Research On Trajectory Optimization Strategy Of Welding Robot Based On PLCopen

Robots can be regarded as an effective tool that can expand the ability of human work.In the process of human transformation of the world,flexible movement and compact structure,which are favored by designers and users in the research of welding robots.Research on the trajectory planning and trajectory tracking of tandem welding robots has always attracted widespread attention from scholars.1.This article introduces the definition,research status and development prospects of the robot.Taking ABB's IRB140 welding robot as the research object,it introduces the spatial pose representation method of the robot.By analyzing its parameter structure,the forward and inverse kinematics analysis is performed to prepare for the trajectory planning of the IRB140 welding robot.2.The general method of welding robot trajectory planning was studied.In the joint space,according to different constraints,IRB140 welding was deeply studied in Cartesian space The linear trajectory planning and circular trajectory planning of the robot,and the advantages and disadvantages of different trajectory planning are analyzed.3.The trajectory tracking method of the welding robot was studied.According to the composition of the execution mechanism of the tandem robot,the method of trajectory tracking of a single axis joint is used to improve the trajectory tracking capability of the welding robot.In the trajectory tracking of axis joints,particle swarm optimization is used to optimize the parameters of the feedforward controller,and the PLCopen platform is used for trajectory tracking experiments.4.Using virtual modeling technology,the simulation model of IRB140 welding robot was established in V-REP,and the trajectory planning simulation of the welding process was carried out according to the planned trajectory of the end welding gun.Changes during exercise.The consistency of the results of theoretical analysis and simulation experiments proves the effectiveness,rationality and feasibility of the trajectory planning algorithm used.