Trajectory optimization of manipulator based on particle swarm optimization with mutation strategy

Abstract In order to solve the problems of slow convergence speed and low convergence accuracy of adaptive particle swarm algorithm, a particle swarm optimization algorithm based on the mutation strategy in the differential evolution algorithm was proposed.Based on the adaptive inertia weight update formula with the improved power finger function and the particle swarm optimization with dynamic learning factor, the variation link in the differential evolution algorithm is introduced to enhance the ability of particles to explore the solution space and improve the convergence speed and accuracy of the algorithm.Taking the medium-sized manipulator PUMA560 as the research object, the posture of each joint under the constraint of key points was determined by the robot inverse kinematics, and the spatial pose curve fitting of the six joints of the manipulator was carried out by using the five-order polynomial.Under the constraint of the maximum angular velocity of rad/s, the shortest motion time of the manipulator is the goal to be optimized, and the mutation strategy is introduced in the iteration, and the particle population under the current iteration is used as the parent to mutate and generate a new population, so as to improve the diversity of the population.Simulation experiments on the MATLAB platform show that the convergence speed and convergence accuracy of the particle swarm optimization algorithm with the mutation strategy for the adaptive particle swarm optimization algorithm, and the pose curves of each joint change are smooth and continuous.The final optimization time was reduced by 50.01% compared to the initial planning time.