Trajectory Optimization Control System of Intelligent Robot Based on Improved Particle Swarm Optimization Algorithm

Trajectory optimization is a hot topic in the field of intelligent robots, whose task is to plan the optimal motion trajectory that passes through a specified point and satisfies constraints such as velocity and acceleration based on a given target trajectory point. In the time optimization problem of robots, particle swarm optimization (PSO) has been widely applied in the time optimization problem of robotic arms due to its simple structure and adjustable parameters. This article conducts research on the intelligent robot trajectory optimization control system based on PSO, and the results show that through experimental data of three joints, it can be found that the optimized trajectory motion time has been reduced by an average of about 50%, achieving the expected goal. Compared with before optimization, PSO has stronger global search ability, faster convergence speed, and good stability, which can effectively improve robot work efficiency and maintain smooth operation. By dynamically adjusting the value of the learning factor in the particle swarm optimization algorithm through PSO, the particle swarm can search for the optimal value in a short period of time in the early stage of iteration and can quickly and accurately converge to the optimal solution in the later stage of iteration.