RateTunableCBFs

This repository supplements our paper Rate-Tunable Control Barrier Functions: Methods and Algorithms for Online Adaptation . Simulation videos are provided below. More instructions on how to run the code will be added soon!

Simulation Results

The ego agent (red) navigates to its goal location while performing collision avoidance with other agents (black).

standard_animation.mp4

Proposed method

The proposed method is able to adapt the parameters and maintain feasibility of controller at all times

proposed_animation.mp4

Name		Name	Last commit message	Last commit date
Latest commit History 74 Commits
bicycle_2d		bicycle_2d
docker_files		docker_files
images		images
leader_follower		leader_follower
notebooks		notebooks
robot_models		robot_models
rt_simple_feasibility		rt_simple_feasibility
scenario1		scenario1
si_2d		si_2d
uav_2d		uav_2d
unicycle_2d		unicycle_2d
utils		utils
.gitignore		.gitignore
README.md		README.md
beast_setup.sh		beast_setup.sh
case1_rc.mp4		case1_rc.mp4
live_animation.py		live_animation.py
mpc.npy		mpc.npy
mpc.py		mpc.py
mpc_cbf_sqp_offline.py		mpc_cbf_sqp_offline.py
scenario1_centralized_mpc.py		scenario1_centralized_mpc.py
scenario1_centralized_predictive.py		scenario1_centralized_predictive.py
scenario1_decentralized_predictive.py		scenario1_decentralized_predictive.py
scenario1_mpc.py		scenario1_mpc.py
setup.sh		setup.sh
test.mp4		test.mp4
test_DI_barrier.py		test_DI_barrier.py
test_unicycle_barrier.py		test_unicycle_barrier.py
trust_utils.py		trust_utils.py