多无人机协同任务规划方法研究综述

doi:10.12305/j.issn.1001-506X.2024.10.21

摘要/Abstract

摘要：

随着通信技术的发展, 未来多无人机系统将会朝向集群化、自主化和智能化方向发展。通过单元间的信息融合和能力互补, 多无人机系统可以打破自身能力壁垒, 形成多样化的任务能力。任务规划是多无人机系统协同应用的顶层设计, 属于带约束的组合优化问题, 是在任务需求、单元能力和环境态势的影响下, 优化求解获得满足要求的、优化的任务执行方案。从问题表征建模和方案求解评估两个角度梳理现有研究, 从场景分析、任务分配和航迹规划3个方面总结问题表征建模部分的研究, 从求解算法和效能评估两个方面总结方案求解评估部分的研究。最后, 分析未来无人机任务规划研究中值得关注的若干方向。

关键词: 多无人机系统, 任务规划, 问题表征及建模, 方案求解评估

Abstract:

With the development of communication technology, the future development of multi-unmanned aerial vehicle (UAV) system is trending towards clusterization, autonomy, and intelligence. Through information fusion and capability complementation among units, multi-UAV system can break through their inherent technology limitations and form diversified mission capabilities. Mission planning serves as the top-level design for collaborative applications of multi-UAV system. It is a combinatorial optimization problem with constraints, seeking to obtain optimal mission execution plans that satisfy requirements, with the consideration of mission demands, unit capabilities, and environmental situations. This paper summarizes existing research result from two perspectives: problem representation modeling and plan solution evaluation. Specifically, it reviews the research result of problem representation modeling in terms of scenario analysis, task allocation, and trajectory planning, and examines solution evaluation in terms of solving algorithms and performance assessment. Finally, several directions worthy of attention in future research on UAV mission planning are analyzed.

Key words: multi-unmanned aerial vehicle (UAV) system, mission planning, problem representation and modeling, plan solving and evaluation

中图分类号:

王建峰, 贾高伟, 郭正, 侯中喜. 多无人机协同任务规划方法研究综述[J]. 系统工程与电子技术, 2024, 46(10): 3437-3450.

Jianfeng WANG, Gaowei JIA, Zheng GUO, Zhongxi HOU. Review of research on multi-UAV collaborative mission planning method[J]. Systems Engineering and Electronics, 2024, 46(10): 3437-3450.

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方法名称	内容	优/缺点
可视图法	连接起始点、目标点和多边形障碍顶点构成网络	构建简单, 但路径与障碍相距较近, 安全性低
Voronoi图法	由围绕障碍物的多个共边多边形组成	路径安全性高, 但路径多在障碍之间的开阔区域, 路径质量不高
栅格法	使用互不重叠的单元离散区域	不需考虑障碍情况, 但数据量大, 需平衡离散精度和搜索效率
概率路标图法	空间预设大量采样点, 在此基础上搜索生成可行路径	复杂障碍场景中可生成可行路径, 但受采样点设置影响大
快速随机树法	从起点开始, 边布置采样点边搜索路径, 直至到达终点	适用于动态复杂障碍场景, 但受采样点生成参数影响大, 随机性高
人工势场法	构建虚拟力场引导目标沿最大梯度方向运动	适用于复杂多障碍场景, 但势场函数设置复杂

曲线名称	内容	优/缺点
Dubins曲线	由直线和圆弧组成, 可增加盘旋或调整曲率控制长度	计算简单且长度最短, 但直线与圆弧连接处曲率不连续
贝塞尔曲线	由线段与节点组成, 通过控制节点来调整曲线空间分布	曲率连续, 但多段拼接困难, 不能进行局部调整
B样条曲线	调整贝塞尔曲线的基函数, 以分段低阶曲线代替高阶曲线	支持多段拼接, 可局部修改, 但计算复杂
毕达哥拉斯曲线	满足毕达哥拉斯条件的参数化多项式曲线	曲率连续且长度可控, 但控制参数复杂

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