A SELF-CALIBRATION METHOD FOR ARTICULATED ARM COORDINATE MEASURING MACHINES

2016 ◽  
Vol 40 (4) ◽  
pp. 645-655 ◽  
Author(s):  
Guanbin Gao ◽  
Jing Na ◽  
Xing Wu ◽  
Yu Guo
Keyword(s):  
Simulated Annealing Algorithm ◽  
Structural Parameters ◽  
Kinematic Model ◽  
Experimental Studies ◽  
Calibration Method ◽  
Constructive Method ◽  
Movement Trajectory ◽  
Coordinate Measuring Machines ◽  
Self Calibration ◽  
Calibration Methods

To improve the accuracy of articulated arm coordinate measuring machines (AACMM) and simplify the calibration process, an improved self-calibration method was proposed. Unlike the traditional calibration methods, which need external expensive precision instruments and elaborate setups, the proposed self-calibration method only requires a gauge to assist the data acquisition operation. By designing a movement trajectory of the AACMM, a series of joint angles can be obtained to form overdetermined equations based on the kinematic model of the AACMM. Therefore, the structural parameters of the AACMM can be obtained by solving the equations. Consequently, the calibration can be achieved by solving these equations. The coefficient matrix of the equations was further analyzed to simplify the equations, and a constructive method was presented to identify the structural parameters by solving the simplified equations with a modified simulated annealing algorithm, in which an optimized search strategy was applied to improve the robustness and efficiency. Experimental studies on an AACMM validate the convenience and effectiveness of the proposed AACMM self-calibration approach.

A Self-Calibration Method for Robotic Measurement System

1999 ◽  
Author(s):  
Chunhe Gong ◽  
Jingxia Yuan ◽  
Jun Ni
Keyword(s):  
Coordinate System ◽  
Measurement System ◽  
Calibration Method ◽  
Position Measurement ◽  
Robot Calibration ◽  
Robot System ◽  
Self Calibration ◽  
World Coordinate System ◽  
Robot Accuracy ◽  
Calibration Methods

Abstract Robot calibration plays an increasingly important role in manufacturing. For robot calibration on the manufacturing floor, it is desirable that the calibration technique be easy and convenient to implement. This paper presents a new self-calibration method to calibrate and compensate for robot system kinematic errors. Compared with the traditional calibration methods, this calibration method has several unique features. First, it is not necessary to apply an external measurement system to measure the robot end-effector position for the purpose of kinematic identification since the robot measurement system has a sensor as its integral part. Second, this self-calibration is based on distance measurement rather than absolute position measurement for kinematic identification; therefore the calibration of the transformation from the world coordinate system to the robot base coordinate system, known as base calibration, is not necessary. These features not only greatly facilitate the robot system calibration but also shorten the error propagation chain, therefore, increase the accuracy of parameter estimation. An integrated calibration system is designed to validate the effectiveness of this calibration method. Experimental results show that after calibration there is a significant improvement of robot accuracy over a typical robot workspace.

scholarly journals IMU-Based Online Kinematic Calibration of Robot Manipulator

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Guanglong Du ◽  
Ping Zhang
Keyword(s):  
Kalman Filter ◽  
Robot Manipulator ◽  
Experimental Studies ◽  
Estimation Method ◽  
Kinematic Parameter ◽  
Corner Detection ◽  
Measurement Unit ◽  
Robot Calibration ◽  
Self Calibration ◽  
Calibration Methods

Robot calibration is a useful diagnostic method for improving the positioning accuracy in robot production and maintenance. An online robot self-calibration method based on inertial measurement unit (IMU) is presented in this paper. The method requires that the IMU is rigidly attached to the robot manipulator, which makes it possible to obtain the orientation of the manipulator with the orientation of the IMU in real time. This paper proposed an efficient approach which incorporates Factored Quaternion Algorithm (FQA) and Kalman Filter (KF) to estimate the orientation of the IMU. Then, an Extended Kalman Filter (EKF) is used to estimate kinematic parameter errors. Using this proposed orientation estimation method will result in improved reliability and accuracy in determining the orientation of the manipulator. Compared with the existing vision-based self-calibration methods, the great advantage of this method is that it does not need the complex steps, such as camera calibration, images capture, and corner detection, which make the robot calibration procedure more autonomous in a dynamic manufacturing environment. Experimental studies on a GOOGOL GRB3016 robot show that this method has better accuracy, convenience, and effectiveness than vision-based methods.

scholarly journals Robotic tooling calibration based on linear and nonlinear formulations.

2021 ◽  
Author(s):  
Mohamed Helal
Keyword(s):  
Industrial Robot ◽  
Kinematic Model ◽  
Position Estimation ◽  
Error Model ◽  
Linear Formulation ◽  
Experimental Result ◽  
Robot Calibration ◽  
Nonlinear Formulation ◽  
Self Calibration ◽  
Calibration Methods

Industrial robot calibration packages, such as ABB CalibWare, are developed only for robot calibration. As a result, the robotic tooling systems designed and fabricated by the user are often calibrated in an ad-hoc fashion. In this thesis, a systematic way for robotic tooling calibration is presented in order to overcome this problem. The idea is to include the tooling system as an extended body in the robot kinematic model, from which two error models are established. The first error model is associated with the robot, while the second error model is associated with the tooling. Once the robot is fully calibrated, the first error will be reduced to the required accuracy. Thus, the method is focused on the second error model. For the tool error calibration, two formulations were used. The first is a linear formulation based on conventional calibration as well as self-calibration methods while the second is a nonlinear formulation. The conventional linear formulation was extensively investigated and implemented while the self-calibration was proven to be inadequate for the tooling calibration. Moreover, the nonlinear formulation was demonstrated to be very effective and accurate through experimental result. The end-effector position estimation as well as the tool pose estimation were obtained using a 3D vision system as an off-line error measurement technique.

Graph-optimisation-based self-calibration method for IMU/odometer using preintegration theory

2022 ◽  
pp. 1-20
Author(s):  
Shiyu Bai ◽  
Jizhou Lai ◽  
Pin Lyu ◽  
Yiting Cen ◽  
Bingqing Wang ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Experimental Tests ◽  
Calibration Method ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Self Calibration ◽  
Calibration Methods ◽  
Calibration Accuracy

Determination of calibration parameters is essential for the fusion performance of an inertial measurement unit (IMU) and odometer integrated navigation system. Traditional calibration methods are commonly based on the filter frame, which limits the improvement of the calibration accuracy. This paper proposes a graph-optimisation-based self-calibration method for the IMU/odometer using preintegration theory. Different from existing preintegrations, the complete IMU/odometer preintegration model is derived, which takes into consideration the effects of the scale factor of the odometer, and misalignments in the attitude and position between the IMU and odometer. Then the calibration is implemented by the graph-optimisation method. The KITTI dataset and field experimental tests are carried out to evaluate the effectiveness of the proposed method. The results illustrate that the proposed method outperforms the filter-based calibration method. Meanwhile, the performance of the proposed IMU/odometer preintegration model is optimal compared with the traditional preintegration models.

A Self-Calibration Method for Robotic Measurement System

1999 ◽  
Vol 122 (1) ◽  
pp. 174-181 ◽  
Author(s):  
Chunhe Gong ◽  
Jingxia Yuan ◽  
Jun Ni
Keyword(s):  
Coordinate System ◽  
Measurement System ◽  
Calibration Method ◽  
Position Measurement ◽  
Robot Calibration ◽  
Robot System ◽  
Self Calibration ◽  
World Coordinate System ◽  
Robot Accuracy ◽  
Calibration Methods

Robot calibration plays an increasingly important role in manufacturing. For robot calibration on the manufacturing floor, it is desirable that the calibration technique be easy and convenient to implement. This paper presents a new self-calibration method to calibrate and compensate for robot system kinematic errors. Compared with the traditional calibration methods, this calibration method has several unique features. First, it is not necessary to apply an external measurement system to measure the robot end-effector position for the purpose of kinematic identification since the robot measurement system has a sensor as its integral part. Second, this self-calibration is based on distance measurement rather than absolute position measurement for kinematic identification; therefore the calibration of the transformation from the world coordinate system to the robot base coordinate system, known as base calibration, is not necessary. These features not only greatly facilitate the robot system calibration, but also shorten the error propagation chain, therefore, increase the accuracy of parameter estimation. An integrated calibration system is designed to validate the effectiveness of this calibration method. Experimental results show that after calibration there is a significant improvement of robot accuracy over a typical robot workspace. [S1087-1357(00)01301-0]

scholarly journals Robotic tooling calibration based on linear and nonlinear formulations.

2021 ◽  
Author(s):  
Mohamed Helal
Keyword(s):  
Industrial Robot ◽  
Kinematic Model ◽  
Position Estimation ◽  
Error Model ◽  
Linear Formulation ◽  
Experimental Result ◽  
Robot Calibration ◽  
Nonlinear Formulation ◽  
Self Calibration ◽  
Calibration Methods

Industrial robot calibration packages, such as ABB CalibWare, are developed only for robot calibration. As a result, the robotic tooling systems designed and fabricated by the user are often calibrated in an ad-hoc fashion. In this thesis, a systematic way for robotic tooling calibration is presented in order to overcome this problem. The idea is to include the tooling system as an extended body in the robot kinematic model, from which two error models are established. The first error model is associated with the robot, while the second error model is associated with the tooling. Once the robot is fully calibrated, the first error will be reduced to the required accuracy. Thus, the method is focused on the second error model. For the tool error calibration, two formulations were used. The first is a linear formulation based on conventional calibration as well as self-calibration methods while the second is a nonlinear formulation. The conventional linear formulation was extensively investigated and implemented while the self-calibration was proven to be inadequate for the tooling calibration. Moreover, the nonlinear formulation was demonstrated to be very effective and accurate through experimental result. The end-effector position estimation as well as the tool pose estimation were obtained using a 3D vision system as an off-line error measurement technique.

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