Hybrid Taguchi DNA Swarm Intelligence for Optimal Inverse Kinematics Redundancy Resolution of Six‐DOF Humanoid Robot Arms

Abstract

This paper presents a hybrid Taguchi deoxyribonucleic acid (DNA) swarm intelligence for solving the inverse kinematics redundancy problem of six degree‐of‐freedom (DOF) humanoid robot arms. The inverse kinematics problem of the multi‐DOF humanoid robot arm is redundant and has no general closed‐form solutions or analytical solutions. The optimal joint configurations are obtained by minimizing the predefined performance index in DNA algorithm for real‐world humanoid robotics application. The Taguchi method is employed to determine the DNA parameters to search for the joint solutions of the six‐DOF robot arms more efficiently. This approach circumvents the disadvantage of time‐consuming tuning procedure in conventional DNA computing. Simulation results are conducted to illustrate the effectiveness and merit of the proposed methods. This Taguchi‐based DNA (TDNA) solver outperforms the conventional solvers, such as geometric solver, Jacobian‐based solver, genetic algorithm (GA) solver and ant, colony optimization (ACO) solver.