Robot Calibration Procedure Research Articles

Body-Mounted Robot for Image-Guided Percutaneous Interventions: Mechanical Design and Preliminary Accuracy Evaluation.

This paper presents a body-mounted, four degree-of-freedom (4-DOF) parallel mechanism robot for image-guided percutaneous interventions. The design of the robot is optimized to be light weight and compact such that it could be mounted to the patient body. It has a modular design that can be adopted for assisting various image-guided, needle-based percutaneous interventions such as arthrography, biopsy and brachytherapy seed placement. The robot mechanism and the control system are designed and manufactured with components compatible with imaging modalities including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). The current version of the robot presented in this paper is optimized for shoulder arthrography under MRI guidance; a Z-shaped fiducial frame is attached to the robot, providing accurate and repeatable robot registration with the MR scanner coordinate system. Here we present the mechanical design of the manipulator, robot kinematics, robot calibration procedure, and preliminary bench-top accuracy assessment. The bench-top accuracy evaluation of the robotic manipulator shows average translational error of 1.01 mm and 0.96 mm in X and Z axes, respectively, and average rotational error of 3.06 degrees and 2.07 degrees about the X and Z axes, respectively.

Calibration method for articulated industrial robots

Robot calibration is technique used to increase system positioning accuracy. In our research, we have reviewed self-calibration techniques of articulated industrial robots and proposed an original method to reduce production and maintenance down times. The proposed technique is using an inertial measurement unit (IMU) to measure robot poses, plus ultrasonic triangulation sensors to increase processing speed and reduce computational power. Thanks to all of that, robot is more responsive, i.e. we have an improved speed, reliability and accuracy in determining the orientation of the manipulators. The advantages of this method in comprising with the vision based calibration, is that it does not need the complex steps, such as camera calibration, image capture, memory and corner detection. This makes the robot calibration procedure more autonomous in a dynamic manufacturing environment. The proposed technique was applied at Six Degrees of Freedom (6-DOF) robot and compared to existing calibration methods.

Online Serial Manipulator Calibration Based on Multisensory Process Via Extended Kalman and Particle Filters

An online robot self-calibration method based on an inertial measurement unit (IMU) and a position sensor is presented in this paper. In this method, a position marker and an IMU are required to be rigidly attached to the robot tool to obtain the position of the manipulator from the position sensor and the orientation of the manipulator from the IMU in real time. An efficient approach that incorporates a Kalman filter (KF) and a particle filter to estimate the position and orientation of the manipulator is proposed in this paper. The use of these pose (orientation and position) estimation methods improves the reliability and accuracy of pose measurements. Finally, an extended KF is used to estimate the kinematic parameter errors. The primary advantage of this method over existing automated self-calibration methods is that it does not involve complex steps, such as camera calibration, corner detection, and laser alignment, which makes the proposed robot calibration procedure more autonomous in a dynamic manufacturing environment. Moreover, the reduction of complex steps improves the accuracy of calibration. Experimental studies on a GOOGOL GRB3016 robot show that the proposed method has better accuracy, convenience, and effectiveness.

Online robot calibration based on hybrid sensors using Kalman Filters

This paper presents an online robot self-calibration method based on an inertial measurement unit (IMU) and a position sensor. In this method, a position marker and an IMU are required to rigidly attach to the robot tool, which makes it possible to obtain the position of the manipulator from the position sensor and the orientation from the IMU in real time. An efficient approach which incorporates Kalman Filters (KFs) to estimate the position and the orientation of the manipulator is proposed in this paper. Using these pose (orientation and position) estimation methods will result in improving the reliability and accuracy of pose measurements. Finally, an Extended Kalman Filter (EKF) is used to estimate kinematic parameter errors. Compared with the existing self-calibration methods, the greatest advantage of this method is that it does not need any complex steps, such as camera calibration, corner detection and laser tracking, which makes the proposed robot calibration procedure more autonomy in a dynamic manufacturing environment. What׳s more, reduction in complex steps leads to improving the accuracy of calibration. Experimental studies on a GOOGOL GRB3016 robot show that this proposed method has better accuracy, convenience and effectiveness.

IMU-Based Online Kinematic Calibration of Robot Manipulator

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.

Uncertainty estimation in robot kinematic calibration

Currently, the results of a robot calibration procedure are expressed generally in terms of the position and orientation error for a set of locations and orientations, which have been obtained from the previously identified kinematic parameters. In this work, a technique is presented to evaluate the calibration uncertainty for a robot arm calibrated using the circle point analysis method. The method developed is based on the probability distribution propagation calculation recommended by the Guide to the Expression of Uncertainty of Measurement and on the Monte Carlo method . This method makes it possible to calculate the uncertainty in the identification of each single robot parameter, and thus, to estimate the robot positioning uncertainty due to the calibration uncertainty, rather than based on a single set locations and orientations that are previously defined for a unique set of identified parameters. Additionally, this technique allows for the establishment of the best possible conditions for the data capture test, which identifies parameters and determines which of them have the least possible calibration uncertainty. This determination is based on the variables involved in the data capture process by propagating their influence up to the final robot accuracy. ► This method allows uncertainty evaluation in robot kinematic calibration procedures. ► Robot position and orientation can be evaluated offline depending on calibration. ► Data capture test for calibration can be optimised to minimise pose uncertainty. ► Individual influence of each parameter in the calibration phase can be characterised.

Autonomous robot calibration using vision technology

Unlike the traditional robot calibration methods, which need external expensive calibration apparatus and elaborate setups to measure the 3D feature points in the reference frame, a vision-based self-calibration method for a serial robot manipulator, which only requires a ground-truth scale in the reference frame, is proposed in this paper. The proposed algorithm assumes that the camera is rigidly attached to the robot end-effector, which makes it possible to obtain the pose of the manipulator with the pose of the camera. By designing a manipulator movement trajectory, the camera poses can be estimated up to a scale factor at each configuration with the factorization method, where a nonlinear least-square algorithm is applied to improve its robustness. An efficient approach is proposed to estimate this scale factor. The great advantage of this self-calibration method is that only image sequences of a calibration object and a ground-truth length are needed, which makes the robot calibration procedure more autonomous in a dynamic manufacturing environment. Simulations and experimental studies on a PUMA 560 robot reveal the convenience and effectiveness of the proposed robot self-calibration approach.

Calibration of an smt robot assembly cell

AbstractA new calibration method for an assembly robot cell is described. The proposed method is a combination of a model‐free, numerical, relative robot calibration procedure and a procedure for the robot periphery calibration. Two important simplifications based on the study of an assembly process are introduced into the calibration strategy. A robot is calibrated in a task (Cartesian) space. The robot workspace and the number of calibrated degrees of freedom (dof) in the task space are reduced in accordance with the difficulty measure of the task. An automatic measurement system for measuring the relative robot accuracy was developed. An original principle of transforming the robot endpoint approach distance into the one‐dimensional position displacement error is introduced. The accuracy errors of each particular calibrated dof in the task space is measured separately. The error tables are used in a direct robot calibration procedure that is based on the linear interpolation of the discrete position‐error functions. An iterative inverse calibration algorithm used in a particular robot cell is described. An efficient sensor‐based system for an additional simultaneous robot periphery calibration is presented. The implementation of the proposed calibration methodology in the pick‐and‐place robot cell for Surface Mount Technology (SMT) is presented. © 1994 John Wiley & Sons, Inc.

Robot Vision System For Depalletizing Steel Cylindrical Billets

A vision system is described for depalletizing steel cylindrical billets for a forging application. An algorithm for accurately locating and measuring the billets is described in some detail. Highlights of this discussion include an algorithm for adaptive threshold selection to accommodate changing image brightnesses and a special robot calibration procedure that enables inference of depth using only a single camera view combined with prior knowledge about the scene. Experimental results show that the system provides accurate measurements in spite of poor, inconsistent contrast.