Calibration Of Kinematic Parameters Research Articles

Optimization of pose samples for calibration of large component docking mechanisms

ABSTRACT The compensation for the positioning error of large component docking mechanisms (LCDM) is the core technology for improving the assembly quality of aircraft. The pose samples (PSs) of the LCDM for kinematic parameter calibration significantly impact the final effect of error compensation. This paper proposes a PSs optimization method for kinematic parameter calibration of LCDM. Firstly, the kinematic model and the principle of kinematic parameter identification of LCDM are introduced. Then, an optimization model for the PSs is established based on the proposed observability index of the kinematic parameters, and a dynamic weighted particle swarm optimization (PSO) algorithm is used to optimize the PSs. Thirdly, the effectiveness of the proposed PSs optimization method is verified by Monte Carlo simulation, and the impacts of the number of measurement points (MPs) and PSs on calibration accuracy are analyzed. Finally, a docking system for simulated LAC is built in the laboratory, and the calibration experiments of the LCDM are conducted based on the optimized PSs. The experimental results show that the assembly accuracy of large components can be significantly improved by reasonably arranging the MPs and using the PSs optimization method proposed in this article.

A Precise Calibration Method for the Robot-Assisted Percutaneous Puncture System

The precision and stability of the Robot-Assisted Percutaneous Puncture (RAPP) system have become increasingly crucial with the widespread integration of robotic technology in the field of medicine. The accurate calibration of the RAPP system prior to surgery significantly influences target positioning performance. This study proposes a novel system calibration method that simultaneously addresses system hand–eye calibration and robot kinematic parameters calibration, thereby enhancing the surgery success rate and ensuring patient safety. Initially, a Closed-loop Hand–eye Calibration (CHC) method is employed to rapidly establish transformation relationships among system components. These CHC results are then integrated with nominal robot kinematic parameters to preliminarily determine the system calibration parameters. Subsequently, a hybrid algorithm, combining the regularized Levenberg–Marquardt (LM) algorithm and a particle filtering algorithm, is utilized to accurately estimate the system calibration parameters in stages. Numerical simulations and puncture experiments were conducted using the proposed system calibration method and other comparative methods. The experimental results revealed that, among several comparative methods, the approach presented in this paper yields the greatest improvement in the puncture accuracy of the RAPP system, demonstrating the accuracy and effectiveness of this method. In conclusion, this calibration method significantly contributes to enhancing the precision, operational capability, and safety of the RAPP system in practical applications.

A two-step method for kinematic parameters calibration based on complete pose measurement—Verification on a heavy-duty robot

A two-step method for kinematic parameters calibration based on complete pose measurement—Verification on a heavy-duty robot

Visual-Biased Observability Index for Camera-Based Robot Calibration

Abstract Efficient robot integration can be realized by matching real and virtual robots, and accurate robot models can be generated by kinematic parameter calibration. End-effector pose selection for pose measurement to discover the positioning errors is critical in kinematic parameter calibration. Ideal pose selection maximizes calibration accuracy for a defined measurement uncertainty and optimizes measurement cost and utility. In the design of the pose selection process, observability indices are widely accepted criteria for effective pose selection to evaluate calibration performance. Observability indices represent the effect of uncertainty in the measured end-effector poses on the calibrated parameters. However, unlike expensive direct measurement using laser, low-cost camera-based kinematic calibration estimates the end-effector poses from the marker points in the captured image. The variance of the detected marker positions biases the end-effector poses and, eventually, the calibrated parameters. Therefore, this study proposes extended observability indices for pose selection based on this bias to realize accurate calibration with a low-cost camera. The target observability index is O1, a scale-free, reliable index used in kinematic calibration. Considering the visual bias, we extended it as Ov1. This study evaluated Ov1 by comparing the positioning accuracies calibrated on poses selected by maximizing it, original O1, O3 known as the best criterion to restrain the end-effector positioning uncertainty, and Ov3, which is the extended O3 for consistency. A ball-bar test showed that the poses selected by the index Ov1 exhibited higher positioning accuracy than the other indices.

Kinematic Calibration of a Space Manipulator Based on Visual Measurement System with Extended Kalman Filter

The calibration of kinematic parameters has been widely used to improve the pose (position and orientation) accuracy of the robot arm. Intelligent measuring equipment with high accuracy is usually provided for the industrial manipulator. Unfortunately, large noise exists in the vision measurement system, which is provided for space manipulators. To overcome the adverse effect of measuring noise and improve the optimality of calibrating time, a calibration method based on extended Kalman filter (EKF) for space manipulators is proposed in this paper. Firstly, the identification model based on the Denavit–Hartenberg (D-H) modeling method is established. Then, the camera which is rigidly attached to the end-effector takes pictures of a calibration board that is settled around the manipulator. The actual pose of the end-effector is calculated based on the pictures of the calibration board. Subsequently, different data between the actual pose and theoretical pose as input, whilst error parameters are estimated by EKF and compensated in the kinematic algorithm. The simulation result shows that the pose accuracy has been improved by approximately 90 percent. Compared with the calibration method of the least squares estimate (LSE), EKF is beneficial to further optimize the calibrating time with a faster computation speed and ensure the stability of the calibration.

Kinematic parameters calibration of industrial robot based on RWS-PSO algorithm

The positioning accuracy of an industrial robot has a significant impact on its application in precision manufacturing, and it is necessary to calibrate robot kinematic parameters. Previous studies have established numerous nonlinear equations to solve the kinematic parameters, which are complicated and time consuming. A standard particle swarm optimization (PSO) algorithm is limited by long running time and low solution efficiency. Therefore, in this study, a dynamic particle swarm optimization algorithm based on roulette wheel selection (RWS-PSO) is proposed to realize the kinematic parameters calibration. First, a kinematics model is constructed using the standard Denavit-Hartenberg (D-H) method, and the theoretical and actual values of the spatial position of the robot endpoint are obtained via forward kinematics and a Laser Tracker, respectively. Next, the kinematic parameters calibration problem is transformed into a solution of a high-dimensional nonlinear equation using the proposed RWS-PSO algorithm. In the proposed RWS-PSO algorithm, the inertia factor is considered as linearly decreasing and the number of particles is selected by the roulette wheel selection (RWS) to improve its computational efficiency. The proposed RWS-PSO and standard PSO algorithms are compared based on various indices by simulation. The results reveal that the time cost of the RWS-PSO algorithm is much lower than that of the standard PSO algorithm on the basis of high precision and a reduced running time of approximately 53%. Finally, the kinematic parameter errors obtained by the two algorithms are compensated. According to the experimental results, the positioning accuracy of the robot in three ( x-, y-, and z-) directions is improved by 78%, 46%, and 67%, respectively, compared to that of before compensation, which proves that the RWS-PSO algorithm is effective and practical for kinematic parameters calibration.

A Novel Calibration Method for Robot Kinematic Parameters Based on Improved Manta Ray Foraging Optimization Algorithm

The positioning accuracy has a significant impact on the application of the robotic arm. In this study, a novel method based on improved manta ray foraging optimization (MRFO) algorithm is presented for kinematic parameters calibration, which can reduce the absolute positioning error of the manipulator. The error model of the robot is established according to the Denavit–Hartenberg (D–H) method. The redundancy of the model is analyzed, and the redundant parameters are eliminated. The kinematic parameter identification of the robot is transformed into a nonlinear optimization problem. The original MRFO algorithm is improved by using the natural selection strategy and adaptive parameter control strategy to improve searching performance and convergence speed. The calibration experiment of the self-designed 6-DOF robot arm is carried out. A measurement method for measuring the position of the robot end using a 3-D scanner is introduced. The improved MRFO (IMRFO) algorithm is used to identify the kinematic parameter errors of the robot. The experimental results show that by using the IMRFO algorithm to calibrate the parameter error, the position error of the robot end is reduced from 10.2357 to 0.5483 mm or by 94.6%. The results show that the IMRFO algorithm proposed in this article has high identification accuracy and fast convergence speed, which can effectively identify the kinematic parameter error of the robot.

Vision-based simultaneous measurement of manipulator configuration and target pose for an intelligent cable-driven robot

Cable-driven robots have great application potentials in confined spaces due to their light and slim bodies. However, their kinematic accuracy is generally much lower than that of the traditional articulated manipulators, because it is difficult to install sensors on its joints to directly measure the joint position. To solve the above problems, this paper proposed a vision-based method for cable-driven robots to simultaneously measure the manipulator configuration and the target pose. The configuration is reconstructed through the detection of Apriltag 2D code and the kinematics model of cable-driven robots. The target detection method in this paper consists of the template matching method and the natural features detection. Experiments are carried out to verify the method proposed in this article, including kinematic parameters calibration, arm-shape measurement, target pose measurement and docking test. The results show that the average position error of the robot end-effector decreases from 9.12 mm to 1.86 mm after calibration. The links’ position errors measured by the method in this paper are less than 10 mm. The position and the angle errors are respectively 2 mm and 1° at the target pose measurement, and the success rate of docking experiment is more than 98%. The experimental results show that the simultaneous measurement method of manipulator configuration and target pose proposed in this paper has a great effect on improving the motion accuracy and the working ability of the intelligent cable-driven robot.

A novel kinematic parameters calibration method for industrial robot based on Levenberg-Marquardt and Differential Evolution hybrid algorithm

• A new method is proposed to calibrate the kinematic parameters of robot, solving the high order nonlinear problem of Levenberg-Marquardt algorithm and the issue of slow convergence speed of Differential Evolution algorithm. • This method is validated on a FANUC M710ic/50 robot. • In consideration of the effect of measurement noise, such as measurement error of laser tracker, the coordinate system transformation error between the robot and laser tracker, the absolute positioning accuracy of robot has increased by almost 73.6% after calibration with hybrid algorithm. • A kind of joint space error compensation method is proposed to compensate for robot kinematic parameter deviations, overcoming the problem of authority limitation of modifying the D-H parameters of robot control system. The poor absolute positioning accuracy of industrial robots is the main obstacle for its further application in precision grinding of complex surfaces, such as blisk, blade, etc. Based on the established kinematic error model of a typical industrial robot FANUC M710ic/50, a novel kinematic parameters calibration method is proposed in this paper to improve the absolute positioning accuracy of robot. The pre-identification of the kinematic parameter deviations of robot was achieved by using the Levenberg-Marquardt algorithm. Subsequently, these identified suboptimal values of parameter deviations were defined as central values of the components of initial individuals to complete accurate identification by using Differential Evolution algorithm. The above two steps, which were regarded as the core of this Levenberg-Marquardt and Differential Evolution hybrid algorithm, were used to obtain the preferable values for kinematic parameters of the robot. On this basis, the experimental investigations of kinematic parameters calibration were conducted by using a laser tracker and numerical simulation method. The results revealed that the robot positioning error decreased from 0.994 mm, initial positioning error measured by laser tracker, to 0.262 mm after calibration with this proposed hybrid algorithm. The absolute positioning accuracy has increased by 40.86% than that of the Levenberg-Marquardt algorithm, increased by 40.31% than that of the Differential Evolution algorithm, and increased by 25.14% than that of the Simulated Annealing algorithm. This work shows that the proposed kinematic parameters calibration method has a significant improvement on the absolute positioning accuracy of industrial robot.

Improved Tracking and Docking of Industrial Mobile Robots Through UKF Vision-Based Kinematics Calibration

Performing an open-loop movement, or docking, for an industrial mobile robot (IMR), is a common necessary procedure when relying on environmental sensors is not possible. This procedure precision and outcome, solely depend on the IMR forward kinematic and odometry correctness, which is tied to the kinematics parameters, depending on the IMR kind. Calibrating the kinematic parameters of an IMR is a time consuming and mandatory procedure, since the mechanical tolerances and the assembly procedure may introduce a large variation from the nominal parameters. Furthermore, calibration inaccuracies might introduce severe inconsistencies in tasks such as localization, mapping, and navigation in general. In this work, we focus on the so-called kinematic parameter calibration. We propose the use of the unscented Kalman filter to perform a calibration procedure of the geometrical kinematic parameters of a mobile platform. The mobile platform is externally tracked during the calibration phase, using a fixed temporary external sensor that retrieves the position of a visual tag fixed to the platform. The unscented Kalman filter, using the calibration phase collected data, estimates the enlarged system state, which is comprised of the parameters that have to be estimated, the platform odometry and the visual tag position. The method can either be used online, to identify parameters and monitor their value while the system is operating, or offline, on logged data. We validate this method on two different devices, a 4 mecanum-wheel IMR, and a Turtlebot 3, using a camera to track the movement trough a reference chessboard, for then comparing the original path to its corrected version.

Measuring and Estimating Rotary Joint Axes of an Articulated Robot

The aim of this article is to chronicle a novel methodology for measuring and estimating rotary joint axes of an articulated robot. The key contribution of the proposed methodology is a practical way of estimating rotary joint axes of an articulated robot by taking static position measurements of every rotary joint, so that the estimated rotary joint axes reflect the real characteristics of the actual robot. Furthermore, the proposed methodology enables an advanced calibration of robot kinematic parameters for a better motion performance. The metrology requires a laser tracker for taking static position measurements. The proposed methodology is intended for articulated robots with serially linked rotary joints. To make the basic concepts easier to understand, the study uses a lower-pair kinematic chain with three serially linked rotary joints. The basic concepts can be extended and applied to higher degree-of-freedom articulated robots. To validate the research outcomes, this article proceeds with a validation experiment that demonstrates the use of the proposed methodology with empirical data produced by a metrology simulation experiment. The results of the validation experiment indicate that the proposed methodology is capable of measuring and estimating rotary joint axes of an articulated manipulator even in the presence of typical sensor noises.

A calibration method of robot kinematic parameters by drawstring displacement sensor

Calibration of robot kinematic parameters can effectively improve the absolute positioning accuracy of the end-effector for industrial robots. This article proposes a calibration method for robot kinematic parameters based on the drawstring displacement sensor. Firstly, the kinematic error model for articulated robot is established. Based on such a model, the position measurement system consisting of four drawstring displacement sensors is used to measure the actual position of the robot end-effector. Then, the deviation of the kinematic parameters of the robot is identified by the least-squares method according to robot end-effector deviations. The Cartesian space compensation method is adopted to improve the absolute positioning accuracy of the robot end-effecter. By experiments on the EFORT ER3A robot, the absolute positioning accuracy of the robot is significantly improved after calibration, which shows the effectiveness of the proposed method.

Analysis of unified error model and simulated parameters calibration for robotic machining based on Lie theory

Abstract In robotic machining process, the kinematic errors of serial structure and compliance errors caused by external cutter-workpiece interactions can result in considerable deviation of the desired trajectory. Therefore, this paper proposes an efficient calibration methodology by establishing a unified error model about kinematic errors and compliance errors based on Lie theory, which simultaneously calibrates the kinematic parameters and joint compliances of a serial machining robot. In this methodology, the propagation law of kinematic errors is investigated by analysis of the kinematic error model, and the corresponding equivalent kinematic error model is thus obtained, in which the joint offset errors are regarded as one source of twist (joint twist and reference configuration twist) errors. On this basis, with the segmentation and modelling of the joint compliance errors caused by the link self-weight and cutting payloads, the unified error model is developed by linear superposition of configuration errors of the robotic end-cutter, calculated from the kinematic errors and compliance errors respectively. Meanwhile, to improve the accuracy of parameters calibration, the observability index is adopted to optimize the calibration configurations so as to eliminate the twist error constraints. The calibrated kinematic parameters and joint compliances are obtained eventually, and used to compensate the kinematic and compliance errors of the serial machining robot. Finally, to validate the effectiveness of the proposed unified error model, simulation analysis is performed using a 6-DOF serial machining robot, namely KUKA KR500. The comparisons among calibrated parameters show that the unified error model is more computationally efficient with optimal calibration configurations, rendering it suitable for the calibration of kinematic parameters and joint compliances in actual machining applications.

Multiple location constraints based industrial robot kinematic parameter calibration and accuracy assessment

Kinematic calibration of industrial robots is an effective method of improving robot accuracy, and thus robot performance, especially when working with offline programming. The state-of-art industrial applications are lacking an easy-to-operate, reliable, and economic solution, hence forbidding frequent on-site calibration. This study presents a robot kinematic calibration method to improve the accuracy of six-axis industrial robots, using multiple location constraints. The robot parameters are calibrated by controlling a robot to reach the same location in different poses. Two standard devices and a non-contact equipment, non-bar device, are used for measurement. The proposed calibration process applies three identification methods and two compensation methods that can be mixed and matched, allowing flexible combinations of solutions to fit various industrial conditions. The proposed measurement/calibration system is economic, easy-to-operate, reliable, and suitable for express user-site industrial robot kinematic calibration, avoiding extensive production interruption. Actual experiments are carried out to verify the effects of joint zero calibration. These experiments demonstrate the proposed method’s ability to improve the accuracy of robots, and thus improve the robot work quality using offline programming.

A simple and rapid calibration methodology for industrial robot based on geometric constraint and two-step error

PurposeThe purpose of this study is the presentation and research of a simple and rapid calibration methodology for industrial robot. Extensive research efforts were devoted to meet the requirements of online compensation, closed-loop feedback control and high-precision machining during the flexible machining process of robot for large-scale cabin.Design/methodology/approachA simple and rapid method to design and construct the transformation relation between the base coordinate system of robot and the measurement coordinate system was proposed based on geometric constraint. By establishing the Denavit–Hartenberg model for robot calibration, a method of two-step error for kinematic parameters calibration was put forward, which aided in achievement of step-by-step calibration of angle and distance errors. Furthermore, KUKA robot was considered as the research object, and related experiments were performed based on laser tracker.FindingsThe experimental results demonstrated that the accuracy of the coordinate transformation could reach 0.128 mm, which meets the transformation requirements. Compared to other methods used in this study, the calibration method of two-step error could significantly improve the positioning accuracy of robot up to 0.271 mm.Originality/valueThe methodology based on geometric constraint and two-step error is simple and can rapidly calibrate the kinematic parameters of robot. It also leads to the improvement in the positioning accuracy of robot.

이륜차동구동형로봇의 복합오차를 고려한 기구학적 파라미터 정밀보정기법

이륜차동구동형로봇의 복합오차를 고려한 기구학적 파라미터 정밀보정기법

1A1-M03 全方向移動台車に軽量アームを搭載した小型室内移動ロボットの開発 : アームの機構補正,机への自律移動,机上物体の認識と把持(ホーム&オフィスロボット)

1A1-M03 全方向移動台車に軽量アームを搭載した小型室内移動ロボットの開発 : アームの機構補正,机への自律移動,机上物体の認識と把持(ホーム&オフィスロボット)

Determination of optimal measurement configurations for polishing robot calibration

Selection of optimal measurement configurations for the kinematic parameter calibration is investigated in this paper. To minimise the effects of measurement and modelling errors on parameter estimation, a series of optimal calibration configurations is obtained with our modified simulated annealing algorithm. To accelerate the convergence rate, a suitable cooling schedule is designed. Independent and interactive effects of the parameters in the cooling schedule on algorithm performance are discussed. Two observability indices are optimised with the modified simulated annealing algorithm to obtain the optimal measurement configurations of the 5-DOF polishing robot. Simulated calibration shows that these optimal configurations improve the calibration accuracy significantly. Experimental results, compared with those of random pose selection and a local search algorithm, are presented to demonstrate the feasibility of the proposed approach.

Laser tracker-based kinematic parameter calibration of industrial robots by improved CPA method and active retroreflector

Typical approaches to calibrate industrial robots are based on open- and closed-loop methods; the screw–axis measurement methods traditionally receive much less attention. Although the identification process does not guarantee the physical–mathematical link between the robot parameters in the first two groups of techniques, these techniques are generally more effective in reducing the global positioning error compared to the screw–axis methods. The third group of techniques acquires parameters based on the physical reality of the robot, effectively keeping the physical–mathematical link. This group is considered more appropriate than the previous two groups; however, it cannot reduce the overall error when considering the entire workspace of the robot compared to the previous groups. This paper presents a new technique to identify the kinematic parameters of an industrial robot based on a combination of techniques from the aforementioned categories. This new data acquisition technique uses a laser tracker with an active target, which maximises the angle covered by each joint and greatly simplifies the screw–axis measurement process. An identification procedure based on circle point analysis is also proposed, and the procedure evaluates the technique by obtaining initial values with a new formulation of the objective function of error based on mutual distances between the points captured in screw–axis measurements. This type of measurement also allows the eccentricity and backlash of each joint to be characterised independently such that local joint corrections could be made in combination with the identified parameters.

Calibration of Kinematic Parameters of Robot Arm Using Laser Tracking System: Compensation for Non-Geometric Errors by Neural Networks and Selection of Optimal Measuring Points by Genetic Algorithm

Offline teaching based on high positioning accuracy of a robot arm is desired to take the place of manual teaching. In offline teaching, joint angles are calculated using a kinematic model of the robot arm. However, a nominal kinematic model does not consider the errors arising in manufacturing or assembly, not to mention the non-geometric errors arising in gear transmission, arm compliance, etc. Therefore, a method of precisely calibrating the parameters in a kinematic model is required. For this purpose, it is necessary to measure the three-dimensional (3-D) absolute position of the tip of a robot arm. In this paper, a laser tracking system is employed as the measurement apparatus. The geometric parameters in the robot kinematic model are calibrated by minimizing errors between the measured positions and the predicted ones based on the model. The residual errors caused by non-geometric parameters are further reduced by using neural networks, realizing high positioning accuracy of sub-millimeter order. To speed up the calibration process, a smaller number of measuring points is preferable. Optimal measuring points, which realize high positioning accuracy while remaining small in number, are selected using Genetic Algorithm (GA).