| Compared with traditional CNC machine tools,industrial robots have the advantages of high flexibility,large working space,and wide range of machined objects.Therefore,industrial robots are more and more used in the field of machining.However,robot milling is currently only concentrated in the occasions with light load and moderate precision.The main reasons that restrict the wide application of robot milling are the low absolute positioning accuracy of the robot and the weak stiffness,which are easily affected by external forces during the milling process.Large deformation makes it difficult to meet the requirements of machining accuracy.In this thesis,the calibration technology and the pose optimization method are used to improve the precision of robot milling and improve the machining quality.This thesis firstly establishes the kinematics model of the robot according to the D-H parameter,and uses the Pieper criterion to solve the inverse kinematics of the robot.Then the differential transformation method is used to calculate the Jacobian matrix of the robot,and the kinematic properties of the robot,such as singularity and dexterity,are analyzed.Then,the absolute positioning accuracy of the robot is improved by the hierarchical calibration technology.Firstly,the error source of the robot is analyzed,and then the geometric parameter error model of the robot is established on the basis of considering the reduction ratio and coupling ratio.After that,the robot error is identified and compensated through two stages.The first stage uses the LM(Levenberg-Marquarelt)algorithm to identify the geometric parameter error of the robot and calculates the remaining residual.The second stage is based on the establishment of particle swarm-support vector regression.The residual error prediction model of the PSO-SVR algorithm is used to predict and compensate for the residual error remaining after correcting the geometric parameters.Finally,a six-degree-of-freedom industrial robot is used to test and verify that the average position error of the robot end center point is reduced from 5.866mm to 0.2116mm,which verifies the correctness and effectiveness of the calibration algorithm.Finally,a method of robot machining pose optimization is proposed to improve the stiffness performance during machining.Firstly,the static stiffness model of the robot is established,and the robot joint stiffness is identified on the basis of the model.Then,a deformation index ID considering the robot stiffness compensation matrix KC is proposed to evaluate the stiffness performance of the robot machining trajectory.With the goal of minimizing the deformation index ID,the robot pose optimization model is established under the consideration of kinematic constraints.Then a discrete Dijkstra optimization method is proposed to solve the global optimal solution of the model.Finally,the effectiveness of the pose optimization method is verified by a series of simulations and experiments on the MOTOMANES165D robot. |
