Dynamic path planning fusion algorithm with improved A* algorithm and dynamic window approach

In the field of robotics, path planning in complex dynamic environments has become a significant research hotspot. Existing methods often suffer from inadequate dynamic obstacle avoidance capabilities and low exploration efficiency. These issues primarily arise from inconsistencies caused by insufficient utilization of environmental maps in actual path planning. To address these challenges, we propose an improved algorithm that integrates the enhanced A* algorithm with the optimized dynamic window approach (DWA). The enhanced A* algorithm improves the robot's path smoothness and accelerates global exploration efficiency, while the optimized DWA enhances local static and dynamic obstacle avoidance capabilities. We performed simulation experiments using MATLAB and conducted experiments in real dynamic environments simulated with Gazebo. Simulation results indicate that, compared to the traditional A* algorithm, our method optimizes traversed grids by 25% and reduces time by 23% in global planning. In dynamic obstacle avoidance, our approach improves path length by 2.7% and reduces time by 19.2% compared to the traditional DWA, demonstrating significant performance enhancements.