An Fusion Algorithm of Improved A-star and DWA for Autonomous Vehicle Path Planning
Yunxiang Liu, Kun Li · 2024
To address the need for global path optimization and dynamic real-time obstacle avoidance in unknown environments for autonomous vehicles, we propose an autonomous vehicle path planning scheme that integrates an optimized A-star algorithm with the Dynamic Window Approach (DWA). The A-star algorithm is capable of finding the global optimal path. By optimizing the A-star algorithm, an adaptive heuristic function is introduced, key path points are selected, and redundant path points are removed. Finally, the Dynamic Window Approach is integrated to achieve dynamic real-time obstacle avoidance in complex environments. Comparative simulation experiments conducted in MATLAB demonstrate that the improved algorithm optimizes trajectory length, trajectory smoothness, and elapsed time. It meets the requirements of global optimization and dynamic real-time obstacle avoidance, providing a superior path planning solution.