系统工程与电子技术 ›› 2023, Vol. 45 ›› Issue (5): 1380-1390.doi: 10.12305/j.issn.1001-506X.2023.05.14

• 系统工程 • 上一篇    

车载多无人机协同多区域覆盖路径规划方法

刘瑶, 夏阳升, 石建迈, 陈超, 黄金才   

  1. 国防科技大学系统工程学院, 湖南 长沙 410073
  • 收稿日期:2021-10-22 出版日期:2023-04-21 发布日期:2023-04-28
  • 通讯作者: 石建迈
  • 作者简介:刘瑶 (1996—), 女, 博士研究生, 主要研究方向为系统科学与建模、智能运筹规划
    夏阳升 (1996—), 男, 助理工程师, 硕士, 主要研究方向为系统科学与建模、智能运筹规划
    石建迈 (1980—), 男, 研究员, 博士, 主要研究方向为智能运筹规划、供应链管理
    陈超 (1977—), 男, 教授, 博士, 主要研究方向为目标规划、智能运筹规划
    黄金才 (1973—), 男, 研究员, 博士, 主要研究方向为智能运筹规划、智能博弈、机器学习

Path planning method for multi-area coverage by cooperated ground vehicle multi-drone

Yao LIU, Yangsheng XIA, Jianmai SHI, Chao CHEN, Jincai HUANG   

  1. College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
  • Received:2021-10-22 Online:2023-04-21 Published:2023-04-28
  • Contact: Jianmai SHI

摘要:

针对小型无人机在区域信息采集中的优势, 考虑到复杂多变的应用情景, 提出一种面向大面积多区域覆盖扫描任务的车载多无人机协同模式。该模式中, 车辆可作为无人机的移动基站, 与多架无人机协同完成多个大面积区域的覆盖扫描任务。充分分析新问题特点后, 建立了优化车辆地面行驶路径和多无人机协同空中飞行路径的0-1整数规划模型, 提出了一种基于三阶段的智能优化算法, 先后对多无人机区域覆盖路径以及车辆协同路径进行规划, 快速构造可行解, 而后基于自适应大规模邻域搜索算法对可行解进行优化。本文设计了包含8个目标区域的实际案例, 验证了车载多无人机协同模式的优势和算法有效性, 并进一步通过10个随机案例验证了算法性能。对比实验证明, 车载多无人机协同模式在执行多个大面积区域覆盖任务上, 相比车载单无人机模式能够显著缩短任务时间。

关键词: 多无人机协同, 路径规划, 区域覆盖, 启发式算法

Abstract:

Aiming at the advantages of drones in regional information collection, and considering complex application scenarios, a cooperative mode with a ground vehicle (GV) and multi-drone is proposed for multiple large area coverage. In this mode, the GV can be used as the mobile base station of the drones to cooperate with multi-drone to complete coverage scanning tasks in multiple large areas. After fully analyzing the characteristics of the new problem, a 0-1 integer programming model to optimize the GV travel path and the multi-drone cooperative air flight path is established, and a three-stage intelligent optimization algorithm is proposed. The regional coverage path of multi-drone and the GV cooperative path are planned successively to quickly construct feasible solutions, and then the feasible solutions are optimized based on the adaptive large scale domain search algorithm. An actual case involving eight target regions is designed to verify the advantages of the GV multi-drone cooperation mode and the effectiveness of the proposed algorithm, and the algorithm performance is further verified through 10 random cases. The comparative experiment proves that the GV multi-drone cooperative mode can significantly shorten the task time compared with the GV single drone mode in performing multiple large area coverage tasks.

Key words: multi-drone cooperation, path planning, area coverage, heuristic algorithm

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