航空学报 > 2023, Vol. 44 Issue (12): 327856-327856   doi: 10.7527/S1000-6893.2022.27856

基于改进ACO的带续航约束无人机全覆盖作业路径规划

于全友1, 徐止政1(), 段纳1, 徐觅蜜1, 程义2   

  1. 1.江苏师范大学 电气工程及自动化学院,徐州 221000
    2.江苏蒲公英无人机有限公司,徐州 221000
  • 收稿日期:2022-07-26 修回日期:2022-09-13 接受日期:2023-01-28 出版日期:2023-06-25 发布日期:2023-02-06
  • 通讯作者: 徐止政 E-mail:xuzz@jsnu.edu.cn
  • 基金资助:
    国家自然科学基金(62173166);江苏省高等学校自然科学研究面上项目(20KJB510047);徐州市基础研究计划(KC22053);江苏省研究生科研与实践创新计划(SJCX21_1130)

Coverage operation path planning of UAV with endurance constraints based on improved ACO

Quanyou YU1, Zhizheng XU1(), Na DUAN1, Mimi XU1, Yi CHENG2   

  1. 1.College of Electrical Engineering and Automation,Jiangsu Normal University,Xuzhou 221000,China
    2.Jiangsu Dandelion UAV Company,Xuzhou 221000,China
  • Received:2022-07-26 Revised:2022-09-13 Accepted:2023-01-28 Online:2023-06-25 Published:2023-02-06
  • Contact: Zhizheng XU E-mail:xuzz@jsnu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(62173166);Universities Natural Science Research Project of Jiangsu Province(20KJB510047);Fundamental Research Program of Xuzhou City(KC22053);Postgraduate Research and Practice Innovation Program of Jiangsu Province(SJCX21_1130)

摘要:

本文研究带续航约束的电动多旋翼无人机全覆盖作业路径规划问题。首先,基于扫描线路径生成方法建立了带续航约束的无人机全覆盖作业路径规划的数学模型,然后,针对该路径规划模型中路径节点拓扑结构动态变化的特点,提出了改进的蚁群算法(ACO)。该蚁群算法给出了无人机返航时机判断机制和返航点计算方法,设计了动态局部距离矩阵,提出了滚动权值加权和的信息素更新机制,在寻优过程中兼顾全局启发性信息和局部启发性信息。最后,采用规则地形和复杂地形多田块作业算例对提出算法的有效性和优越性进行仿真验证,实验结果表明与其他4种对比算法相比,提出算法获得的规则作业田块的最优转移路径长度至少缩短了1.8%,获得的复杂作业田块的最优转移路径长度至少缩短了11.4%。

关键词: 无人机, 全覆盖作业, 路径规划, 续航约束, 蚁群算法

Abstract:

This paper studies the path planning problem of full coverage operation of electric multi-rotor UAVs with endurance constraint. Firstly, a mathematical model of path planning for UAV full coverage operation with endurance constraints is established based on the sweep method. Then, an improved Ant Colony Optimization (ACO) is proposed for handling the dynamic change of path node topology in the path planning model. In the improved ACO, the assessment mechanism of UAV’s return time and calculation method of the return point is provided. A dynamic local distance matrix, together with a pheromone updating mechanism based on the rolling weight weighted sum, is designed, considering both global and local heuristic information in the optimization process. Finally, two examples of multi-field operation tasks with regular and complex terrains are used to verify the effectiveness and advantage of the proposed algorithm. The results show that, compared with the other four algorithms, the proposed algorithm can reduce the length of the shifting path by at least 1.8% and 11.4% on the regular terrain and complex terrain, respectively.

Key words: unmanned aerial vehicle, full coverage operation, path planning, endurance constraint, ant colony optimization

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