scholarly journals A Novel Optimal Design of Measurement Configurations in Robot Calibration

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Qingxuan Jia ◽  
Shiwei Wang ◽  
Gang Chen ◽  
Lei Wang ◽  
Hanxu Sun
Keyword(s):  
Optimal Design ◽  
Pose Estimation ◽  
Kinematic Parameter ◽  
Critical Importance ◽  
Kinematic Parameters ◽  
Robot Calibration ◽  
End Effector ◽  
Proposed Model ◽  
Observability Index ◽  
Sample Data

Robot calibration highly depends on redundant measurement configurations to collect enough sample data for higher accuracy, but excessive measurements seem to be uneconomical and time-consuming. Thus lots of observability indexes to evaluate the goodness of the measurement configurations have been proposed. However, in some circumstances, it is of critical importance to obtain accurate kinematic parameters and estimate the end-effector pose precisely at the same time. Obviously one single observability index can hardly meet this need yet. Accordingly, after analyzing the essential constrains of robot calibration and the influence of measurement configurations on the observability indexes, an optimization model with two observability indexes to be taken into consideration is proposed in this paper, and then the existing DETMAX algorithm is modified to seek optimal design of measurement configurations, by adopting a set-constructing method and a set-shrinking method. Much better results have been obtained by simulation study, which implies that the proposed model and the modified DETMAX algorithm perform well in both kinematic parameter identification and pose estimation of the end-effector of the robot.

scholarly journals Test of Robot Distance Error and Compensation of Kinematic Full Parameters

2014 ◽  
Vol 6 ◽  
pp. 810684 ◽  
Author(s):  
Tie Zhang ◽  
Liang Du ◽  
Xiaoliang Dai
Keyword(s):  
Kinematic Parameter ◽  
Error Model ◽  
Kinematic Parameters ◽  
Positioning Accuracy ◽  
Error Matrix ◽  
Modified Model ◽  
Distance Error ◽  
Proposed Model ◽  
Kinematic Error Model ◽  
The Relationship

In order to improve the positioning accuracy of robots in the workspace, the maximum cube of robots is solved according to ISO 9283:1998 standard. In addition, in order to efficiently test and identify the kinematic parameters of robots, a mapping from robot's distance error onto kinematic parameter errors is presented based on Hayati's modified D-H model. Then, by analyzing the condition number of distance error matrix, it is concluded that parameter d i can be deleted when parameter β i is added in the joint of the modified model. Furthermore, by analyzing the relationship among the parameters of the distance error model, it is found that the deletion of some unidentified kinematic parameters may not result in the accuracy decrease of kinematic error model. Finally, some compensation experiments of the proposed model without unidentified kinematic parameters are carried out by using a laser tracker system. The results show that the proposed method effectively reduces the distance error and greatly improves the positioning accuracy of robots.

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

2018 ◽  
Vol 45 (6) ◽  
pp. 715-721 ◽  
Author(s):  
Jiabo Zhang ◽  
Xibin Wang ◽  
Ke Wen ◽  
Yinghao Zhou ◽  
Yi Yue ◽  
...  
Keyword(s):  
Coordinate System ◽  
Large Scale ◽  
Industrial Robot ◽  
Geometric Constraint ◽  
Machining Process ◽  
Kinematic Parameters ◽  
Robot Calibration ◽  
Positioning Accuracy ◽  
Content Type ◽  
Rapid Calibration

Purpose The 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/approach A 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. Findings The 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/value The 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.

scholarly journals End-Effector Pose Estimation in Complex Environments Using Complementary Enhancement and Adaptive Fusion of Multisensor

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mingrui Luo ◽  
En Li ◽  
Rui Guo ◽  
Jiaxin Liu ◽  
Zize Liang
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Unscented Kalman Filter ◽  
Rbf Neural Network ◽  
Estimation Error ◽  
Estimation Method ◽  
Original Data ◽  
Measurement Unit ◽  
Complex Environments ◽  
End Effector

Redundant manipulators are suitable for working in narrow and complex environments due to their flexibility. However, a large number of joints and long slender links make it hard to obtain the accurate end-effector pose of the redundant manipulator directly through the encoders. In this paper, a pose estimation method is proposed with the fusion of vision sensors, inertial sensors, and encoders. Firstly, according to the complementary characteristics of each measurement unit in the sensors, the original data is corrected and enhanced. Furthermore, an improved Kalman filter (KF) algorithm is adopted for data fusion by establishing the nonlinear motion prediction of the end-effector and the synchronization update model of the multirate sensors. Finally, the radial basis function (RBF) neural network is used to adaptively adjust the fusion parameters. It is verified in experiments that the proposed method achieves better performances on estimation error and update frequency than the original extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithm, especially in complex environments.

scholarly journals A peduncle detection method of tomato for autonomous harvesting

2021 ◽  
Author(s):  
Jiacheng Rong ◽  
Guanglin Dai ◽  
Pengbo Wang
Keyword(s):  
Pose Estimation ◽  
Pitch Angle ◽  
Detection Method ◽  
Geometric Model ◽  
Average Error ◽  
Long Distance ◽  
End Effector ◽  
Key Points ◽  
Yaw Angle ◽  
Cutting Point

AbstractFor automating the harvesting of bunches of tomatoes in a greenhouse, the end-effector needs to reach the exact cutting point and adaptively adjust the pose of peduncles. In this paper, a method is proposed for peduncle cutting point localization and pose estimation. Images captured in real time at a fixed long-distance are detected using the YOLOv4-Tiny detector with a precision of 92.7% and a detection speed of 0.0091 s per frame, then the YOLACT +  + Network with mAP of 73.1 and a time speed of 0.109 s per frame is used to segment the close-up distance. The segmented peduncle mask is fitted to the curve using least squares and three key points on the curve are found. Finally, a geometric model is established to estimate the pose of the peduncle with an average error of 4.98° in yaw angle and 4.75° in pitch angle over the 30 sets of tests.

A New Measurement Device for Kinematic Calibration of Parallel Manipulators

2004 ◽  
Author(s):  
Abdul Rauf ◽  
Sung-Gaun Kim ◽  
Jeha Ryu
Keyword(s):  
Parallel Manipulators ◽  
Measurement Procedure ◽  
Initial Guess ◽  
Measurement Noise ◽  
Kinematic Calibration ◽  
Kinematic Parameters ◽  
End Effector ◽  
Experimental Procedure ◽  
New Device ◽  
Rotation Component

Kinematic calibration is a process that estimates the actual values of geometric parameters to minimize the error in absolute positioning. Measuring all the components of Cartesian posture assure identification of all parameters. However, measuring all components, particularly the orientation, can be difficult and expensive. On the other hand, with partial pose measurements, experimental procedure is simpler. However, all parameters may not be identifiable. This paper proposes a new device that can be used to identify all kinematic parameters with partial pose measurements. Study is performed for a 6 DOF (degree-of-freedom) fully parallel Hexa Slide manipulator. The device, however, is general and can be used for other parallel manipulators. The proposed device consists of a link with U joints on both sides and is equipped with a rotary sensor and a biaxial inclinometer. When attached between the base and the mobile platform, the device restricts the end-effector’s motion to 5 DOF and measures two position components and one rotation component of the end-effector. Numerical analyses of the identification Jacobian reveal that all parameters are identifiable. Computer simulations show that the identification is robust for the errors in the initial guess and the measurement noise. Intrinsic inaccuracies of the device can significantly deteriorate the calibration results. A measurement procedure is proposed and cost functions are discussed to prevent propagation of the inaccuracies to the calibration results.

Object Oriented Dexterity Analysis and Design Application for Spatial Robot Hands

1989 ◽  
Author(s):  
L. Lu ◽  
C. Cai ◽  
A. H. Soni
Keyword(s):  
Performance Evaluation ◽  
Object Oriented ◽  
Kinematic Parameter ◽  
Least Square ◽  
Parameter Determination ◽  
Robot Hand ◽  
Kinematic Parameters ◽  
Analysis And Design ◽  
Robot Hands

Abstract For an arbitrarily shaped object manipulated by a robot hand, this paper presents a procedure for analyzing the position and rotation ranges of the object, and a procedure for designing the kinematic parameters of a hand to meet given requirements on the motion ranges. Rotation dexterity index, dexterity charts, and a dexterity scalar characterizing both position range and rotation range are introduced for the performance evaluation of a robot hand. Least-square-error iteration and steps are detailed for the kinematic parameter determination of a robot hand.

scholarly journals Multi-Person Pose Estimation using an Orientation and Occlusion Aware Deep Learning Network

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1593 ◽  
Author(s):  
Yanlei Gu ◽  
Huiyang Zhang ◽  
Shunsuke Kamijo
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Body Orientation ◽  
Learning Approaches ◽  
The Public ◽  
Learning Network ◽  
Proposed Model ◽  
Body Boundary ◽  
Public Dataset ◽  
Deep Learning Network

Image based human behavior and activity understanding has been a hot topic in the field of computer vision and multimedia. As an important part, skeleton estimation, which is also called pose estimation, has attracted lots of interests. For pose estimation, most of the deep learning approaches mainly focus on the joint feature. However, the joint feature is not sufficient, especially when the image includes multi-person and the pose is occluded or not fully visible. This paper proposes a novel multi-task framework for the multi-person pose estimation. The proposed framework is developed based on Mask Region-based Convolutional Neural Networks (R-CNN) and extended to integrate the joint feature, body boundary, body orientation and occlusion condition together. In order to further improve the performance of the multi-person pose estimation, this paper proposes to organize the different information in serial multi-task models instead of the widely used parallel multi-task network. The proposed models are trained on the public dataset Common Objects in Context (COCO), which is further augmented by ground truths of body orientation and mutual-occlusion mask. Experiments demonstrate the performance of the proposed method for multi-person pose estimation and body orientation estimation. The proposed method can detect 84.6% of the Percentage of Correct Keypoints (PCK) and has an 83.7% Correct Detection Rate (CDR). Comparisons further illustrate the proposed model can reduce the over-detection compared with other methods.

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 Nonlinear Synchronous Control for H-Type Gantry Stage Used in Electric VehiclesManufacturing

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2305
Author(s):  
Ran Chen ◽  
Zongxia Jiao ◽  
Liang Yan ◽  
Yaoxing Shang ◽  
Shuai Wu
Keyword(s):  
Tracking Error ◽  
Tracking Performance ◽  
System Model ◽  
Lyapunov Analysis ◽  
End Effector ◽  
Mechanical Coupling ◽  
Synchronous Control ◽  
Linear Motors ◽  
Proposed Model ◽  
Synchronous Error

The H-type gantry stage (HGS) is widely used in electric vehicle manufacturing and other fields. However, resulting from the existence of mechanical coupling, the synchronous control problem of HGS always troubles many engineers. Most synchronization schemes were either engaged in improving each motor’s tracking performance or committed to pure motion synchronization only. However, tracking and synchronous performance are interconnected, because of the mechanical coupling. In this paper, a rigid assumed system model of HGS, concerning the effects of mid-beam rotary inertia, mid-beam stiffness, and end-effector movement, is presented. Based on the proposed model, an adaptive robust synchronous control based on a rigid assumed model (ARSCR) is proposed to improve both synchronous and tracking performance of the HGS. From the Lyapunov analysis, the proposed ARSCR can achieve the convergence of synchronous error and tracking error, simultaneously. An HGS driven by dual linear motors is built and used to perform the experimental verification. The experimental results indicate the effectiveness of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document