A combinatorial search method for the quasi-static payload capacity of serial modular reconfigurable robots

This paper presents a new combinatorial search method for the quasi-static stiffness performance evaluation and payload capacity analysis of serial modular reconfigurable robots (MRRs). The goal in this paper is to optimize the external payload capacity to achieve a global set of satisfactory kineto-elastic performance requirements for all module configurations. This problem exhibits considerable numerical complexities since the maximum payload capacity is dependent on a large number of possible module configurations, the worst-case stiffness poses for each configuration, and multiple kineto-elastic performance requirements. Therefore, to alleviate these difficulties, the problem is decomposed into an elimination search algorithm to reduce the configuration search space, a genetic algorithm to directly search the configuration workspaces and find the worst-case stiffness poses, and a bisection method to determine the maximum payload capacity at the worst-case configurations and poses. It is demonstrated that the worst-case stiffness search method in this paper is superior in computational time and numerical accuracy compared to previous hierarchical or incremental search methods, which require searching through a pre-determined number of poses. Through case studies, the new combinatorial search method is proven to be computationally efficient and can obtain accurate results for the worst-case stiffness poses and maximum payload capacity.