分段优化RRT的无人机动态航迹规划算法

UAV dynamic path planning algorithm based on segmentated optimization RRT

针对突发威胁,无人机重新规划局部航迹的问题,提出了分段优化快速扩展随机树(rapidly-exploring random tree,RRT)的无人机动态航迹规划算法。首先利用分段优化RRT算法生成全局航迹,然后根据突发威胁的相关信息确定局部航迹的起点和终点,最后利用分段优化RRT算法生成局部航迹,绕过突发威胁并回到原航迹。实验结果表明算法运行时间和路径代价都降低了10%左右。对于动态航迹规划,该算法的鲁棒性与实时性较强。

To solve the problem of sudden threat, the unmanned aerial vehicle （UAV） replanning the local path, this paper presents a segmented optimization rapidlyexploring random tree （RRT） UAV dynamic path planning algorithm. Firstly, the global algorithm is used to generate the global path, and then the starting and ending points of the local path are determined according to the relevant information of the sudden threat. Finally, the local path is generated by the segmented RRT algorithm. The research shows that the algorithm’s runtime and path cost are reduced by about 10%. For the dynamic path planning, the robustness and realtime performance of the algorithm are strong.