北京航空航天大学学报 ›› 2016, Vol. 42 ›› Issue (10): 2130-2138.doi: 10.13700/j.bh.1001-5965.2015.0613

• 论文 • 上一篇    下一篇

具有持续侦察时间约束的协同航路规划

朱黔, 周锐   

  1. 北京航空航天大学 自动化科学与电气工程学院, 北京 100083
  • 收稿日期:2015-09-18 出版日期:2016-10-20 发布日期:2015-11-19
  • 通讯作者: 周锐,Tel.:010-82339232,E-mail:zhr@buaa.edu.cn E-mail:zhr@buaa.edu.cn
  • 作者简介:朱黔男,博士研究生。主要研究方向:多无人机协同控制。E-mail:ZhuQian@buaa.edu.cn;周锐男,博士,教授,博士生导师。主要研究方向:无人机自主控制、任务规划与管理、多飞行器协同控制等。Tel.:010-82339232,E-mail:zhr@buaa.edu.cn
  • 基金资助:
    国家自然科学基金(61273349,61175109)

Cooperative path planning with reconnaissance duration time constraints

ZHU Qian, ZHOU Rui   

  1. School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
  • Received:2015-09-18 Online:2016-10-20 Published:2015-11-19
  • Supported by:
    National Natural Science Foundation of China (61273349, 61175109)

摘要: 为获得目标有效信息,无人机(UAVs)执行侦察任务时针对不同目标所需的持续侦察时间存在一定的差异。本文假设无人机在目标持续侦察过程中保持定直平飞以确保有效侦察,针对至多3个侦察任务重叠的情况,通过几何分析,提出了存在侦察任务重叠情况下的多侦察任务同时侦察方法。在考虑侦察任务重叠和多机协同侦察的同时,以最小化侦察路径长度为性能指标,相邻侦察点间采用Dubins曲线进行航路规划,利用引入精英机制的混合粒子群优化算法实现侦察任务序列优化,实现具有持续侦察时间约束的协同航路规划。仿真结果表明提出算法的有效性。

关键词: 无人机, 侦察任务重叠, 协同航路规划, 精英机制, 混合粒子群优化算法

Abstract: When performing reconnaissance tasks, unmanned aerial vehicles (UAVs) keep reconnoitering the targets during different reconnaissance time for acquiring effective target information. It is assumed that UAVs fly in a straight line, which is suitable for better reconnaissance results during reconnaissance missions. A new simultaneous reconnaissance scheme was proposed by geometrical analysis for less than or equal to three reconnaissance mission overlapping. For the purpose of minimizing cooperative reconnaissance path length, modified hybrid particle swarm optimization algorithm with elitism mechanism was used to optimize the reconnaissance sequence, and the path planning was implemented between consecutive reconnaissance missions by Dubins path, with consideration of reconnaissance missions overlapping and reconnaissance duration time constraints. Simulation results demonstrate the effectiveness of the proposed method.

Key words: unmanned aerial vehicle, reconnaissance missions overlapping, cooperative path planning, elitism mechanism, hybrid particle swarm optimization algorithm

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