He Yong, Hou Ticheng, Wang Mingran
School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, 410114, China.
Sci Rep. 2024 Apr 22;14(1):9257. doi: 10.1038/s41598-024-60051-4.
To solve the problems of UAV path planning, such as low search efficiency, uneven path, and inability to adapt to unknown environments, this paper proposes A double-layer optimization A* and dynamic window method for UAV path planning. Firstly, the neighboring node clip-off rule is defined to optimize the node expansion mode of the A* algorithm, and the obstacle coverage model is designed to dynamically adjust the heurizing function of the A* algorithm to improve the path search efficiency. Then, the Bresenham algorithm is adopted for collision detection and critical path nodes are extracted to significantly reduce the number of path turning points. Secondly, a new tracking index is proposed to optimize the evaluation function of the dynamic window method to make the local path fit the global path further. By detecting the dangerous distance, the dynamic adaptive method of evaluation function weight is designed to improve the fixed weight of the dynamic window method. Finally, the key turning point of optimizing the A* algorithm is taken as the temporary target point to improve the DWA algorithm, and the local part follows the global part, and the fusion of the two algorithms is realized. Simulation results show that the proposed method can significantly improve the efficiency and smoothness of mobile robot path planning, enhance the real-time obstacle avoidance and adaptive ability of unknown environments, and better meet the requirements of complex planning tasks.
为解决无人机路径规划中存在的搜索效率低、路径不平坦以及无法适应未知环境等问题，本文提出一种用于无人机路径规划的双层优化A算法与动态窗口方法。首先，定义邻域节点裁剪规则以优化A算法的节点扩展方式，设计障碍物覆盖模型以动态调整A算法的启发函数，从而提高路径搜索效率。然后，采用布雷森汉姆算法进行碰撞检测并提取关键路径节点，显著减少路径转折点数量。其次，提出一种新的跟踪指标以优化动态窗口方法的评估函数，使局部路径更贴合全局路径。通过检测危险距离，设计评估函数权重的动态自适应方法，改进动态窗口方法的固定权重。最后，将优化A算法的关键转折点作为临时目标点来改进DWA算法，使局部跟随全局，实现两种算法的融合法的融合。仿真结果表明，所提方法能显著提高移动机器人路径规划的效率和平滑性，增强未知环境下的实时避障与自适应能力，更好地满足复杂规划任务的要求。
