Managed by pipenv
.
$ pip install pipenv
$ pipenv install
pipenv run python convex_optimization_algorithms/primal_dual_interpoint/primal_dual_interpoint.py
pipenv run python convex_optimization_algorithms/active_set/active_set.py
A* based speed planning test in t-s space.
$ pipenv run python astar_velocity_planning/astar_speed_planner.py
- Black : Obstacle occupied area
- Red : Optimal path
- Gray : Candidates
calculate stop dist and generate csv.
$ pipenv run python stop_dist_calc_w_jerk_acc_constraint/calc_to_generate_csv.py
calculate stop dist and plot the result.
$ pipenv run python stop_dist_calc_w_jerk_acc_constraint/calc_with_plots.py
$ pipenv run python ./path_following_sim/sim_1d_time_delay.py
Path following error dynamics with time_delay: tau
and time_constant: d
-h
shows help for usage.
$ pipenv run python3 ./path_following_sim/sim_1d_time_delay.py -h
usage: sim_1d_time_delay.py [-h] [-D] [-v VELOCITY] [-d TIME_DELAY]
[-t TIME_CONSTANT] [-kp P_GAIN] [-kd D_GAIN]
optional arguments:
-h, --help show this help message and exit
-D, --sim_with_delay set if sim with delay time is needed
-v VELOCITY, --velocity VELOCITY
sim parameter: velocity
-d TIME_DELAY, --time_delay TIME_DELAY
sim parameter: delay time
-t TIME_CONSTANT, --time_constant TIME_CONSTANT
sim parameter: time constant
-kp P_GAIN, --p_gain P_GAIN
sim parameter: p gain
-kd D_GAIN, --d_gain D_GAIN
sim parameter: d gain