Space Motion Control Optimization of Robot Arm Based on Modeling Analysis and RRT Algorithm

The existing spatial motion control methods for industrial production robotic arms suffer from sluggish convergence efficiency and inadequate control accuracy. With the aim of resolving this matter, the research proposes an optimization method for spatial motion control of robotic arms based on modeling analysis and the fast extended random tree algorithm. The study first models and analyzes the spatial motion of the robotic arm, and combines the Monte Carlo method for random sampling to create a significant amount of feasible paths. The difference between the feasible path and the planned path is that the feasible path is optimized through multiple iterations, gradually approaching the optimal solution, while the planned path is a predetermined fixed path. Afterwards, the improved fast expansion random tree algorithm is used to optimize the path and achieve efficient planning of the robotic arm path. Finally, the proportional integral derivative control method and the recursive least squares method are employed to flexibly control the spatial motion end of the robotic arm, improving the motion accuracy and stability of the robotic arm. The experimental outcomes reveal that the node utilization rate and planned path length of the proposed control method in simulation analysis are 20.71% and 111.61 cm, respectively, and the fitting degree between its control trajectory and the ideal trajectory exceeds 0.98. In practical applications, the average time required for the robotic arm using the proposed method is only 12.5 s, and the quality pass rate remains above 98%. In addition, the completion rate of its winding task is 99.5%. The proposed control optimization approach can effectively improve the control accuracy and production quality of industrial production robotic arms.