Minimum-Jerk Robot Joint Trajectory Using Particle Swarm Optimization

Abstract

In robot trajectory planning, finding the minimum-jerk joint trajectory is a crucial issue in robotics because most robots are asked to perform a smooth trajectory. Jerk, the third derivative of joint position of a trajectory, influences how smoothly and efficiently a robot moves. Thus, the minimum-jerk joint trajectory makes the robot control algorithm simple and robust. To find the minimum-jerk joint trajectory, it has been formulated as an optimization problem constrained by joint inter-knot parameters including initial joint displacement and velocity, intermediate joint displacement, and final joint displacement and velocity. In this paper, we propose a novel approach based on particle swarm optimization (PSO) with Kmeans clustering for solving the near-global minimum-jerk joint trajectory subject to different objective functions, which differs from previous work in its simple implementation and generalization. Computer simulations were conducted and showed the competent performance of our approach on a six degree-of-freedom robot manipulator.