Robot Time Optimal Trajectory Planning Based on Improved Simplified Particle Swarm Optimization Algorithm

In order to tackle the robot trajectory planning problem with the short running time as the optimization goal, a time-optimal trajectory planning algorithm was presented based on improved simplified particle swarm optimization (ISPSO). The robot's trajectory was constructed by 3-5-3 polynomial interpolation in the joint space of the robot. Under the condition of satisfying the velocity constraint, the objective function was constructed by the sum of the time intervals between each node. ISPSO was used to optimize the objective function. The algorithm was improved by optimizing the inertia weight updating method and introducing a golden sine segmentation algorithm as an optimization operator. Compared with other particle swarm optimization algorithms, ISPSO had higher search velocity and accuracy. The effectiveness of the proposed algorithm was demonstrated through simulations using the PUMA 560 industrial robot, which resulted in a 19% reduction in time compared to the simplified particle swarm algorithm. The simulation results show that ISPSO achieved time optimization under the condition of velocity constraint, which proved its superiority in trajectory planning.

Automated summary

This paper proposes a time-optimal trajectory planning algorithm based on improved simplified particle swarm optimization (ISPSO) to solve the robot trajectory planning problem with the optimization goal of short running time. The robot's trajectory is constructed by 3-5-3 polynomial interpolation in the joint space of the robot. The objective function is constructed by the sum of the time intervals between each node while satisfying the velocity constraint. ISPSO is used to optimize the objective function by improving the inertia weight updating method and introducing a golden sine segmentation algorithm as an optimization operator. Compared with other particle swarm optimization algorithms, ISPSO demonstrates higher search velocity and accuracy. The effectiveness of the proposed algorithm is demonstrated through simulations using the PUMA 560 industrial robot, showing a 19% reduction in time compared to the simplified particle swarm algorithm. The simulation results prove that ISPSO achieves time optimization under the condition of velocity constraint, indicating its superiority in trajectory planning.