多机器人协同围捕方法综述

周萌; 李建宇; 王昶; 王晶; 王力

doi:10.16383/j.aas.c240114

多机器人协同围捕方法综述

doi: 10.16383/j.aas.c240114

周萌^1,,
李建宇^1,,
王昶^2,,
王晶^1,,
王力^1,

1.
北方工业大学电气与控制工程学院北京 100144
2.
北京航天自动控制研究所北京 100854

基金项目: 国家重点研发计划课题(2023YFB4704404), 北京市教育委员会科学研究计划项目(KM202410009014), 北京市属高等学校优秀青年人才培育计划项目(BPHR202203032), 北方工业大学毓秀创新项目(2024NCUTYXCX107)资助

详细信息

作者简介:
周萌：北方工业大学电气与控制工程学院教授. 主要研究方向为多机器人协同路径规划与控制, 复杂系统故障诊断与容错控制. E-mail: zhoumeng@ncut.edu.cn

李建宇：北方工业大学电气与控制工程学院硕士研究生. 主要研究方向为多机器人协同围捕, 机器人安全运动控制. E-mail: li1296659870@163.com

王昶：北京航天自动控制研究所高级工程师. 主要研究方向为智能多机协同, 软件自动化生成. E-mail: wwcc099@126.com

王晶：北方工业大学电气与控制工程学院教授. 主要研究方向为多无人机系统协同自主控制, 复杂工业过程的建模、优化、先进控制及其工业应用. 本文通信作者. E-mail: jwang@ncut.edu.cn

王力：北方工业大学电气与控制工程学院教授. 主要研究方向为网联交通智能控制, 公共交通系统风险控制. E-mail: li.wang@ncut.edu.cn

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出版历程
- 收稿日期: 2024-03-05
- 录用日期: 2024-07-23
- 网络出版日期: 2024-08-12

Multi-robot Cooperative Hunting: A Survey

ZHOU Meng^1
,,
LI Jian-Yu^1
,,
WANG Chang^2
,,
WANG Jing^1
,,
WANG Li^1
,

1.
College of Electrical and Control Engineering, North China University of Technology, Beijing 100144
2.
Beijing Aerospace Automatic Control Institute, Beijing 100854

Funds: Supported by National Key Research and DevelopmentProgram of China (2023YFB4704404), R&D Program of Beijing Municipal Education Commission (KM202410009014), Project of Cultivation for Young Top-motch Talents of Beiiing Municipal Institutions (BPHR202203032) and Yuxiu Innovation Project of NCUT (2024NCUTYXCX107)

More Information

Author Bio:
ZHOU Meng　Professor at the College of Electrical and Control Engineering, North China University of Technology. Her research interest covers multi-robot cooperative path planning and control, fault diagnosis and fault-tolerant control of complex systems

LI Jian-Yu　Master student at the College of Electrical and Control Engineering, North China University of Technology. His research interest covers multi-robot cooperative hunting and safe motion control of robots

WANG Chang　Senior engineer at the Beijing Aerospace Automatic Control Institute. His research interest covers intelligent multi-robot cooperation and software automated generation

WANG Jing　Professor at the College of Electrical and Control Engineering, North China University of Technology. Her research interest covers cooperative autonomous control of multiple unmanned aerial systems, modeling, optimization, advanced control, and industrial application of complex industrial processes. Corresponding author of this paper

WANG Li　Professor at the College of Electrical and Control Engineering, North China University of Technology. His research interest covers intelligent control of connected transportation and risk control of public transportation systems

摘要

摘要: 多机协同围捕作为多机器人协同领域的一项重要分支, 着重研究多个机器人通过相互协作对动态可疑目标实现有效地追踪与围捕, 在军事侦查、紧急救援、协同探测等领域具有重要的研究意义与实际应用价值. 首先通过国内外科学引文数据库对多机协同围捕领域相关的文献进行全面检索, 深入剖析目前该领域前沿技术的发展现状与研究热点, 接下来从理论与技术层面分别针对多机协同围捕领域中的目标协同搜索、多机任务分配、协同围捕控制等方面进行全面总结, 重点阐述各研究内容常用方法与技术的工作原理、优缺点及适用范围等. 最后对该领域的发展现状进行总结, 并分析探讨目前尚未解决的难点, 对未来的发展方向提出展望.
- 多机协同围捕 /
- 协同目标搜索 /
- 任务分配 /
- 围捕控制
Abstract: As a significant branch of multi-robot coordination, multi-robot cooperative hunting mainly focuses on tracking and capturing dynamic suspicious targets effectively through cooperation. It has important significance and has been applied in various fields, such as military reconnaissance, emergency rescue, and collaborative detection. This paper conducts a comprehensive search of relevant literature in the field of multi-robot cooperative hunting through domestic and foreign scientific citation databases. It then thoroughly analyzes the current development status and research hotspots of frontier technologies in this field. Following this, the paper offers a thorough summary of research in the field of multi-robot cooperative hunting, covering theoretical and technical aspects, which concentrates on target cooperative search, multi-robot task allocation, and cooperative hunting control, etc. The working principles, advantages, disadvantages, and application ranges of commonly used methods and technologies in each research aspect are introduced in detail. Finally, this paper summarizes the current state of development and unresolved challenges in this field, and suggests potential directions for future development.
- Multi-robot collaborative hunting /
- cooperative target search /
- task allocation /
- hunting control

HTML全文

图 1 现有成果逐年发表情况

Fig. 1 Publication status of existing achievements by year

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图 2 关键词同现网络图

Fig. 2 Keyword co-occurrence network diagram

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图 3 复杂动态环境下围捕场景

Fig. 3 Hunting scenario in complex dynamic environments

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图 4 覆盖式搜索原理图

Fig. 4 The schematic of coverage search method

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图 5 搜索机器人传感器的探测区域

Fig. 5 Detection area of search robot sensors

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图 6 覆盖式搜索方法运动模式

Fig. 6 Motion patterns of coverage search methods

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图 7 搜索机器人等效搜索路径图

Fig. 7 Equivalent search path diagram of search robots

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图 8 扫描式搜索盲区示意图

Fig. 8 The schematic of scan search blind

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图 9 精准式搜索原理图

Fig. 9 The principle diagram of precision search

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图 10 滚动优化

Fig. 10 Rolling optimization

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图 11 围捕任务分配问题

Fig. 11 Hunting task allocation problem

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图 12 围捕点位置分布

Fig. 12 Distribution of hunting points

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图 13 集中式求解方法

Fig. 13 Centralized solution method

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图 14 遗传算法围捕分配模型

Fig. 14 Genetic algorithm hunting allocation model

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图 15 粒子群算法围捕分配模型

Fig. 15 Particle swarm optimization hunting allocation model

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图 16 合同网络示意图

Fig. 16 The schematic of contract network

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图 17 拍卖算法示意图

Fig. 17 The schematic of auction algorithm

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图 18 Delaunay三角形构造方式

Fig. 18 Delaunay triangulation construction method

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图 19 超平面构造方式

Fig. 19 Hyperplane construction method

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图 20 博弈围捕方法分类

Fig. 20 Classification of game-based hunting methods

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图 21 优势区域

Fig. 21 Advantageous region

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图 22 几何优势区域

Fig. 22 Geometric advantageous region

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图 23 智能体优势区域

Fig. 23 Agent advantageous region

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图 24 强化学习围捕流程

Fig. 24 Reinforcement learning hunting process

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图 25 强化学习方法

Fig. 25 Reinforcement learning methods

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图 26 通讯拓扑

Fig. 26 Communication topology

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表 1 多机协同搜索方法总结

Table 1 Summary of multi-robot cooperative search methods

搜索环境	搜索方法	优点	缺点	覆盖范围
静态目标搜索	图搜索法^[5−6]	搜索半径自适应调整、较低轨迹误差	不适应复杂环境、需要准确的本地化信息	不完全覆盖
	概率图^[7]	避免寻优过程的局部最优解	不确定信息带来的复杂性	不完全覆盖
	快速探索随机数^[8]	最短路径	无法确定最后搜索时间	完全覆盖
	启发式算法^[9]	无需先验知识、无需对搜索环境进行划分	没有考虑能耗	完全覆盖
动态目标搜索	覆盖直线搜索^[10−11]	简单、高效、容易实现	无法对资源进行灵活分配	完全覆盖
动态目标搜索	预测概率图^[12−14]	高成功率、低搜索时间	计算复杂	不完全覆盖

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表 2 围捕机器人常见运动学与动力学约束

Table 2 Common kinematic and dynamic constraints of hunting robots

运动学与动力学约束	约束描述
最小、最大运动速度约束	围捕机器人速度须介于最小速度和最大速度之间
最小、最大运动加速度约束	围捕机器人加速度须介于最小加速度和最大加速度之间
最小步长约束	围捕机器人轨迹从当前状态到改变行进方向的下一状态之间的直线运动距离须大于最小步长
最小转弯半径约束	围捕机器人轨迹的转弯半径须大于最小转弯半径
最大航偏角约束	围捕机器人运动过程中的航偏角须小于最大航偏角

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表 3 多机器人协同围捕任务分配方法总结

Table 3 Summary of multi-robot cooperative hunting task allocation methods

任务分配算法	架构特点	优点	缺点
贪婪算法^[91]	集中式	实时性高, 简单易实现	局部最优解, “死锁”分配
匈牙利算法^[74−75]	集中式	效率高, 局部最优解, 简单易实现	不适合大规模任务分配场景, 只适合一对一分配
遗传算法^[81]	集中式	全局搜索能力, 适应性和鲁棒性, 并行性	计算成本高, 难以收敛, 参数设置敏感, 编码方式选择困难
粒子群算法^{[88, 90]}	集中式	快速收敛, 简单易实现, 适应性强	过早收敛, 参数敏感
契约合同网络方法^[95−96]	分布式	分布式协作, 动态灵活性, 适应性	高复杂性和通讯开销大, 信任建立和信息共享困难
拍卖算法^[106−107]	分布式	分布式协作, 灵活性, 任务动态调整	竞争激烈可能导致效率下降, 存在信息不对称, 对于大规模问题不再适用

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表 4 生物启发式神经网络方法分析

Table 4 Biologically inspired neural network method analysis

领域	内容	优点	缺点
路径规划^{[125−126, 128]}	解决机器人路径规划、防碰撞问题	实时性好	仅适用于二维平面
多机围捕^[137]	首次将围捕问题与生物神经网络模型对应	兼顾围捕任务目标搜索、任务分配过程	模型设计复杂
路径规划^[129]	引入假想非障碍物相邻点, 考虑转角因素	解决了路径错判问题	计算效率低
路径规划^[130]	模型考虑相邻神经元权值影响	使模型更具网络特性	增加碰撞检测计算节点
路径规划^[131−132]	决策项考虑洋流影响	使方法更贴合实际环境	缺乏高效任务分配方法
多机围捕^[133−134]	决策项考虑洋流影响	使方法更贴合实际环境	规划路径可能并非最优解
多机围捕^[135]	设计基于协商思想的任务分配方法	增加了围捕任务效率	仅适用于低维环境
多机围捕^[136]	将方法拓展至三维环境	增加了方法的可拓展性	奖励函数设计复杂、计算效率低

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