Enhanced A*–Fuzzy DWA Hybrid Algorithm for AGV Path Planning in Confined Spaces

Addressing the challenges of inefficient prolonged trajectory resolution and unreliable dynamic obstacle avoidance for intelligent vehicles in complex confined environments, this study proposes an innovative hybrid path planning method. Its core novelty is the deep integration of an enhanced A* algorithm for smooth global planning with a fuzzy logic-controlled Dynamic Window Approach (DWA). The enhanced A* generates efficient and smooth global paths, while fuzzy control significantly improves DWA’s robustness in dynamic, uncertain scenarios. This hybrid strategy achieves efficient synergy between global optimality and local reactive obstacle avoidance. Simulations demonstrate its superiority over conventional A* or DWA in path length, planning efficiency, and obstacle avoidance success rate. Experimental validation on a physical platform in simulated complex scenarios shows an average trajectory deviation ≤ 7.14%. The work provides an effective theoretical and methodological framework for navigation in constrained spaces, offering significant value for practical applications like logistics and automated parking.