基于灰狼算法的多无人机带时间窗路径规划

Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm.

作者信息

Zhang Changchun, Liu Yifan, Hu Chunhe

机构信息

School of Technology, Beijing Forestry University, Beijing 100083, China.

Research Center for Biodiversity Intelligent Monitoring, Beijing Forestry University, Beijing 100083, China.

出版信息

Biomimetics (Basel). 2022 Dec 3;7(4):225. doi: 10.3390/biomimetics7040225.

DOI:10.3390/biomimetics7040225

PMID:36546924

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9775644/

Abstract

The Gray Wolf (GWO) algorithm aims to address the path planning problem of multiple UAVs, and the scene setting is mainly to avoid threats, meet the constraints of UAVs themselves and avoid obstacles between UAVs. The scene setting is relatively simple. To address such problems, the problem of time windows is considered in this paper, so that the UAV can arrive at the same time, and the Gray Wolf algorithm is used to optimize the problem. Finally, the experimental results verify that the proposed method can plan a safe flight path in the process of multi-UAV flight and reach the goal point at the same time. The mean error of flight time between UAVs of the GWO is 0.213, which is superior to PSO (0.382), AFO (0.315) and GA (0.825).

摘要

灰狼（GWO）算法旨在解决多无人机的路径规划问题，场景设置主要是为了规避威胁、满足无人机自身的约束条件以及避免无人机之间的障碍物。场景设置相对简单。为了解决此类问题，本文考虑了时间窗问题，以使无人机能够同时到达，并使用灰狼算法对该问题进行优化。最后，实验结果验证了所提方法能够在多无人机飞行过程中规划出安全的飞行路径并同时到达目标点。灰狼算法中无人机之间飞行时间的平均误差为0.213，优于粒子群优化算法（PSO，0.382）、人工鱼群算法（AFO，0.315）和遗传算法（GA，0.825）。