Path planning of welding robot based on deep learning

Abstract

In this work, a method that integrates deep learning and genetic algorithms is proposed to enhance the precision and efficiency of welding robots and achieve optimal robot path planning. The process involves using SolidWorks to create a 3D model, applying the D-H method to obtain data on the connecting rod parameters, performing theoretical calculations for both forward and inverse kinematics solutions, and utilizing the MATLAB robotics toolbox to validate these solutions. Furthermore, joint space trajectory planning is performed using the quintic polynomial curve method. Through analysis, we identified that abrupt acceleration changes at the initial and final positions significantly impact the smoothness of the motion process. The findings reveal that traditional artificial bee colonies tend to stabilize after 190 iterations, whereas genetic algorithms stabilize around 160 iterations, demonstrating superior convergence speed compared to the traditional ABC algorithm. The optimized approach yields an optimal welding obstacle avoidance path with rapid optimization speed and a stable process. The proposed method effectively addresses the obstacle avoidance path planning challenge for welding robots, showcasing improved convergence speed and stability compared to traditional methods.