Kinematic Modeling of Six-Axis Industrial Robot and its Parameter Identification: A Tutorial

In advanced industrial applications, like machining, the absolute positioning accuracy of a six-axis robot is indispensable. To improve the absolute positioning accuracy of an industrial robot, numerical compensation based on positioning error prediction by the Denavit and Hartenberg (D-H) model has been investigated extensively. The main objective of this study is to review the kinematic modeling theory for a six-axis industrial robot. In the form of a tutorial, this paper defines a local coordinate system based on the position and orientation of the rotary axis average lines, as well as the derivation of the kinematic model based on the coordinate transformation theory. Although the present model is equivalent to the classical D-H model, this study shows that a different kinematic model can be derived using a different definition of the local coordinate systems. Subsequently, an algorithm is presented to identify the error sources included in the kinematic model based on a set of measured end-effector positions. The identification of the classical D-H parameters indicates a practical engineering application of the kinematic model for improving a robot’s positioning accuracy. Furthermore, this paper presents an extension of the present model, including the angular positioning deviation of each rotary axis. The angular positioning deviation of each rotary axis is formed as a function of the axis’ command angles and the direction of its rotation to model the effect of the rotary axis backlash. The identification of the angular positioning deviation of each rotary axis and its numerical compensation are presented, along with their experimental demonstration. This paper provides an essential theoretical basis for the error source diagnosis and error compensation of a six-axis robot.

Performance Evaluation of Low-Cost Multi-Frequency GNSS Receivers and Antennas for Displacement Detection

Low-cost Global Navigation Satellite System (GNSS) receivers are currently used in various engineering applications. These low-cost devices are regarded as suitable sensors for applications in areas with a high risk of instrument damage. The main objectives of this research were to identify the size of displacements that can be detected in relative and absolute positioning modes by low-cost GNSS instruments and to compare the results of selected antennas. Additionally, geodetic and low-cost GNSS instruments were compared in the level of observations. For this study, low-cost SimpleRTK2B V1 boards, which house ZED-F9P GNSS chips, and three low-cost antennas, namely, Survey, Tallysman TW3882, and Survey Calibrated, were selected. While antenna calibration parameters are known for the last antenna, this is not the case for the first two. For testing purposes, a geodetic network consisting of four points was established; horizontal and vertical movements were imposed by a special mechanism with high accuracy. In relative positioning mode, the results indicate that the Survey Calibrated antenna can detect horizontal and vertical displacements with sizes of 4 mm, and 6 mm, respectively. In the detection of horizontal displacements, the performance of the Survey antenna was not as good as that of Tallysman, and the sizes of detected displacements were 6 mm and 4 mm for the first, and second antennas, respectively. Vertical displacements of 9 mm were detected using both Survey and Tallysman antennas. In absolute positioning mode, Survey Calibrated also had better performance than the Tallysman antenna, and spatial displacements of 20 mm or greater were detected by low-cost GNSS instruments. The observations made with low-cost and geodetic GNSS instruments were compared, and the latter showed better performance. However, the differences in cycle slips and the noise of phase observations were inferior. Considering their cost and proven performance, it can be concluded that such sensors can be considered for setting up a highly accurate but low-cost geodetic monitoring system.

Pose estimation for robotic percussive riveting.

Recently, a robotic percussive riveting system has been developed at Ryerson University for an automation of percussive riveting process of aero-structural fastening assembly. The system consists of a robot holding a percussive riveting gun equipped with a rivet feeder, a gantry holding a working panel of aero-structure, and a position visual sensor. Prior to riveting, the robot is required to first position and then insert a rivet precisely into a hole on the panel without engaging the panel to prevent potential damage. The underlying challenges to precise insertion are various sources of system uncertainties, mainly including alignment errors among coordinate systems of the robot, panel and sensor, and relatively poor absolute positioning accuracy of the robot due to mechanical deflection, assembly clearance, and machining tolerance. For this reason, the research of relative pose estimation between the robot and panel has been carried out pertaining to these challenges. Essentially, pose estimation is proposed for robotic percussive riveting, which estimates the relative pose between two rigid bodies based on noisy visual measurements of point features on rigid bodies. Three categories can be classified, namely, static, dynamic, and robust pose estimation. Firstly, static pose estimation is the parameter estimation of static relative pose transformations among a number of frames, which solves the issue of alignment errors. Direct solutions of static relative pose estimation are derived based on least-square methods. Secondly, to tackle the issue of poor absolute positioning accuracy of the robot, dynamic relative pose estimation is proposed addressing a state estimation of relative poses during motion. Iterative extended Kalman filter method is adapted for the state estimation. Thirdly, for robustness against outliers of point measurements, robust pose estimation is proposed based on an outlier diagnosis using the technique of relaxation of rigid body constraints. Indeed, outlier diagnosis is a pre-processing of point measurements, in which outliers are detected and removed prior to the relative pose estimation. Further, a decorrelation method is proposed for measurement calibration using multivariate statistical analysis to find an optimal sensor-to-target configuration. As a result, each coordinate measurement is close to uncorrelated and it allows for a simple calibration.

Pose estimation for robotic percussive riveting.

Recently, a robotic percussive riveting system has been developed at Ryerson University for an automation of percussive riveting process of aero-structural fastening assembly. The system consists of a robot holding a percussive riveting gun equipped with a rivet feeder, a gantry holding a working panel of aero-structure, and a position visual sensor. Prior to riveting, the robot is required to first position and then insert a rivet precisely into a hole on the panel without engaging the panel to prevent potential damage. The underlying challenges to precise insertion are various sources of system uncertainties, mainly including alignment errors among coordinate systems of the robot, panel and sensor, and relatively poor absolute positioning accuracy of the robot due to mechanical deflection, assembly clearance, and machining tolerance. For this reason, the research of relative pose estimation between the robot and panel has been carried out pertaining to these challenges. Essentially, pose estimation is proposed for robotic percussive riveting, which estimates the relative pose between two rigid bodies based on noisy visual measurements of point features on rigid bodies. Three categories can be classified, namely, static, dynamic, and robust pose estimation. Firstly, static pose estimation is the parameter estimation of static relative pose transformations among a number of frames, which solves the issue of alignment errors. Direct solutions of static relative pose estimation are derived based on least-square methods. Secondly, to tackle the issue of poor absolute positioning accuracy of the robot, dynamic relative pose estimation is proposed addressing a state estimation of relative poses during motion. Iterative extended Kalman filter method is adapted for the state estimation. Thirdly, for robustness against outliers of point measurements, robust pose estimation is proposed based on an outlier diagnosis using the technique of relaxation of rigid body constraints. Indeed, outlier diagnosis is a pre-processing of point measurements, in which outliers are detected and removed prior to the relative pose estimation. Further, a decorrelation method is proposed for measurement calibration using multivariate statistical analysis to find an optimal sensor-to-target configuration. As a result, each coordinate measurement is close to uncorrelated and it allows for a simple calibration.

A Competitive Analysis Of Top Ten Pharmaceutical Companies In India

Contemplating the activities and conduct of close contenders is fundamental. Competitive analysis is a significant piece of the vital arranging process, and the wellsprings of such Web data are enhanced, and the data are refreshed much of the time. This expansive intrigue has brought about different definitions and conceptualizations of recognizable contender proof just as different ways to deal with considering it, which disables the reconciliation of existing information planned for responding to fundamental inquiries regarding its revenue, procedures, and suggestions. To compete with the opposition organizations are against, it is essential to survey things that are genuinely working in the business. Inquire about rely upon measurable studying, which is limited most definitely and limit. There can be more recommendations, which can show profitability to the association. In this task, the top ten pharmaceutical organizations of India are firmly watched. The absolute positioning gives according to general income, i.e., overall revenue.

A calibration method for enhancing robot accuracy through integration of kinematic model and spatial interpolation algorithm

Industrial robots are finding their niche in the field of machining due to their advantages of high flexibility, good versatility and low cost. However, limited by the low absolute positioning accuracy, there are still huge obstacles in high precision machining processes such as grinding. Aiming at this problem, a compensation method combining analytical modeling for quantitative errors with spatial interpolation algorithm for random errors is proposed based on the full consideration of the source and characteristics of positioning errors. Firstly, as for the quantitative errors, namely geometric parameter and compliance error in this paper, a kinematics-based error model is constructed taking the coupling effect of errors into consideration. Then avoiding the impact of random errors, the extended Kalman filtering algorithm (EKF) is adopted to identify the error parameters. Secondly, based on the similarity principle of spatial error, spatial interpolation algorithm is used to model the residual error caused by temperature, gear clearance etc. Based on the spatial anisotropy characteristics of robot motion performance, an adaptive mesh division algorithm was proposed to balance the accuracy and efficiency of mesh division. Then, an inverse distance weighted interpolation algorithm considering the influence degree of different joints on the end position was established to improve the approximation accuracy of residual error. Finally, the rough-fine two-stage serial error compensation method was carried out. Experimental results show the mean absolute positioning accuracy is improved from 1.165 mm to 0.106 mm, which demonstrates the effectiveness of the method in this paper.

Improving the accuracy of absolute positioning of Global Navigation Satellite Systems for geodesy purposes

Статья посвящена повышению точности глобальных навигационных спутниковых систем (ГНСС) измерений в абсолютном режиме. Для повышения точности применяются линейные LR-модели. Рассматривается методика построения такой модели, исследуется эффект её применения, даются рекомендации. Исследования, проведённые в Арабской республике Египет, показали значительный положительный эффект применения данной модели. LR-модель позволяет с минимальными затратами существенно повысить точность абсолютных спутниковых определений для инженерно-геодезических изысканий.

Research on Absolute Positioning Sensor Based on Eddy Current Reflection for High-Speed Maglev Train

A novel absolute positioning sensor for high-speed maglev train based on eddy current effect is studied in this paper. The sensor is designed with photoelectric switch and four groups of unilateral coplanar code-reading detection coil combination. The photoelectric switch realizes the positioning of the marker plate, and the four groups of detection coils read the mileage code of the mileage sign plate to obtain the absolute mileage information of the vehicle, which effectively reduces the quality and volume of the sensor, and reduces the impact of ice and snow. At the same time, the code-reading reliability and speed adaptability index are proposed. The code-reading reliability of the sensor is analyzed and tested under the fluctuation of levitation guidance, and the positioning error under the speed range of 0–600 km/h is calculated and analyzed. The results show that the novel sensor has the advantages of simple and compact structure. It still satisfies the system’s requirements for absolute vehicle mileage information under the conditions of vehicle operating attitude fluctuations and changes in the full operating speed range.