A two-phased robust safety control framework integrating path planning and control barrier functions for autonomous robots
TL;DR
A novel two-phased robust safety control framework is proposed by integrating dynamic programming for global path planning and CBFs-QP controller for local real-time obstacle avoidance, to enhance the safety robustness of autonomous robots in complex obstacle configurations while reducing computational and energy consumption.
Abstract
Autonomous mobile robots face significant challenges when navigating complex environments that contain some static and dynamic obstacles. Traditional safety control methods based on control barrier functions (CBFs) with quadratic programming (QP) often suffer from high computational burden and vulnerability to deadlock situations, especially in cluttered environments. To this end, a novel two-phased robust safety control framework is proposed in this paper by integrating dynamic programming (DP) for global path planning and CBFs-QP controller for local real-time obstacle avoidance, to enhance the safety robustness of autonomous robots in complex obstacle configurations while reducing computational and energy consumption. Moreover, an improved A* algorithm is presented to resolve the deadlock problem caused by dynamic obstacles. In the first phase, DP-based global path planning with redundancy pruning is performed as a one-shot pre-computation to generate an optimal path before autonomous robots set off. In the second phase, CBFs are used for real-time safety and local obstacle avoidance by tracking the global path information. If deadlock situations still arise, then an A* algorithm is adopted to identify a feasible path across dynamic obstacles. With the assistance of the A* algorithm, CBFs can effectively guarantee local real-time tackling.