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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.Abstract: Swarm robotic system is an important application and research field of swarm intelligence, and it is also an important trend of robotic systems in the future. Properties evaluation of swarm robotic systems is a challenging technical and theoretical problem, which is of great significance to its research and development. Firstly, this paper introduces the concept of swarm robotic system, and classifies it from different attributes. Secondly, some important properties expected are summarized. On this basis, it reviews current research status on swarm robotic systems property evaluation from three aspects including evaluation standards, evaluation index system and evaluation method. Finally, some shortcomings of current research on swarm robotic systems property evaluation are analyzed and summarized, and the future development direction is prospected.
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Key words:
- Swarm robotic /
- system property /
- evaluation standard /
- evaluation index /
- evaluation method
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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
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 1990 Zeigler 自主系统 3 一维 1991 Sheridan 自动装置 10 一维 2000 DOD UAV 10 一维 2002 ASB 陆上机器人系统 10 一维 2003 NIST ALFUS 无人系统(单机→集群) 10 三维 2003 Draper 机器人系统 4 三级 2003 AFRL UAV (单机→集群) 11 四维 2006 NASA UAV (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 
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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
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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
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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
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