This python package enables learning of robot trajectories without exceeding limits on the position, velocity, acceleration and jerk of each robot joint.
Movements are generated by mapping the predictions of a neural network to safely executable joint accelerations. The time between network predictions must be constant, but can be chosen arbitrarily.
Our method ensures that the kinematic constraints are never in conflict, which means that there is at least one safely executable joint acceleration at any time.
This package provides the code to compute the range of safely executable joint accelerations.
The package can be installed by running
pip install klimits
To generate a random trajectory with limited jerk, acceleration, velocity and position run
python -m klimits.test_trajectory_generation
Several parameters can be adjusted to modify the generated trajectory. E.g:
python -m klimits.test_trajectory_generation --time_step=0.1 --pos_limits='[[-2.96705972839, 2.96705972839], [-2.09439510239, 2.09439510239]]' --vel_limits='[[-1.71042266695, 1.71042266695], [-1.71042266695, 1.71042266695]]' --acc_limits='[[-15, 15], [-7.5, 7.5]]' --plot_joint='[1, 0]' --pos_limit_factor=0.9 --vel_limit_factor=0.8 --acc_limit_factor=0.7 --jerk_limit_factor=0.6 --trajectory_duration=20 --plot_safe_acc_limits
Run
python -m klimits.test_trajectory_generation --help
for further details on the optional arguments.
A preprint of the corresponding ICRA 2021 publication is available at arXiv.org.
Further information on the implementation can be found here.
This library is used by safeMotions to learn collision-free reaching tasks via reinforcement learning.
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