Time-Optimal Robotic Arm Trajectory Planning for Coating Machinery Based on a Dynamic Adaptive PSO Algorithm

Abstract

To address the issues of low trajectory planning efficiency, high motion impact, and poor operational stability in robotic arms during the automatic loading and unloading of aluminum blocks in coating machinery, a time-optimal trajectory optimization method based on a dynamically adaptive Particle Swarm Optimization (PSO) algorithm is proposed. First, the loading and unloading process of aluminum block components is described, followed by a kinematic analysis of the robotic arm in joint space. Then, the “3-5-3” hybrid polynomial interpolation method is used to fit the robotic arm’s motion trajectory and simulate the analysis. Finally, with the robotic arm’s operation time as the objective function, the dynamically adaptive PSO algorithm is applied to optimize the trajectory constructed by hybrid polynomial interpolation, achieving time-optimal trajectory planning for aluminum block handling. The results demonstrate that the proposed method successfully reduces the trajectory planning times for condition 1 and condition 2 from 6 s to 3.59 s and 3.14 s, respectively, improving overall efficiency by 40.2% and 47.7%. This confirms the feasibility of the method and significantly enhances the efficiency of automated loading and unloading tasks for aluminum blocks in coating machinery. The proposed method is highly adaptable and well-suited for real-time trajectory optimization of robotic arms. It can also be broadly applied to other robotic systems and manufacturing processes, enhancing operational efficiency and stability.