Path Planning Method for Quadcopter Unmanned Aerial Vehicles Based on Improved A-Star

This study delves deeply into the critical issue of path planning for unmanned aerial vehicle (UAV) missions, aiming to enhance its safety and efficiency. Given the limitations of traditional A-star algorithm in search efficiency, excessive turning points in the path, and insufficient path smoothness, this paper proposes and verifies an innovative UAV path planning method. This method integrates an improved A-star algorithm with a B-spline algorithm to achieve efficient and smooth path planning. Specifically, a dynamic weighting strategy is adopted to optimize the A-star algorithm, significantly improving its search efficiency, reducing unnecessary search nodes, and thus accelerating the response speed of path planning. Additionally, the B-spline algorithm is utilized to post-process the path generated by the A-star algorithm, effectively reducing the number of turning points in the path, significantly enhancing its smoothness, and ensuring the stability and safety of the UAV during flight. Experimental results demonstrate that compared to the traditional A-star algorithm, this method exhibits significant advantages in search efficiency, search speed, and path smoothness, providing powerful technical support for efficient and safe flight of UAVs in complex environments.