电子科技 ›› 2023, Vol. 36 ›› Issue (7): 39-48.doi: 10.16180/j.cnki.issn1007-7820.2023.07.006

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基于队形变化的多无人机航迹规划算法

王杨斌,章伟,胡陟   

  1. 上海工程技术大学 机器人智能控制实验室,上海 201620
  • 收稿日期:2022-01-07 出版日期:2023-07-15 发布日期:2023-06-21
  • 作者简介:王杨斌(1994-),男,硕士研究生。研究方向:机器人路径规划。|章伟(1977-),男,博士,教授。研究方向:非线性控制与观测、多智能体协调控制。
  • 基金资助:
    国家自然科学基金(62003207)

Multi-UAV Path Planning Algorithm Based on Formation Change

WANG Yangbin,ZHANG Wei,HU Zhi   

  1. Laboratory of Intelligent Control and Robotics,Shanghai University of Engineering Science,Shanghai 201620,China
  • Received:2022-01-07 Online:2023-07-15 Published:2023-06-21
  • Supported by:
    National Natural Science Foundation of China(62003207)

摘要:

针对多无人机在复杂环境下的航迹规划问题,文中提出基于队形变化的多无人机航迹规划算法。利用领航-跟随的无人机拓扑结构,设计了一种以时间与航程作为衡量指标的代价函数,求解出最优的编队集结点。采用改进的Informed-RRT*算法求解出领航者的渐近最优航迹,结合队形变化策略实现了跟随者的航迹规划与避障。在定义队形变化量、路径长度比、航向稳定性性能指标的基础上,文中进行了仿真实验并对生成航迹进行评价与对比。仿真结果表明,无人机编队实现了在复杂环境下航迹规划与避障,同时为跟随者规划出最优航迹,与领航者最优航迹长度相差不到1%,验证了该算法的实用性与有效性。

关键词: 无人机编队, 领航-跟随法, Informed-RRT*, 编队集结, 航迹规划, 队形变化, 最优航迹, 避障

Abstract:

In view of the problem of trajectory planning of multiple UAVs in complex environments, a multi-UAV trajectory planning algorithm based on formation changes is proposed. Based on the topology of pilot-following UAV, a cost function with time and range as the metrics is designed to solve the optimal formation rendezvous point. The improved Informed-RRT* algorithm is used to solve the asymptotic optimal track of the leader, and the track planning and obstacle avoidance of the follower is realized by combining the formation change strategy. On the basis of defining formation variation, path length ratio, and heading stability performance indicators, simulation experiments are carried out and the generated tracks are evaluated and compared. The simulation results show that the UAV formation can achieve trajectory planning and obstacle avoidance in complex environments, and at the same time plan the optimal trajectory planning for the follower, which is less than 1% away from the optimal trajectory length of the leader, which improves the practicality and effectiveness of the algorithm.

Key words: UAV formation, leader-following method, informed-RRT*, formation assembly, path planning, formation change, optimal track, obstacle avoidance

中图分类号: 

  • TP242
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