Trajectory planning of sorting robot based on improved particle swarm optimization algorithm

Aiming at the low efficiency of trajectory planning and unstable operation of sorting robot, a particle swarm optimization (PSO) is proposed, which can dynamically adjust the learning factor. In this method, the trajectory of the sorting robot is fitted by piecewise polynomial interpolation, and the trajectory of the sorting robot is optimized by using the improved PSO as the fitness function of time. The piecewise polynomial interpolation function is effectively combined with PSO, and the complex process of the traditional PSO adaptation function is avoided. The problems that it is easy to fall into local extreme value in the early stage and slow convergence rate in the later stage are improved. Experiments show that this method can optimize the joint motion posture, speed and acceleration trajectory of the sorting robot, and effectively improve the operating efficiency and stability of the sorting robot.