Safety-Critical Path Planning of Autonomous Surface Vehicles Based on Rapidly-Exploring Random Tree Algorithm and High Order Control Barrier Functions
Yihe Li, Zhouhua Peng, Lu Liu, Haoliang Wang, Nan Gu, Anqing Wang, Dan Wang · 2023
This paper presents a maritime path planning algorithm for autonomous surface vehicles (ASVs). A improved RRT algorithm for generating sample nodes is proposed. The HOCBF-RRT algorithm leverages high order control barrier functions (HOCBFs) to take into account the dynamics of the ASV and produce a path that prioritizes safety. In addition, to complete the path optimization, combined with control Lyapunov functions (CLFs), the expansion to the target point is accelerated and the planning time is reduced. In simulation experiments, it is observed that the HOCBF-RRT algorithm and HOCBF-CLF-RRT algorithm offer improvements over traditional RRT algorithms. Specifically, the HOCBF-RRT algorithm is found to enhance the smoothness of paths, while the HOCBF-CLF-RRT algorithm effectively optimizes the expansion of the random tree towards the target point.