Solve the Inverse Kinematics of Robot Arms using Sand Cat Swarm Optimization (SCSO) Algorithm

Abstract

Inverse kinematics of robot arms is one of the optimization problems. The six joints of the Six degrees of freedom PUMA 560 robot arm are considered as an inverse kinematics system in this study. There are many possibilities for joint angles in this problem, making the analysis difficult to determine using deterministic rules. Several metaheuristic algorithms are presented in this paper for solving the inverse kinematics problem of robot arms, including the sand cat swarm optimization algorithm (SCSO). Additionally, we compare the particle swarm optimization (PSO), grey wolf optimization (GWO), and whale optimization algorithm (WOA) optimization algorithms to see which is most efficient. In this study, meta-heuristic algorithms are used to determine the inverse kinematics of the robotic arm, which are essential to tracking a rectangular trajectory in three dimensions. A cost function analysis was conducted in order to further analyze the results. In addition, the results of the comparison of the meta-heuristic algorithms to the inverse kinematics task showed that the SCSO algorithm performed better than the competitors.