An Improved DWA-Based Local Path-Planning Method for an AMR Using Adaptive Fuzzy Control

In the highly dynamic production environment of intelligent logistics centers, autonomous mobile robots (AMRs) serve as the core carriers connecting automated storage systems with assembly lines. Their operational efficiency directly determines the overall production cycle of the entire facility. In actual working conditions, narrow aisles in line-side material stacking zones often lead to local deadlocks. Additionally, the presence of mixed human–machine traffic and numerous dynamic obstacles causes traditional algorithms to struggle with obstacle avoidance or path oscillation during long-distance delivery. This paper investigates the M150 warehouse mobile robot and proposes an improved Dynamic Window Approach (DWA) integrated with adaptive fuzzy control for local path planning. First, the velocity evaluation function is modified for confined spaces by incorporating angular velocity to enhance escape capability and adding a target distance function to optimize sub-goal tracking. Second, a dynamic sampling space based on obstacle distance is constructed to balance computational load. Finally, a two-input, four-output fuzzy controller is designed to adaptively adjust evaluation function weighting factors in real time, enhancing the AMR’s adaptability across diverse scenarios. MATLAB-based simulations demonstrate that compared to traditional algorithms, the improved DWA produces smoother trajectories, effectively resolving jamming and obstacle avoidance issues, thereby meeting enterprises’ stringent requirements for AMR operational stability and safety.