Path planning of AMR in complex environments based on improved A* and DWA algorithms

Abstract

This paper aims to address the path planning problem of autonomous mobile robots in complex environments. To achieve this goal, a hybrid DWA algorithm integrating an improved A* algorithm with fuzzy inference logic is proposed. Specifically, the improved A* algorithm is used to rapidly generate an initial feasible path, the rolling window mechanism is adopted to select sub-target points, and the evaluation weights are dynamically adjusted through a fuzzy controller. The research findings show that the improved A* algorithm has significant enhancements in search efficiency and path smoothness across different maps; meanwhile, the proposed improved hybrid algorithm exhibits notable improvements in runtime and average curvature, and can stably handle complex environments. The originality of this paper lies in the proposed DWA hybrid algorithm based on the improved A* integrated with fuzzy inference logic, which enables the local planner to flexibly adjust weight parameters in response to environmental changes relying on reasonable global path guidance.