Research on Path Planning for Mobile Charging Robots Based on Improved A* and DWA Algorithms

Abstract

Driven by rapid growth in the new-energy vehicle (NEV) market and advances in automation, mobile charging robots are increasingly deployed in parking facilities. In complex environments featuring both static and dynamic obstacles, conventional trajectory plans often exhibit insufficient safety margins and poor smoothness. This paper proposes a hybrid path-planning strategy that combines an improved A* algorithm with an enhanced dynamic window approach (DWA). The enhanced A* algorithm incorporates obstacle influence factors and adaptive weighting during global search, enabling proactive avoidance of obstacle-dense regions and employing segmented Bezier curves for path smoothing. In local planning, the modified DWA integrates a global guidance term and distance-dependent heading weights to mitigate issues of local minima and target loss. Simulation results indicate that the proposed method substantially improves path safety, continuity, and adaptability to complex scenarios while maintaining computational efficiency. Specifically, under high-obstacle-density conditions (e.g., a 20 × 20 grid map), the collision rate is reduced by 66.7% compared to the standard A* algorithm (from 30% to 10%), and the minimum safety distance increases to 0.5 m. Current validation is conducted in simulations; future work will involve real-robot experiments to evaluate real-time performance and robustness in practical environments.