Inverse Kinematics of Robot Manipulator Based on BODE-CS Algorithm

Abstract

Differential evolution is a popular algorithm for solving global optimization problems. When tested, it has reportedly outperformed both robotic problems and benchmarks. However, it may have issues with local optima or premature convergence. In this paper, we present a novel BODE-CS (Bidirectional Opposite Differential Evolution–Cuckoo Search) algorithm to solve the inverse kinematics problem of a six-DOF EOD (Explosive Ordnance Disposal) robot manipulator. The hybrid algorithm was based on the differential evolution algorithm and Cuckoo Search algorithm. To avoid any local optimum and accelerate the convergence of the swarm, various strategies were introduced. Firstly, a forward-kinematics model was established, and the objective function was formulated according to the structural characteristics of the robot manipulator. Secondly, a Halton sequence and an opposite search strategy were used to initialize the individuals in the swarm. Thirdly, the optimization algorithms applied to the swarm were dynamically allocated to the Differential Evolution algorithm or the Cuckoo algorithm. Fourthly, a composite differential algorithm, which consisted of a dynamically opposite differential strategy, a bidirectional search strategy, and two other typically used differential strategies were introduced to maintain the diversity of the swarm. Finally, two adaptive parameters were introduced to optimize the amplification factor F and cross-over probability Cr. To verify the performance of the BODE-CS algorithm, two different tasks were tested. The experimental results of the simulation showed that the BODE-CS algorithm had high accuracy and a fast convergence rate, which met the requirements of an inverse solution for the manipulator.