Analyzing the performances of evolutionary multi-objective optimizers on design optimization of robot gripper configurations

Abstract

Robot grippers are widely used in a variety of areas requiring automation, precision, and safety. The performance of the grippers is directly associated with their design. In this study, four different multiobjective metaheuristic algorithms including particle swarm optimization (MOPSO), artificial algae algorithm (MOAAA), grey wolf optimizer (MOGWO) and nondominated sorting genetic algorithm (NSGA-II) were applied to two different configurations of highly nonlinear and multimodal robot gripper design problem including two objective functions and a certain number of constraints. The first objective is to minimize the difference between minimum and maximum forces for the assumed range in which the gripper ends are displaced. The second objective is force transmission rate that is the ratio of the actuator force to the minimum holding force obtained at the gripper ends. The performance of the optimizers was examined separately for each configuration by using pareto-front curves and hyper-volume (HV) metric. Performances of the optimizers on the specific problem were compared with results of previously proposed algorithms under equal conditions. With respect to these comparisons, the best-known results of the configurations were obtained. Furthermore, the pareto optimal solutions are thoroughly examined to present the relationship between design variables and objective functions.