TY - JOUR
T1 - Adaptive High-Order Control Barrier Function-Based Iterative LQR for Real Time Safety-Critical Motion Planning
AU - Kong, Xiangyu
AU - Ning, Wentao
AU - Xia, Yuanqing
AU - Sun, Zhongqi
AU - Xie, Huahui
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2024/7/1
Y1 - 2024/7/1
N2 - This letter proposes an adaptive high-order control barrier function-based iterative linear quadratic regulator (AHOCBF-ILQR) algorithm for real time safety-critical motion planning. Firstly, we propose a HOCBF-ILQR method, where a HOCBF-based controller is designed as a safety filter of ILQR to guarantee safety. Then, to address the potential infeasibility issue in HOCBF-ILQR, AHOCBF-ILQR is proposed by introducing an auxiliary variable. In AHOCBF-ILQR, only an unconstrained optimization problem and a quadratic programming need to be solved within a sampling interval. The low computation burden significantly enhances the time efficiency of AHOCBF-ILQR. Furthermore, we provide theoretical proof of the safety and feasibility of AHOCBF-ILQR. The algorithms are tested by motion planning experiments for a wheeled mobile robot, where the robot is required to navigate around static or moving obstacles that are unknown in advance. The experimental results show that AHOCBF-ILQR can solve the control inputs within text{0.05},s, and ensure safety.
AB - This letter proposes an adaptive high-order control barrier function-based iterative linear quadratic regulator (AHOCBF-ILQR) algorithm for real time safety-critical motion planning. Firstly, we propose a HOCBF-ILQR method, where a HOCBF-based controller is designed as a safety filter of ILQR to guarantee safety. Then, to address the potential infeasibility issue in HOCBF-ILQR, AHOCBF-ILQR is proposed by introducing an auxiliary variable. In AHOCBF-ILQR, only an unconstrained optimization problem and a quadratic programming need to be solved within a sampling interval. The low computation burden significantly enhances the time efficiency of AHOCBF-ILQR. Furthermore, we provide theoretical proof of the safety and feasibility of AHOCBF-ILQR. The algorithms are tested by motion planning experiments for a wheeled mobile robot, where the robot is required to navigate around static or moving obstacles that are unknown in advance. The experimental results show that AHOCBF-ILQR can solve the control inputs within text{0.05},s, and ensure safety.
KW - Constrained Motion Planning
KW - Robot Safety
KW - Wheeled Robots
UR - http://www.scopus.com/inward/record.url?scp=85192993321&partnerID=8YFLogxK
U2 - 10.1109/LRA.2024.3398506
DO - 10.1109/LRA.2024.3398506
M3 - Article
AN - SCOPUS:85192993321
SN - 2377-3766
VL - 9
SP - 6099
EP - 6106
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 7
ER -