A memetic algorithm approach for solving the task-based configuration optimization problem in serial modular and reconfigurable robots

Abstract

SUMMARYThis paper presents a novel configuration optimization method for multi degree-of-freedom modular reconfigurable robots (MRR) using a memetic algorithm (MA) that combines genetic algorithms (GAs) and a local search method. The proposed method generates multiple solutions to the inverse kinematics (IK) problem for any given spatial task and the MA chooses the most suitable configuration based on the search objectives. Since the dimension of each robotic link in this optimization is considered telescopic, the proposed method is able to find better solutions to the IK problem than GAs. The case study for a 3-DOF MRR shows that the MA finds solutions to the IK problem much faster than a GA with noticeably less reachability error. Additional case studies show that the proposed MA method can find multiple IK solutions in various scenarios and identify the fittest solution as a suboptimal configuration for the MRR.