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

Granularity Partition Method for Robot Functional Modules Based on Fuzzy Dendrogram and DS Evidence Theory

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.

A novel evaluation method of granularity partition for functional modules based on robot technology middle- ware （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 struc- tured 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 opti- mal 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.