DESIGN OF OPTIMAL INVERSE KINEMATIC SOLUTION FOR HUMANOID ROBOTIC ARMS

Abstract

One of the main problems in robotics is the Inverse Kinematics (IK) problem. In this paper, three optimization algorithms are proposed to solve the IK of Humanoid Robotic Arms (HRAs). A Particle Swarm Optimization (PSO), Social Spider Optimization (SSO), and Black Hole Optimization (BHO) algorithms are proposed in order to optimize the parameters of the proposed IK. Also, in this paper, each optimization method is applied on both right and left arms to find the desired positions and required angles with a minimum error. Denavit-Hartenberg (D-H) method is used to design and simulate the mathematical model of HRAs for both arms in which each arm has five Degree Of Freedom (DOF). The HRAs model is tested for performance by several positions to be reached by both arms in the same time to find which optimization algorithm is better. Optimal solution obtained by SSO, PSO and BHO algorithms are evaluated and listed in comparison table between them. These optimization algorithms are assessed by calculating the Computational Time (CT) and Root Mean Squared Error (RMSE) for the absolute error vector of the positions. Calculation and simulation results showed that BHO algorithm is better than the other optimization algorithms from point of view of CT and RMSE. The worst RMSE is 0.0864 was calculated using PSO algorithm. But longer CT is 7.6521 second, which was calculated using SSO. While the best RMSE and shorter CT.are  and 3.0156 second respectively were calculated by BHO algorithm. Moreover, in this paper, the Graphical User Interface (GUI) is designed and built for motional characteristics of the HRAs model in the Forward Kinematics (FK) and IK. The optimization algorithms are designed using MATLAB package facilities to simulate the HRAs model and the solution of IK problem.