Optimal Trajectory Planning for Robotic Arm Based on Improved Whale Optimization Algorithm

Abstract

An integrated optimal trajectory planning approach is suggested to increase the shock resilience and operational efficiency of industrial robots. First, the refractive reverse learning mechanism, Lévy flights, and nonlinear convergence factor are incorporated to the basic whale optimization algorithm to create the improved whale optimization algorithm (IWOA). Then the superiority and efficacy of the improved algorithm are confirmed using the CEC2017 test function set. Subsequently, trajectory planning in the robot joint space utilizing 3-5-3 polynomial functions determines the whole optimization objective function in terms of time, impact, and energy consumption. When the joint motion curves derived from the improved algorithm are continuous and devoid of sudden shifts, the efficacy of the trajectory planning approach has finally been verified.