Identification of Kinematic Errors of Five-axis Machine Tool Trunnion Axis from Finished Test Piece

Compared with the traditional non-cutting measurement,machining tests can more accurately reflect the kinematic errors of five-axis machine tools in the actual machining process for the users.However,measurement and calculation of the machining tests in the literature are quite difficult and time-consuming.A new method of the machining tests for the trunnion axis of five-axis machine tool is proposed.Firstly,a simple mathematical model of the cradle-type five-axis machine tool was established by optimizing the coordinate system settings based on robot kinematics.Then,the machining tests based on error-sensitive directions were proposed to identify the kinematic errors of the trunnion axis of cradle-type five-axis machine tool.By adopting the error-sensitive vectors in the matrix calculation,the functional relationship equations between the machining errors of the test piece in the error-sensitive directions and the kinematic errors of C-axis and A-axis of five-axis machine tool rotary table was established based on the model of the kinematic errors.According to our previous work,the kinematic errors of C-axis can be treated as the known quantities,and the kinematic errors of A-axis can be obtained from the equations.This method was tested in Mikron UCP600 vertical machining center.The machining errors in the error-sensitive directions can be obtained by CMM inspection from the finished test piece to identify the kinematic errors of five-axis machine tool trunnion axis.Experimental results demonstrated that the proposed method can reduce the complexity,cost,and the time consumed substantially,and has a wider applicability.This paper proposes a new method of the machining tests for the trunnion axis of five-axis machine tool.

Accurate parameter estimation of systematic odometry errors for two-wheel differential mobile robots

Odometry using incremental wheel encoder odometry suffers from the accumulation of kinematic sensors provides the relative robot pose estimation. However, the modeling errors of wheels as the robot＇s travel distance increases. Therefore, the systematic errors need to be calibrated. The University of Michigan Benchmark（UMBmark） method is a widely used calibration scheme of the systematic errors in two wheel differential mobile robots. In this paper, the accurate parameter estimation of systematic errors is proposed by extending the conventional method. The contributions of this paper can be summarized as two issues. The first contribution is to present new calibration equations that reduce the systematic odometry errors. The new equations were derived to overcome the limitation of conventional schemes. The second contribu tion is to propose the design guideline of the test track for calibration experiments. The calibration performance can be im proved by appropriate design of the test track. The simulations and experimental results show that the accurate parameter es timation can be implemented by the proposed method.

Machining Complex Oriented Compensation System for Generalized Kinematic Errors

This paper puts forward a machining complex oriented compensation strategy for the generalized kinematic errors (GKEs). According to this strategy, the error map, which is constructed by using the off line measuring information of the machined workpiece, is not oriented for the machine tool but for the machining complex to compensate the GKEs. The error map is derived by the proposed predictive learning control algorithm (PLCA), which is supported by the information model of machining complex. Experimental results show that the machining complex oriented GKEs compensation strategy and the information model based PLCA is effective.

Analysis of Systematic Error Influences on Accuracy of Airborne Laser Scanning Altimetry

The error sources related to the laser rangefinder, GPS and INS are analyzed in details. Several coordinates systems used in airborne laser scanning are set up, and then the basic formula of system is given. This paper emphasizes on discussing the kinematic offset correction between GPS antenna phase center and laser fired point. And kinematic time delay influence on laser footprint position, the ranging errors, positioning errors, attitude errors and integration errors of the system are also explored. Finally, the result shows that the kinematic time delay can be neglected as compared with other error sources. The accuracy of the coordinates is not only influenced by the amplitude of the error, but also controlled by the operation parameters such as flight height, scanning angle amplitude and attitude magnitude of the platform.

Robot Calibration Using Active Vision-based Measurement

This paper presents an efficient robot calibration method with non-contact vision metrology. Using the coplanar pattern to calibrate camera made the active-vision-based end-effector pose measurement be a feasible and costeffective way. Kinematic parameter errors were linearized and identified through two-step procedure, thus the singular and non-linear condition was overcome. These errors were then compensated using inverse model method. The whole calibration process is flexible, easy to implement and prevents the error propagation from the earlier stages to the later ones. Calibration was performed on MOTOMAN SV3industrial robot. Experiment results show that the proposed method is easy to setup and with satisfactory accuracy.

Precision measurement and compensation of kinematic errors for industrial robots using artifact and machine learning

Industrial robots are widely used in various areas owing to their greater degrees of freedom(DOFs)and larger operation space compared with traditional frame movement systems involving sliding and rotational stages.However,the geometrical transfer of joint kinematic errors and the relatively weak rigidity of industrial robots compared with frame movement systems decrease their absolute kinematic accuracy,thereby limiting their further application in ultraprecision manufacturing.This imposes a stringent requirement for improving the absolute kinematic accuracy of industrial robots in terms of the position and orientation of the robot arm end.Current measurement and compensation methods for industrial robots either require expensive measuring systems,producing positioning or orientation errors,or offer low measurement accuracy.Herein,a kinematic calibration method for an industrial robot using an artifact with a hybrid spherical and ellipsoid surface is proposed.A system with submicrometric precision for measuring the position and orientation of the robot arm end is developed using laser displacement sensors.Subsequently,a novel kinematic error compensating method involving both a residual learning algorithm and a neural network is proposed to compensate for nonlinear errors.A six-layer recurrent neural network(RNN)is designed to compensate for the kinematic nonlinear errors of a six-DOF industrial robot.The results validate the feasibility of the proposed method for measuring the kinematic errors of industrial robots,and the compensation method based on the RNN improves the accuracy via parameter fitting.Experimental studies show that the measuring system and compensation method can reduce motion errors by more than 30%.The present study provides a feasible and economic approach for measuring and improving the motion accuracy of an industrial robot at the submicrometric measurement level.

Random Weighting Estimation Method for Dynamic Navigation Positioning

This paper presents a new random weighting estimation method for dynamic navigation positioning. This method adopts the concept of random weighting estimation to estimate the covariance matrices of system state noises and observation noises for controlling the disturbances of singular observations and the kinematic model errors. It satisfies the practical requirements of the residual vector and innovation vector to sufficiently utilize observation information, thus weakening the disturbing effect of the kinematic model error and observation model error on the state parameter estimation. Theories and algorithms of random weighting estimation are established for estimating the covariance matrices of observation residual vectors and innovation vec- tors. This random weighting estimation method provides an effective solution for improving the positioning accuracy in dynamic navigation. Experimental results show that compared with the Kalman filtering, the extended Kalman filtering and the adaptive windowing filtering, the proposed method can adaptively determine the covariance matrices of observation error and state error, effectively resist the disturbances caused by system error and observation error, and significantly improve the positioning accu- racy for dynamic navigation.