Social Spider Optimization for Solving Inverse Kinematics for Both Humanoid Robotic Arms

Abstract

The non-linearity of Inverse kinematics (IK) equations are complex. A Social Spider Optimization (SSO) and Particle Swarm Optimization (PSO) algorithms are proposed in this paper to solve the IK of Humanoid Robotic Arms (HRA). These optimization algorithms are applied on both right and left arms to find the required angles and desired positions with minimum error. Mathematical model of HRA is simulated depending on Denavit-Hartenberg (D-H) method for each arm in which each arm has five Degree Of Freedom (DOF). Performance of HRA model is tested by many positions to be reach by both arms to obtain which optimization algorithm is better. Comparisons are listed between optimal solution using PSO and SSO algorithms. These optimization algorithms are assessed by calculating the Root Mean Squared Error (RMSE) for the absolute error vector of the positions. Simulations and calculation results showed that RMSE value using SSO is less than RMSE value using PSO. We got the largest RMSE of 0.0864 using PSO algorithm. while the lowest possible error, which is 0.00004 was acquired by SSO algorithm. The Graphical User Interface (GUI) is designed and built for motional characteristics of the HRA model in the Forward Kinematics (FK) and IK.