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集群机器人系统特性评价研究综述

武文亮 周兴社 沈博 赵月

武文亮, 周兴社, 沈博, 赵月. 集群机器人系统特性评价研究综述. 自动化学报, 2022, 48(5): 1153−1172 doi: 10.16383/j.aas.c200964
引用本文: 武文亮, 周兴社, 沈博, 赵月. 集群机器人系统特性评价研究综述. 自动化学报, 2022, 48(5): 1153−1172 doi: 10.16383/j.aas.c200964
Wu Wen-Liang, Zhou Xing-She, Shen Bo, Zhao Yue. A review of swarm robotic systems property evaluation research. Acta Automatica Sinica, 2022, 48(5): 1153−1172 doi: 10.16383/j.aas.c200964
Citation: Wu Wen-Liang, Zhou Xing-She, Shen Bo, Zhao Yue. A review of swarm robotic systems property evaluation research. Acta Automatica Sinica, 2022, 48(5): 1153−1172 doi: 10.16383/j.aas.c200964

集群机器人系统特性评价研究综述

doi: 10.16383/j.aas.c200964
基金项目: 国防科技创新特区项目(18-163-11-ZT-003-010-01)资助
详细信息
    作者简介:

    武文亮:西北工业大学计算机学院博士研究生. 主要研究方向为集群机器人系统特性, 人工智能系统智能性评价. E-mail: wuwenliang@mail.nwpu.edu.cn

    周兴社:西北工业大学计算机学院教授. 主要研究方向为分布式计算, 信息物理系统. 本文通信作者. E-mail: zhouxs@nwpu.edu.cn

    沈博:西北工业大学计算机学院副教授. 主要研究方向为信息物理系统, 物联网. E-mail: shen@nwpu.edu.cn

    赵月:西北工业大学计算机学院博士研究生. 主要研究方向为集群机器人行为建模, 集群机器人系统特性评估. E-mail: zhaoyueplc@mail.163.com

A Review of Swarm Robotic Systems Property Evaluation Research

Funds: Supported by National Defense Science and Technology Innovation Program of China (18-163-11-ZT-003-010-01)
More Information
    Author Bio:

    WU Wen-Liang Ph.D. candidate at the School of Computer Science, Northwestern Polytechnical University. His research interest covers swarm robotic systems property and artificial intelligent systems intelligence evaluation

    ZHOU Xing-She Professor at the School of Computer Science, Northwestern Polytechnical University. His research interest covers distributed computing and cyber physical systems. Corresponding author of this paper

    SHEN Bo Associate professor at the School of Computer Science, Northwestern Polytechnical University. His research interest covers cyber physical systems and internet of things

    ZHAO Yue Ph.D. candidate at the School of Computer Science, Northwestern Polytechnical University. Her research interest covers behaviors modeling of swarm robots and swarm robotic systems property evaluation

  • 摘要: 集群机器人系统是群体智能的一个重要应用研究领域, 也是机器人系统未来发展的重要方向之一. 集群机器人系统特性评价是一个极具挑战性的关键技术与理论问题, 对于集群机器人系统的研究与发展具有重要意义. 首先, 给出了对集群机器人系统基本概念的理解, 并且从多种不同角度作出了分类. 其次, 梳理了多个关键的集群机器人系统期望特性; 在此基础上, 分别从评价标准、评价指标体系和评价方法三方面对已有集群机器人系统特性评价研究成果进行了比较全面的评述. 最后, 分析总结了当前集群机器人系统特性评价研究工作的不足, 并对未来发展方向进行了展望.
  • 图  1  基于分层递阶的集群机器人协同控制结构

    Fig.  1  Cooperative control process of swarm robots based on hierarchical structure

    图  2  基于自组织的集群机器人协同控制流程

    Fig.  2  Cooperative control process of swarm robots based on self-organization

    图  3  协同OODA模型

    Fig.  3  Co-OODA model

    图  4  NIST ALFUS的三轴模型

    Fig.  4  3-axis model proposed by NIST ALFUS

    图  5  无人驾驶车辆评测模型

    Fig.  5  Testing and evaluation model of UGV

    图  6  协同体系结构

    Fig.  6  Cooperative architecture

    图  7  ACL发展路线图

    Fig.  7  ACL development roadmap

    图  8  自主控制系统初始ACL雷达图

    Fig.  8  Initial ACL radar chart of autonomous control systems

    图  9  自主性评价的蛛网模型

    Fig.  9  Cobweb model for autonomy evaluation

    图  10  群体熵的组成结构

    Fig.  10  Composition structure of swarm entropy

    表  1  不同自主性等级划分标准比较

    Table  1  Comparison of different autonomy level classification standards

    提出时间提出者应用对象级数维度
    1990Zeigler自主系统3一维
    1991Sheridan自动装置10一维
    2000DODUAV10一维
    2002ASB陆上机器人系统10一维
    2003NIST ALFUS无人系统(单机→集群)10三维
    2003Draper机器人系统4三级
    2003AFRLUAV (单机→集群)11四维
    2006NASAUAV (单机→集群)6二维
    2010国防科技大学UAV (单机→集群)7五维
    2011北京航空航天大学UAV (单机→集群)9四维
    下载: 导出CSV

    表  2  系统自主性等级评价指标体系比较

    Table  2  Comparison of autonomy level evaluation index systems

    提出者优点缺点
    Draper突出系统自主性的核心能力缺乏系统自主行为实现模型支撑所有指标均采用定性度量
    AFRL以 OODA 模型为依据, 从本质上反映系统自主性不能体现外部自主需求
    NIST ALFUS可较好反映系统外部自主需求不能体现系统内部自主能力
    国防科技大学以 Co-OODA 模型为依据, 融合任务能力与效果任务能力的各个度量定义太简单
    北京航空航天大学增加智能性与通信度量缺乏系统自主行为实现模型支撑
    下载: 导出CSV

    表  3  系统智能性量化评价指标体系比较

    Table  3  Comparison of system intelligence quantitative evaluation index systems

    提出者优点缺点
    北京理工大学以系统智能行为为依据构建评价指标,
    评价者无需详细了解系统的内部实现
    需要构建统一的测试场景, 设计专门的数据采集系统,
    实施难度大, 成本高
    北京科技大学以系统结构与行为效果为依据构建指标,
    分布测试, 无需构造统一的测试场景
    评价者需要从对系统的内部实现详细的了解,
    并设计与实施多个不同的测试项
    下载: 导出CSV

    表  4  机器人系统自主性的不同评价方法比较

    Table  4  Comparison of different autonomy evaluation methods of robotic system

    方法名称量化属性方法优点方法缺点
    定性等级量表法定性转定量直观、具有明确的量化坐标值缺乏详细量化指标
    时间序列预测法定性为主便于制定发展决策与规划标准需要持续更新
    三维智能空间图表法定性转定量三维定性定级, 管理人员喜欢缺乏详细量化指标
    多维区间打分法定性转定量多维综合评分定级, 管理人员喜欢缺乏详细量化指标
    蛛网评价模型量化为主通用性强, 轴可扩展、等级可定制轴与等级定制是关键
    模糊综合评价法定性转定量模糊决策, 更符合人的思维方式计算复杂, 不易操作
    云重心评价法定性转定量模糊决策, 更符合人的思维方式计算复杂, 不易操作
    下载: 导出CSV
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  • 收稿日期:  2020-11-20
  • 录用日期:  2021-03-02
  • 网络出版日期:  2021-05-14
  • 刊出日期:  2022-05-13

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