Exploring Time-optimal Trajectory of Automatic Charging Manipulator with Improved Particle Swarm Optimization Algorithm
A particle swarm optimization(PSO)algorithm based on the nonlinear dynamic learning factor was proposed to solve the time optimization problem in the joint space trajectory planning of a truss charging manipulator.The workspace was obtained through kinematic analysis,and the 3-5-3 polynomial interpolation was introduced for the trajectory planning.The shortest motion time was sought through combining velocity constraints with acceleration constraints.The convergence speed of the improved PSO algorithm was compared with that of the basic PSO algorithm,and the variation of motion time of each joint before and after optimization was analyzed.The simulation results show that the convergence performance of the improved PSO algorithm is faster than that of the basic PSO algorithm and that the overall motion time is shortened by about 33%,confirming the feasibility of the improved PSO algorithm.
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