TY - GEN
T1 - Control Barrier Function Augmentation in Sampling-based Control Algorithm for Sample Efficiency
AU - Tao, Chuyuan
AU - Kim, Hunmin
AU - Yoon, Hyungjin
AU - Hovakimyan, Naira
AU - Voulgaris, Petros
N1 - Publisher Copyright:
© 2022 American Automatic Control Council.
PY - 2022
Y1 - 2022
N2 - For a nonlinear stochastic path planning problem, sampling-based algorithms generate thousands of random sample trajectories to find the optimal path while guaranteeing safety by Lagrangian penalty methods. However, the sampling-based algorithm can perform poorly in obstacle-rich environments because most samples might violate safety constraints, invalidating the corresponding samples. To improve the sample efficiency of sampling-based algorithms in cluttered environments, we propose an algorithm based on model predictive path integral control and control barrier function (CBF). The proposed algorithm needs fewer samples and time-steps and has a better performance in cluttered environments compared to the original model predictive path integral control algorithm.
AB - For a nonlinear stochastic path planning problem, sampling-based algorithms generate thousands of random sample trajectories to find the optimal path while guaranteeing safety by Lagrangian penalty methods. However, the sampling-based algorithm can perform poorly in obstacle-rich environments because most samples might violate safety constraints, invalidating the corresponding samples. To improve the sample efficiency of sampling-based algorithms in cluttered environments, we propose an algorithm based on model predictive path integral control and control barrier function (CBF). The proposed algorithm needs fewer samples and time-steps and has a better performance in cluttered environments compared to the original model predictive path integral control algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85138495997&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85138495997&partnerID=8YFLogxK
U2 - 10.23919/ACC53348.2022.9867832
DO - 10.23919/ACC53348.2022.9867832
M3 - Conference contribution
AN - SCOPUS:85138495997
T3 - Proceedings of the American Control Conference
SP - 3488
EP - 3493
BT - 2022 American Control Conference, ACC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 American Control Conference, ACC 2022
Y2 - 8 June 2022 through 10 June 2022
ER -