Path planning for mobile robots in dynamic environments based on a multistrategy-enhanced dynamic window approach

Abstract

To address the limitations of path planning in dynamic environments—namely poor real-time performance and inadequate dynamic obstacle avoidance—a multistrategy-enhanced dynamic window approach is proposed. First, the Anytime Repairing A* (ARA*) algorithm is employed for fast path generation and progressive refinement, producing path nodes aligned with the global objective. Then, within the dynamic window approach (DWA), a velocity adjustment strategy, a multidimensional evaluation function, and adaptive weight parameters are integrated to improve local planning responsiveness and dynamic obstacle avoidance capabilities. Finally, guided by the global path from ARA*, the improved DWA algorithm is used to continuously refine the local trajectory, achieving a coordinated integration of global planning and local avoidance. Simulation results demonstrate that the proposed method significantly enhances both path accuracy and obstacle avoidance performance in dynamic environments.