A combinatorial search method for the quasi-static payload capacity of serial modular reconfigurable robots. (October 2015)

• Record Type: Journal Article
• Authors: Mohamed, Richard Phillip; Xi, Fengfeng (Jeff); Lin, Yu
• Is Part Of: Mechanism and machine theory. Volume 92(2015:Oct.)
• Page Start: 240
• Page End: 256
• Publication Date: 2015-10
• Subjects: Modular reconfigurable robot -- Genetic algorithm -- Workspace search -- Payload capacity -- Reconfiguration analysis; Machine theory -- Periodicals; Machinery -- Periodicals; Machines -- Périodiques; Génie mécanique -- Périodiques; Machine theory; Machinery; Periodicals; 621.81
• DOI: 10.1016/j.mechmachtheory.2015.05.016
• Languages: English
• ISSNs: 0094-114X

Abstract

Abstract: 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. Highlights: Payload capacity analysis for serial modular reconfigurable robots is introduced. A novel combinatory algorithm for multiple stiffness performance requirements An automated enumeration scheme to reduce number of possible configurations A more direct method of searching configuration workspaces to find worst-case Case studies demonstrate the computational superiority over existing methods.