Visualized trajectory planning of flexible redundant robotic arm using a novel hybrid algorithm

Abstract

Open-chain robots simulating human upper extremity have been developed to perform various tasks, and trajectory optimization significantly affects the performance of flexible redundant robotic arm. In this paper, a novel hybrid algorithm combing crossover and mutation particle swarm optimization (CMPSO) with interior point method (IPM) is proposed for motion planning of the three-link robot with rotational joints. Based on stochastically generated locations in three-dimension space, the minimum-length motion path of robot end effector is obtained using CMPSO. Subsequently, the gravitational potential energy is selected as minimization objective, and the desired robot postures are optimized while the robot end effector moving along the obtained motion path. Meanwhile, the optimized robot trajectories and postures are presented in visual form. Simulation results indicate that the proposed hybrid algorithm has promising potential for the trajectory searching missions of flexible redundant robotic arms, and that the visualized trajectories effectively contribute to analyzing robot motion.