An improved multi-objective antlion optimization algorithm for the optimal design of the robotic gripper

Abstract

This paper presents an evolutionary method to find the optimally designed gripper configuration for the automated material handling process. The optimal design of the robot gripper is a non-linear, multicriteria and multi-constraint problem. Evolutionary computational methods are introduced to overcome the difficulty associated while finding the optimal dimensions of the gripper. In this study, an Improved Multi-objective Antlion Optimisation Algorithm (I-MOALO) has been proposed to find the optimal dimensions of three different robotic gripper mechanisms. The multi-objective optimisation problem (MOOP) is formulated from a geometric model of the gripper configuration with conflicting objectives. The primary objective is to find optimum force at the tip of the gripper while grasping the object while satisfying the constraint using the improved multi-objective antlion optimisation algorithm. A comparative analysis has been conducted to evaluate the effectiveness and performance of the proposed algorithm with well-established multi-objective algorithms. The performance analysis for comparing multi-objective algorithm has been carried out using some of the well-recognised performance metrics such as hypervolume, diversity metric, optimiser overhead, and the ratio of non-dominated individuals. From the result obtained after the performance metric study, it is concluded that the multi-criteria design optimisation problem of robotic gripper effectively solved using the proposed I-MOALO algorithm.