Inverse Kinematics Solution of Redundant Degree of Freedom Robot Based on Improved Quantum Particle Swarm Optimization

To overcome the shortcomings of conventional methods in solving inverse kinematics problems of redundant degree-of-freedom robots, this paper converts the inverse kinematics problems of the manipulator into target optimization problems, and it presents an Improved quantum particle swarm optimization algorithm (IQPSO), which is used to solve the inverse kinematics problems. In this algorithm, quantum behavior is added to the particle swarm optimization algorithm, and the improved contraction expansion coefficient with first large and then small is adopted, which can not only traverse the whole search space, but also improve the convergence speed and solution accuracy. Based on the forward kinematics equation, this paper takes the position error of the end-effector of the robot and the minimum energy consumption in the process of the robot motion as the optimization objectives, and it conducts simulation experiments on a 7-degree-of-freedom (7-DOF) robot. The experimental result shows that the IQPSO algorithm has faster convergence speed and higher solution accuracy than the traditional particle swarm optimization algorithm (PSO) and quantum particle swarm optimization algorithm (QPSO). It is an effective method to solve the inverse kinematics problem of the robot.