Research on trajectory planning based on multi-strategy improved particle swarm optimization

To improve the operational efficiency and motion smoothness of robotic arms used for hazardous chemical cleaning, a trajectory optimization approach is proposed based on a Multi-Strategy Improved Particle Swarm Optimization algorithm employing 3-5-3 polynomials. The global search capability and convergence precision of the optimization algorithm are enhanced through population initialization using Logistic Chaos Mapping, along with dynamic adjustment of inertia weights and learning factors. Comparative analysis indicates that the proposed method achieves higher optimization accuracy than the conventional Particle Swarm Optimization algorithm. Using the HSR-PT1600 robotic arm as the subject of investigation, time-optimal trajectory planning is implemented by integrating 3-5-3 segmented polynomials with the Multi-Strategy Improved Particle Swarm Optimization algorithm under velocity constraints. MATLAB simulation results confirm that the proposed method significantly enhances convergence speed, optimization accuracy, and trajectory execution efficiency.