Research On Path Planning Of Automobile Spot Welding Robot Based On Improved Grey Wolf Optimization

With the continuous development and innovation of the industrial era,industrial robots have been widely used in the field of automobile production and manufacturing,and the welding application of spot welding robots has gradually replaced manual welding.The application of spot welding robots not only improves production efficiency,but also ensures welding quality.At the same time,in order to improve the work efficiency of spot welding robots and ensure their stability,manufacturers’ demand for spot welding robots is also gradually increasing.For example,when the robot moves between welding points,each joint should be kept stable and impact force should be minimized to ensure the safety of the welding task;While improving the welding efficiency of the robot,corresponding constraints are met.The artificially planned welding path has random factors and lacks rationality.Therefore,scientific path planning is indispensable,and selecting intelligent algorithms for spot welding path planning is of great significance.(1)This thesis studies the pose theory of a six axis robot,establishes its linkage model,and analyzes the transformation form of the robot’s end pose matrix.Kinematics analysis is carried out based on CRP robot,and a six axis manipulator model is established through matlab to verify the correctness of theoretical analysis and lay a theoretical foundation for the following trajectory planning.(2)This thesis analyzes the motion characteristics of parabolic transition linear interpolation trajectory and quintic function trajectory,selects the quintic function trajectory planning method suitable for spot welding robot operation,and uses quintic function to carry out trajectory planning simulation for six axis robot,to verify the stability of spot welding robot during spot welding.To ensure the safety of the robot as a prerequisite,appropriate time adjustments are made to the robot trajectory to improve joint motion efficiency.(3)This thesis uses the Grey Wolf Optimization algorithm to optimize the path of the robot’s spot welding task.Analyze the advantages and disadvantages of the Grey Wolf Optimization algorithm and make improvements to address its shortcomings.Change the variation form of the original convergence factor and introduce a logarithmic convergence method.Propose a cosine mutation strategy and retain effective individuals.Based on the standard function library,the improved algorithm in this article is tested.By comparing it with the original algorithm and the Whale Optimization algorithm,it is proven that the comprehensive performance of the improved algorithm in this thesis is significantly improved.(4)To further verify the feasibility of the improved Grey Wolf Optimization algorithm in spot welding tasks,10 sets of city examples from the TSPLIB database were selected for testing the algorithm.Compared with the original Grey Wolf Optimization algorithm and Whale Optimization algorithm through simulation,the results show that the improved Grey Wolf Optimization algorithm has significant advantages in spot welding path planning.Import the door welding point data into the algorithm and simulate it with the other two algorithms.The final simulation results were used as a basis for conducting path planning experiments on the car door,verifying the effectiveness and feasibility of spot welding robot path planning for the car door.