基于模糊树图与DS证据理论的机器人功能模块粒度划分方法
基于模糊树图与DS证据理论的机器人功能模块粒度划分方法基于模糊树图与DS证据理论的机器人功能模块粒度划分方法
贾松敏, 张国梁
北京工业大学信息学部，北京 100124
Granularity Partition Method for Robot Functional Modules Based onFuzzy Dendrogram and DS Evidence Theory
JIA Songmin, ZHANG Guoliang
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
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摘要 基于机器人技术中间件（RTM）提出一种功能模块粒度划分评价方法．首先，结合模糊层次分析法（FAHP）获取模块功能与结构相关性指标的综合权重，构造系统相关矩阵，通过模糊树图聚类算法得到不同粒度下的机器人系统模块划分方案；以模块独立性为原则，构建各模块划分方案的内聚度与耦合度模型，并将其视为DS（Dempster-Shafer）证据理论的两个证据源，建立多属性决策矩阵；通过区间偏好排序法对决策方案的信任区间进行排序，得到机器人系统最优模块划分方案．以机器人3维地图创建系统为例，对所提评价方法进行验证，系统实现及结果表明了该方法的有效性和可行性．
关键词 ： 模块划分,机器人技术中间件,模糊树图,DS证据理论,模糊层次分析法
Abstract：A novel evaluation method of granularity partition for functional modules based on robot technology middleware (RTM) is proposed. Firstly, the comprehensive weights for pertinence indexes of structures and functions of modules are calculated by fuzzy analytical hierarchy process (FAHP), and correlation matrix of the system is established. A fuzzy dendrogram clustering algorithm is proposed to obtain the module partition schemes for the robot system under different granularities. To construct multi-attribute decision matrix, the models of cohesion and coupling for each scheme are structured as two sources of evidences for DS (Dempster-Shafer) evidence theory based on the principle of module independence. Then the trust intervals of every decision scheme are sorted by a preference ordering method for intervals to obtain the optimal module partition scheme for the robot system. The evaluation method is verified by applying it to the robot 3D mapping system. The system implementation and results show that the method is effective and feasible.
Key words： module partition RTM (robot technology middleware) fuzzy dendrogram DS evidence theory FAHP (fuzzy analytical hierarchy process)
收稿日期: 2016-09-11
1:TP242
基金资助:北京工业大学智能机器人“大科研”推进计划（002000514316009）；国家自然科学基金（61175087）
通讯作者: 张国梁，285719262@qq.com E-mail: 285719262@qq.com
作者简介: 贾松敏（1964-），女，博士，教授．研究领域：智能服务机器人，机器人分散控制，计算机视觉．
张国梁（1990-），男，博士生．研究领域：机器人分散控制，机器人动作识别．
引用本文:
贾松敏, 张国梁. 基于模糊树图与DS证据理论的机器人功能模块粒度划分方法. 机器人, 2016, 38(6): 696-703.
JIA Songmin, ZHANG Guoliang. Granularity Partition Method for Robot Functional Modules Based onFuzzy Dendrogram and DS Evidence Theory. ROBOT, 2016, 38(6): 696-703.
链接本文:
http://robot.sia.cn/CN/10.13973/j.cnki.robot.2016.0696 或 http://robot.sia.cn/CN/Y2016/V38/I6/696
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