Industrial robot trajectory planning based on improved pso algorithm
Shunjie Han, Xinchao Shan, Jinxin Fu, Weijin Xu, Hongyan Mi
Abstract
Abstract A particle swarm optimization (PSO) algorithm which can dynamically adjust learning factors is proposed to solve the problems of low efficiency and unstable operation of traditional industrial robots. The method uses piecewise polynomial interpolation to fit the trajectory, and uses an improved particle swarm algorithm to optimize the trajectory of industrial robots with time as a fitness function. This method effectively combines the piecewise polynomial interpolation function with PSO, avoids the complex process of particle swarm algorithm to construct the adaptation function, and improves the problem that the traditional PSO is more likely to fall into local extreme value in the early stage and convergence speed is slow in the later stage. Through experiments to obtain the motion pose, velocity and acceleration trajectory of each joint of the manipulator, we can know that this method can effectively realize the trajectory optimization of the industrial robot, and ensure the stability of the overall operation while improving the operating efficiency.