An approach for design optimization of 3R manipulator using Adaptive Cuckoo Search algorithm

Abstract

In this study, the workspace volume of a 3R manipulator has been maximized using a biologically inspired optimization algorithm, namely Adaptive Cuckoo Search (ACS) algorithm. The proposed algorithm is tested on four diverse cases involving different constraints and without imposing any constraint. The outcomes of this study are compared with the standard results of different heuristics, such as Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Bacteria Foraging Algorithm (BFA), and Cuckoo Search (CS) algorithm. Statistical tests were performed to test the superiority of the proposed algorithm. Further, the cross-sectional area of the voids in the workspace has been estimated. The convergence study along with the coefficient of variation analysis identifies the critical kinematic parameters. A constraint conformation study has been performed to investigate the relative importance of the constraints. In addition, to ensure the applicability of the proposed algorithm in practice it is tested by using the kinematic parameters of two existing industrial robot manipulators (KUKA KR-30 and Mitsubishi MRP-700A). It has been found that the predicted results from the proposed algorithm for the two robot manipulators are in line with the actual values.