Improved Multi-Scale A* and DWA Algorithm for USV Path Planning Integrating MMG Maneuvering Constraints

To balance safety, real-time performance, and dynamic feasibility in path planning for unmanned surface vehicles (USVs) operating in complex aquatic environments, the study proposes a hybrid planning framework that integrates a multi-scale A* algorithm with the Dynamic Window Approach (DWA) under Maneuvering Modeling Group (MMG) dynamic constraints. Global path optimization incorporates target-oriented neighborhood expansion, obstacle risk constraints, and a maneuverability cost function to comprehensively evaluate path length and collision risk, thereby enhancing the efficiency of the hybrid global-local planning framework. Local trajectory prediction strictly couples the MMG USV motion model with the DWA algorithm. Real-time optimization of these dynamically constrained trajectories ultimately achieves proactive and safe evasion of dynamic obstacles. Compared with the traditional A* + DWA method, the proposed hybrid method reduces path length and computational time by 5.09% and 31.93%, effectively suppressing abrupt heading changes during dynamic obstacle avoidance. This paper provides an efficient path planning strategy that conforms to USV maneuvering constraints for autonomous navigation and dynamic obstacle avoidance.