Flexible Active Safety Motion Control for Robotic Obstacle Avoidance: A CBF-Guided MPC Approach
Jinhao Liu, Jun Yang, Jianliang Mao, Tianqi Zhu, Qihang Xie, Yimeng Li, Xiangyu Wang, Shihua Li
Abstract
A flexible active safety motion (FASM) control approach is proposed for collision avoidance in robot manipulators. The key feature is the use of control barrier functions (CBFs) to design flexible CBF-guided safety criteria (CBFSC) with dynamically optimized decay rates, providing both flexibility and active safety in dynamic environments for robots. First, discrete-time CBFs are utilized to formulate the new flexible CBFSC with dynamic decay rates, which is then integrated into the model predictive control (MPC) framework. Notably, the decay rates of the CBFSC are incorporated as decision variables, allowing for dynamic adaptability during obstacle avoidance. In addition, a new cost function with an integrated penalty term is designed to dynamically adjust the safety margins. Finally, experiments in various scenarios using a Universal Robots 5 (UR5) manipulator validate the effectiveness of the proposed approach.