hybridrobotics / cbf Goto Github PK

A/M. Let's convert this after submission.

Dear Dr. Thirugnanam
I have some code understanding problems. Very disturbing! Thank you very much!
dcbf_optimizer.py add_convex_to_convex_constraint function
What is the meaning and function of the following codes?
line 136 np.dot(robot_G, self.state.rotation().T)
line 137 np.dot(np.dot(robot_G, self.state.rotation().T), self.state.translation()) + robot_g
line 147 robot_R
line 163 robot_T
line 167 ca.mtimes((ca.mtimes(mat_A, robot_T) - vec_b).T, lamb[:, i])
line 171 ca.mtimes(ca.mtimes(robot_R.T, mat_A.T), lamb[:, i])
line 174 self.opti.subject_to(ca.mtimes(temp.T, temp) <= 1)

We need this feature for more general geometric model and challenging environment.

I have problems in formula derivation. Very disturbing! Thank you very much!
In Section II. B, the authors state “Note that, due to the relaxation variable \omega, enforcing multiple DCBF constraints for each hi is equivalent to enforcing a single DCBF constraint.” I am confused about this issue. Why the relaxation variable \omega leads to this case? Is the statement not true without the relaxation variable \omega?
In Section III. C, the authors state “Additionally, we can introduce the relaxation
variables without affecting the analysis in this section.” Why?

Mentioned by Sha, it shall be correctly implemented as follows on the right hand side,

omega_i * param.gamma^i *(cbf_curr - param.margin_dist) + param.margin_dist

I'm agreeing with it, how do you think about it? @AkshayThiru

We need to maintain an update-to-date readme for tons of features in this repository, which allows all collaborators to test and run examples and source code in a fast pace. I will take responsibility and this is intended to carried out after ICRA deadline.

Create readme for two duality papers and feasibility paper.

It would be interesting to evaluate behavior changes with respect to different hyperparameters and think about the a systematical way to handle this problem, initialized from Sha's issue.

Hello @junzengx14 @koushils @ayush-agrawal

Thanks for sharing this repo and your work in using CBF and CLF for the safety and stability of non-linear dynamic systems. I have read two of your papers, "Safety-Critical Control and Planning for Obstacle Avoidance between Polytopes with Control Barrier Functions" arxiv and "Enhancing Feasibility and Safety of Nonlinear Model Predictive Control with Discrete-Time Control Barrier Functions" IEEE.

Very interesting papers.

I am working on a similar project where the agent has discrete action space (left, right, forward etc.). As far as I understood, you have applied CBF-CLF in continuous action space (steering and acceleration).

Does this implementation of CBF also work for discrete action space?

I have a question about the code inside the function safe_dist(x, timestep, amin, amax, dist_margin) in kinematic_car.py.
Should the code:

brake_min_dist = (abs(x[2]) + amax * timestep) ** 2 / (2 * amax) + dist_margin

be changed to:

brake_min_dist = (abs(x[2]) + amax * timestep) ** 2 / (2 * abs(amin)) + dist_margin

even though in the current code amax = abs(amin)

am i right？

Add interfaces for system design for various solvers.

Dear Dr. Zeng,
I have some code problems. Very disturbing! Thank you very much!
I run this project in ubuntu18.04 and install required packages in python3.7. Is the following error caused by matplotlib's version?

The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
Traceback (most recent call last):
File "/home/highlight/cbf/models/kinematic_car_test.py", line 344, in
kinematic_car_all_shapes_simulation_test(maze_type, robot_shape)
File "/home/highlight/cbf/models/kinematic_car_test.py", line 257, in kinematic_car_all_shapes_simulation_test
plot_world(sim, robot_indexes, figure_name=name, local_traj_indexes=traj_indexes, maze_type=maze_type)
File "/home/highlight/cbf/models/kinematic_car_test.py", line 83, in plot_world
plt.savefig("figures/" + figure_name + ".eps", format="eps", dpi=500, pad_inches=0)
File "/home/highlight/anaconda3/envs/cbf/lib/python3.7/site-packages/matplotlib/pyplot.py", line 977, in savefig
res = fig.savefig(*args, **kwargs)
File "/home/highlight/anaconda3/envs/cbf/lib/python3.7/site-packages/matplotlib/figure.py", line 3058, in savefig
self.canvas.print_figure(fname, **kwargs)
File "/home/highlight/anaconda3/envs/cbf/lib/python3.7/site-packages/matplotlib/backend_bases.py", line 2325, in print_figure
**kwargs)
File "/home/highlight/anaconda3/envs/cbf/lib/python3.7/site-packages/matplotlib/_api/deprecation.py", line 415, in wrapper
return func(*inner_args, **inner_kwargs)
File "/home/highlight/anaconda3/envs/cbf/lib/python3.7/site-packages/matplotlib/backends/backend_ps.py", line 847, in print_eps
return self._print_ps(outfile, 'eps', **kwargs)
File "/home/highlight/anaconda3/envs/cbf/lib/python3.7/site-packages/matplotlib/_api/deprecation.py", line 389, in wrapper
return func(*inner_args, **inner_kwargs)
File "/home/highlight/anaconda3/envs/cbf/lib/python3.7/site-packages/matplotlib/backends/backend_ps.py", line 889, in _print_ps
orientation=orientation, papertype=papertype, **kwargs)
File "/home/highlight/anaconda3/envs/cbf/lib/python3.7/site-packages/matplotlib/backend_bases.py", line 1648, in wrapper
return func(*args, **kwargs)
File "/home/highlight/anaconda3/envs/cbf/lib/python3.7/site-packages/matplotlib/backends/backend_ps.py", line 1024, in _print_figure
with cbook.open_file_cm(outfile, "w", encoding="latin-1") as file:
File "/home/highlight/anaconda3/envs/cbf/lib/python3.7/site-packages/matplotlib/cbook/init.py", line 465, in open_file_cm
fh, opened = to_filehandle(path_or_file, mode, True, encoding)
File "/home/highlight/anaconda3/envs/cbf/lib/python3.7/site-packages/matplotlib/cbook/init.py", line 451, in to_filehandle
fh = open(fname, flag, encoding=encoding)
FileNotFoundError: [Errno 2] No such file or directory: 'figures/triangle_maze.eps'

Dear Dr. Thirugnanam:
I have problems in adding terminal condition constraints in dcbf optimizer such as the desired heading angle at goal position. Very disturbing! Thank you very much!