SSA*-PDWA: A Hierarchical Path Planning Framework with Enhanced A* Algorithm and Dynamic Window Approach for Mobile Robots

With the rapid development of intelligent navigation technology, efficient and safe path planning for mobile robots has become a core requirement. To address the challenges of complex dynamic environments, this paper proposes an intelligent path planning framework based on grid map modeling. First, an improved Safe and Smooth A* (SSA*) algorithm is employed for global path planning. By incorporating obstacle expansion and corner-point optimization, the proposed SSA* enhances the safety and smoothness of the planned path. Then, a Partitioned Dynamic Window Approach (PDWA) is integrated for local planning, which is triggered when dynamic or sudden static obstacles appear, enabling real-time obstacle avoidance and path adjustment. A unified objective function is constructed, considering path length, safety, and smoothness comprehensively. Multiple simulation experiments are conducted on typical port grid maps. The results demonstrate that the improved SSA* significantly reduces the number of expanded nodes and computation time in static environments while generating smoother and safer paths. Meanwhile, the PDWA exhibits strong real-time performance and robustness in dynamic scenarios, achieving shorter paths and lower planning times compared to other graph search algorithms. The proposed method maintains stable performance across maps of different scales and various port scenarios, verifying its practicality and potential for wider application.