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

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.