Hierarchical planning method for mobile robot path-following in dynamic complex environments

Abstract

To address the challenge of achieving efficient and safe autonomous navigation for mobile robots in complex dynamic environments, this paper proposes a hierarchical planning architecture based on point-by-point tracking control. A node detection strategy grounded in the safe workspace effectively prevents collisions between the generated path and surrounding obstacles. A two-stage heuristic search strategy is designed, incorporating an intermediate node mechanism to substantially enhance search efficiency. Furthermore, the potential field model of the Artificial Potential Field (APF) method is optimized, and a node attraction strategy is introduced to improve overall path quality. Simulation results demonstrate that, compared to baseline algorithms, the global planner achieves significant improvements in path length, computation time, and the number of sampling iterations. The local planner also exhibits superior performance in both computation time and path quality relative to other comparative algorithms. Finally, both ROS-based simulations and physical experiments validate that the proposed hierarchical planning framework delivers exceptional path planning efficiency and obstacle avoidance capability in complex dynamic environments.