
index 0000000..171fa7f
--- /dev/null
+++ b/dmp/my_sol/run_dmp.py
@@ -0,0 +1,117 @@
+from create_dmp import DMP
+from rtde_control import RTDEControlInterface as RTDEControl
+from rtde_receive import RTDEReceiveInterface as RTDEReceive
+from robotiq_gripper import RobotiqGripper
+import pinocchio as pin
+import numpy as np
+import os
+import time
+import signal
+import matplotlib.pyplot as plt
+
+
+#urdf_path_relative = "../../robot_descriptions/urdf/ur5e_with_robotiq_hande.urdf"
+#urdf_path_absolute = os.path.abspath(urdf_path_relative)
+#mesh_dir = "../../robot_descriptions/"
+#mesh_dir_absolute = os.path.abspath(mesh_dir)
+##print(mesh_dir_absolute)
+#model = pin.buildModelFromUrdf(urdf_path_absolute)
+##print(model)
+#data = pin.Data(model)
+##print(data)
+
+rtde_control = RTDEControl("192.168.1.102")
+rtde_receive = RTDEReceive("192.168.1.102")
+#rtde_control = RTDEControl("127.0.0.1")
+#rtde_receive = RTDEReceive("127.0.0.1")
+
+N_ITER = 10000
+qs = np.zeros((N_ITER, 6))
+dmp_poss = np.zeros((N_ITER, 6))
+dqs = np.zeros((N_ITER, 6))
+dmp_vels = np.zeros((N_ITER, 6))
+
+def handler(signum, frame):
+ print('sending 100 speedjs full of zeros')
+ for i in range(100):
+ rtde_control.endFreedriveMode()
+ vel_cmd = np.zeros(6)
+ rtde_control.speedJ(vel_cmd, 0.1, 1.0 / 500)
+
+ print('also plotting')
+ time = np.arange(N_ITER)
+ ax_q = plt.subplot(221)
+ ax_dq = plt.subplot(222)
+ ax_dmp_q = plt.subplot(223)
+ ax_dmp_dq = plt.subplot(224)
+ for i in range(5):
+ ax_q.plot(time, qs[:,i], color='blue')
+ ax_dq.plot(time, dqs[:,i], color='red')
+ ax_dmp_q.plot(time, dmp_poss[:,i], color='green')
+ ax_dmp_dq.plot(time, dmp_vels[:,i], color='orange')
+ # for labels
+ ax_q.plot(time, qs[:,5], color='blue', label='q')
+ ax_q.legend()
+ ax_dq.plot(time, dqs[:,5], color='red', label='dq')
+ ax_dq.legend()
+ ax_dmp_q.plot(time, dmp_poss[:,5], color='green', label='dmp_q')
+ ax_dmp_q.legend()
+ ax_dmp_dq.plot(time, dmp_vels[:,5], color='orange', label='dmp_dq')
+ ax_dmp_dq.legend()
+ plt.show()
+ exit()
+# if not rtde_control.isProgramRunning():
+# exit()
+
+signal.signal(signal.SIGINT, handler)
+dmp = DMP()
+rtde_control.moveJ(dmp.pos.reshape((6,)))
+# TODO check that you're there instead of a sleep
+#time.sleep(3)
+
+#exit()
+update_rate = 500
+dt = 1.0 / update_rate
+JOINT_ID = 6
+
+kp = 100
+acceleration = 0.15
+
+print("starting the trajectory")
+for i in range(N_ITER):
+ start = time.time()
+#while True:
+ dmp.step(dt)
+ q = np.array(rtde_receive.getActualQ())
+ dq = np.array(rtde_receive.getActualQd())
+ #print("dq:", dq)
+ vel_cmd = dmp.vel + kp * (dmp.pos - q.reshape((6,1)))
+ #print("dmp.vel", dmp.vel.reshape((6,)))
+ #print("dmp.pos", dmp.pos.reshape((6,)))
+ #print("q:", q)
+ #print("vel_cmd", vel_cmd.reshape((6,)))
+ vel_cmd = np.clip(vel_cmd, -2.0, 2.0)
+ if np.isnan(vel_cmd[0][0]):
+ break
+ exit()
+ #rtde_control.speedJ(vel_cmd, 0.1, 1.0 / 500)
+ rtde_control.speedJ(vel_cmd, acceleration, dt)
+
+ qs[i] = q.reshape((6,))
+ dmp_poss[i] = dmp.pos.reshape((6,))
+ dqs[i] = dq.reshape((6,))
+ dmp_vels[i] = dmp.vel.reshape((6,))
+ end = time.time()
+ diff = end - start
+ print(diff)
+ try:
+ time.sleep(dt - diff)
+ except ValueError:
+ print("it took", dt - (end - start), "more than", dt, "seconds")
+
+handler(None, None)
+
+ #print(vel_cmd[0][0])
+
+
+
diff --git a/dmp/my_sol/test_traj_w_movej.py b/dmp/my_sol/test_traj_w_movej.py
new file mode 100644
index 0000000..83e2eb2
--- /dev/null
+++ b/dmp/my_sol/test_traj_w_movej.py
@@ -0,0 +1,35 @@
+from create_dmp import DMP
+from rtde_control import RTDEControlInterface as RTDEControl
+from rtde_receive import RTDEReceiveInterface as RTDEReceive
+from robotiq_gripper import RobotiqGripper
+import pinocchio as pin
+import numpy as np
+import os
+import time
+import signal
+import matplotlib.pyplot as plt
+
+
+#urdf_path_relative = "../../robot_descriptions/urdf/ur5e_with_robotiq_hande.urdf"
+#urdf_path_absolute = os.path.abspath(urdf_path_relative)
+#mesh_dir = "../../robot_descriptions/"
+#mesh_dir_absolute = os.path.abspath(mesh_dir)
+##print(mesh_dir_absolute)
+#model = pin.buildModelFromUrdf(urdf_path_absolute)
+##print(model)
+#data = pin.Data(model)
+##print(data)
+
+rtde_control = RTDEControl("192.168.1.102")
+rtde_receive = RTDEReceive("192.168.1.102")
+
+trajectory_loadpath = './ur10_omega_trajectory.csv'
+data = np.genfromtxt(trajectory_loadpath, delimiter=',')
+time = data[:, 0]
+time = time.reshape(1, len(time))
+y = np.array(data[:, 1:])
+
+for i in range(len(y)):
+ if i % 100 == 0:
+ rtde_control.moveJ(y[i])
+
diff --git a/dmp/my_sol/test_traj_w_speedj.py b/dmp/my_sol/test_traj_w_speedj.py
new file mode 100644
index 0000000..c00dc8e
--- /dev/null
+++ b/dmp/my_sol/test_traj_w_speedj.py
@@ -0,0 +1,42 @@
+from create_dmp import DMP
+from rtde_control import RTDEControlInterface as RTDEControl
+from rtde_receive import RTDEReceiveInterface as RTDEReceive
+from robotiq_gripper import RobotiqGripper
+import pinocchio as pin
+import numpy as np
+import os
+import time
+import signal
+import matplotlib.pyplot as plt
+
+
+#urdf_path_relative = "../../robot_descriptions/urdf/ur5e_with_robotiq_hande.urdf"
+#urdf_path_absolute = os.path.abspath(urdf_path_relative)
+#mesh_dir = "../../robot_descriptions/"
+#mesh_dir_absolute = os.path.abspath(mesh_dir)
+##print(mesh_dir_absolute)
+#model = pin.buildModelFromUrdf(urdf_path_absolute)
+##print(model)
+#data = pin.Data(model)
+##print(data)
+
+rtde_control = RTDEControl("192.168.1.102")
+rtde_receive = RTDEReceive("192.168.1.102")
+
+trajectory_loadpath = './ur10_omega_trajectory.csv'
+data = np.genfromtxt(trajectory_loadpath, delimiter=',')
+t = data[:, 0]
+t = t.reshape(1, len(t))
+y = np.array(data[:, 1:]).T
+
+n = y.shape[0]
+yd_demo = (y[:, 1:] - y[:, :-1]) / (t[0, 1:] - t[0, :-1])
+yd_demo = np.concatenate((yd_demo, np.zeros((n, 1))), axis=1)
+#print(yd_demo)
+rtde_control.moveJ(y[:,0])
+for i in range(yd_demo.shape[1]):
+ start = time.time()
+ rtde_control.speedJ(y[:,i], 0.1, 1/500)
+ end = time.time()
+ diff = end - start
+ time.sleep(diff)
diff --git a/dmp/my_sol/ur10_omega_trajectory.csv b/dmp/my_sol/ur10_omega_trajectory.csv
new file mode 100644
index 0000000..98f354a
--- /dev/null
+++ b/dmp/my_sol/ur10_omega_trajectory.csv
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diff --git a/dmp/notes.md b/dmp/notes.md
new file mode 100644
index 0000000..35e852d
--- /dev/null
+++ b/dmp/notes.md
@@ -0,0 +1,59 @@
+# pixel trajectory:
+-----------------
+have your pixels in a 2D array of length N points : ((x0,y0), (x1,y1),...(xN,yN))
+time is just np.arange(N).
+these guys are relatively sparse, ~150 points.
+in the program the distribution is dependent on how fast you move your mouse,
+which is why i assume means it's not of great importance as long as it
+covers everything reasonably
+
+# 3D cartesian trajectory:
+-------------------------
+just put your pixels onto a plane onto a predefined place with the right coordinate transform.
+ofc you have the pixel -> mm conversion and mins and maxes. whatever makes sense workspace-wise is good.
+
+# timed joint trajectory
+-----------------------------
+the UR matlab module just magically calculates what's needed.
+thus just running clik along the points to get joint angles for each point is almost certainly fine,
+although can be probably made better or worse.
+once you got q(t), you just do the most basic finite-difference
+to get q_dot(t) and q_ddot(t).
+that's it as far as trajectory generation is concerned.
+the result in the end is a
+struct q_traj{
+ t : 1 x N;
+ pos: 6 x N;
+ vel: 6 x N;
+ acc: 6 x N
+}
+how to read this from the .mat if you leave it this way idk.
+but you'll almost certainly want to replicate this code anyway so who cares.
+
+# dmp-ing the trajectory
+--------------------------
+
+- idea type beat: we're doing it in joint space, but since it's just a demo,
+doing it with speedl might be easier (it's not like
+the damped pseudoinverse is peak performance, let's be real)
+
+
+# control loop around trajectory following
+-------------------------------------------
+it's literally just feedforward position and feedback velocity
+```python
+def compute_ctrl(self, target_state, robot_state):
+ ff = 0.
+ fb = 0.
+ target_position = np.array(target_state.position)
+ target_velocity = np.array(target_state.velocity)
+ robot_position = np.array(robot_state.position)
+ if target_velocity.size > 0:
+ ff = target_velocity
+ if target_position.size > 0:
+ fb = self.kp * (target_position - robot_position)
+ vel_cmd = ff + fb
+ np.clip(vel_cmd, -self.vel_max, self.vel_max, out=vel_cmd)
+
+ return vel_cmd
+```
diff --git a/dmp/pdfs/neco_a_00393.pdf b/dmp/pdfs/neco_a_00393.pdf
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diff --git a/dmp/traj_scaling_node b/dmp/traj_scaling_node
deleted file mode 100755
index e4e6aca..0000000
--- a/dmp/traj_scaling_node
+++ /dev/null
@@ -1,113 +0,0 @@
-#!/usr/bin/env python
-
-from motion_planning.online_traj_scaling import GMRwOA, TrajectoryGenerator
-import numpy as np
-import rospy
-from robot_ctrl.msg import CtrlTarget
-from std_msgs.msg import Float64, String
-from motion_planning.msg import TrajGenState
-
-
-class TrajGenNode:
- def __init__(self):
- self.active = False
- self.rate = None
- self.dt = None
- self.traj_gen = None
-
- # Load parameters from server
- if not self.load_params():
- rospy.logwarn("Trajectory Generator not initialized")
-
- # Setup ros topics
- self.target_pub = rospy.Publisher("robot_ctrl/command", CtrlTarget, queue_size=1)
- self.state_pub = rospy.Publisher("motion_planning/state", TrajGenState, queue_size=1)
- rospy.Subscriber("motion_planning/command", String, self.command_parser)
-
- # Go into spin
- self.spin()
-
- def load_params(self):
- parameters = ['x0', 's_end', 'priors', 'mu', 'sigma', 'o_c', 'o_r']
- if not rospy.has_param('motion_planning/update_rate'):
- rospy.logerr("Parameter " + rospy.get_namespace() + "motion_planning/update_rate not on parameter server.")
- return False
- update_rate = rospy.get_param('motion_planning/update_rate')
- self.rate = rospy.Rate(update_rate)
- params = {}
- for i in range(len(parameters)):
- if not rospy.has_param('motion_planning/' + parameters[i]):
- rospy.logerr("Parameter " + rospy.get_namespace() + "motion_planning/" + parameters[i] + " not on parameter server.")
- return False
- params[parameters[i]] = rospy.get_param('motion_planning/' + parameters[i])
- gmr = GMRwOA(params)
- self.traj_gen = TrajectoryGenerator(gmr)
- return True
-
- def command_parser(self, msg):
- if msg.data == 'start':
- self.active = True
- elif msg.data == 'stop':
- self.active = False
- elif msg.data == 'reset':
- self.traj_gen.reset()
- self.active = False
- rospy.loginfo("Reset motion_planner.")
- elif msg.data == 'step':
- self.traj_gen.step(self.dt)
- elif msg.data == 'load_params':
- self.load_params()
- rospy.loginfo("Reloaded motion_planner parameters.")
- else:
- rospy.loginfo("Invalid command string for motion planner.")
-
- # spin -----------------------------------------------------
- # -----------------------------------------------------------
- def spin(self):
- print_flag = True
- while not rospy.is_shutdown():
-
- if self.active:
- # Step DMP
- self.traj_gen.step(self.dt)
- # Send target trajectory state command
- pub_msg = CtrlTarget()
- pub_msg.header.stamp = rospy.Time.now()
- pub_msg.acceleration = self.traj_gen.acc
- pub_msg.velocity = self.traj_gen.vel
- pub_msg.position = self.traj_gen.pos
- self.target_pub.publish(pub_msg)
- else:
- print_flag = True
-
- # Publish DMP state command
- pub_msg = TrajGenState()
- pub_msg.header.stamp = rospy.Time.now()
- pub_msg.acceleration = [0.0032*self.traj_gen.acc[0], 0, 0.0026*self.traj_gen.acc[1]]
- pub_msg.velocity = [0.0032*self.traj_gen.vel[0], 0, 0.0026*self.traj_gen.vel[1]]
- pub_msg.position = [0.0032*self.traj_gen.pos[0], 0, 0.0026*self.traj_gen.pos[1]+0.7]
- pub_msg.s = self.traj_gen.s
- pub_msg.s_dot = self.traj_gen.ds
- self.state_pub.publish(pub_msg)
-
- # Notify when DMP trajectory is finished
- if print_flag and self.traj_gen.s > self.traj_gen.dyn_sys.s_end:
- rospy.loginfo("Trajectory finished.")
- print_flag = False
-
- # Sleep
- self.rate.sleep()
-
-
-# --------------------------------------------------------------------
-# Here is the main entry point
-if __name__ == '__main__':
- try:
- # Init the node:
- rospy.init_node('TrajGenNode')
-
- # Initializing and continue running the main class:
- TrajGenNode()
-
- except rospy.ROSInterruptException:
- pass
diff --git a/dmp/vel_ff_pos_fb_node b/dmp/vel_ff_pos_fb_node
new file mode 100755
index 0000000..d003395
--- /dev/null
+++ b/dmp/vel_ff_pos_fb_node
@@ -0,0 +1,105 @@
+#!/usr/bin/env python
+
+import rospy
+import numpy as np
+from sensor_msgs.msg import JointState
+from std_msgs.msg import Empty
+from robot_ctrl.msg import CtrlTarget
+
+class VelFFPosFB:
+ def __init__(self, kp, vel_max):
+ # Controller parameters:
+ self.kp = kp
+ self.vel_max = vel_max
+ # Command state
+ self.vel_cmd = None
+
+ def compute_ctrl(self, target_state, robot_state):
+ ff = 0.
+ fb = 0.
+ target_position = np.array(target_state.position)
+ target_velocity = np.array(target_state.velocity)
+ robot_position = np.array(robot_state.position)
+ if target_velocity.size > 0:
+ ff = target_velocity
+ if target_position.size > 0:
+ fb = self.kp * (target_position - robot_position)
+ vel_cmd = ff + fb
+ np.clip(vel_cmd, -self.vel_max, self.vel_max, out=vel_cmd)
+
+ return vel_cmd
+
+
+class VelFFPosFBNode:
+ def __init__(self):
+ self.ctrl = None
+ self.rate = None
+ self.max_msg_deadtime = 0.1
+ self.robot_state = CtrlTarget()
+ self.target_state = CtrlTarget()
+
+ if not self.load_params(0):
+ exit(1)
+
+ self.vel_cmd_publisher = rospy.Publisher("robot_interface/vel_command", CtrlTarget, queue_size=1)
+ rospy.Subscriber("robot_interface/joint_states", JointState, self.state_update)
+ rospy.Subscriber("robot_ctrl/command", CtrlTarget, self.target_update)
+ rospy.Subscriber("robot_ctrl/reload_params", Empty, self.load_params)
+
+ self.spin()
+
+ def load_params(self,dummydata):
+ # Check necessary parameter existences
+ if not rospy.has_param('robot_ctrl/update_rate'):
+ rospy.logerr("Parameter " + rospy.get_namespace() + "robot_ctrl/update_rate not set.")
+ return False
+ if not rospy.has_param('robot_ctrl/kp'):
+ rospy.logerr("Parameter " + rospy.get_namespace() + "robot_ctrl/kp not set.")
+ return False
+ if not rospy.has_param('robot_ctrl/vel_max'):
+ rospy.logerr("Parameter " + rospy.get_namespace() + "robot_ctrl/vel_max not set.")
+ return False
+ # Load parameters from server
+ self.rate = rospy.Rate(rospy.get_param('robot_ctrl/update_rate'))
+ kp = rospy.get_param('robot_ctrl/kp')
+ vel_max = np.array(rospy.get_param('robot_ctrl/vel_max'))
+ # Create control object
+ self.ctrl = VelFFPosFB(kp, vel_max)
+ rospy.loginfo("Loaded robot_ctrl parameters.")
+ return True
+
+ def state_update(self, msg):
+ self.robot_state = msg
+
+ def target_update(self, msg):
+ self.target_state = msg
+ self.target_state.header.stamp = rospy.Time.now()
+
+ def spin(self):
+ while (not rospy.is_shutdown()):
+ target_msg_deadtime = (rospy.Time.now() - self.target_state.header.stamp).to_sec()
+ robot_msg_deadtime = (rospy.Time.now() - self.robot_state.header.stamp).to_sec()
+ if target_msg_deadtime < self.max_msg_deadtime and robot_msg_deadtime < self.max_msg_deadtime:
+ # Compute velocity command
+ vel_cmd = self.ctrl.compute_ctrl(self.target_state, self.robot_state)
+
+ # publish joint velocities command
+ cmd_msg = CtrlTarget()
+ cmd_msg.header.stamp = rospy.Time.now()
+ cmd_msg.velocity = vel_cmd
+ self.vel_cmd_publisher.publish(cmd_msg)
+
+ self.rate.sleep()
+
+
+# Here is the main entry point
+if __name__ == '__main__':
+ try:
+ # Init the node
+ rospy.init_node('VelFFPosFBNode')
+
+ # Initializing and continue running the main class
+ VelFFPosFBNode()
+
+ except rospy.ROSInterruptException:
+ pass
\ No newline at end of file
diff --git a/docs/scriptManual-3.5.4.pdf b/docs/scriptManual-3.5.4.pdf
new file mode 100644
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diff --git a/util/freedrive.py b/util/freedrive.py
new file mode 100644
index 0000000..c0f7534
--- /dev/null
+++ b/util/freedrive.py
@@ -0,0 +1,22 @@
+from rtde_control import RTDEControlInterface as RTDEControl
+from rtde_receive import RTDEReceiveInterface as RTDEReceive
+import time
+import signal
+
+def handler(signum, frame):
+ print('i will end freedrive and exit')
+ rtde_control.endFreedriveMode()
+ exit()
+
+
+rtde_control = RTDEControl("192.168.1.102")
+rtde_receive = RTDEReceive("192.168.1.102")
+
+rtde_control.freedriveMode()
+signal.signal(signal.SIGINT, handler)
+time.sleep(100)
+
+#time.sleep(1)
+#print(rtde_control.getFreedriveStatus())
+#rtde_control.endFreedriveMode()
+#print("done")
--
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