Kinematics Solution using Metaheuristic Algorithms

Abstract

In this paper, Artificial bee colony (ABC) and Grey wolf optimization (GWO) techniques have been proposed to find kinematics solution. Inverse kinematics is an important parameter for the movement of joints from one location to end-effectors' position. During the movement to reach to the destination various errors will incur. Different evolutionary and metaheuristics have been proposed to solve the inverse kinematics solution with minimum errors. ABC and GWO are two novel metaheuristic techniques that are based on population. These algorithms are used to minimize the errors present in the inverse kinematics solution. Errors to be calculated are position error and absolute error. GWO takes less time than ABC algorithm during the iteration. ABC and GWO are naturally inspired swarm techniques.