Gaussian Process Based Multi-Rate Observer for the Dynamic Positioning Error of a Measuring Machine

2019 ◽  
Author(s):  
Michael Ringkowski ◽  
Oliver Sawodny
Keyword(s):  
Gaussian Process ◽  
Dynamic Positioning ◽  
Positioning Error ◽  
Measuring Machine

Structure design and performance analysis of self-driving articulated arm coordinate measuring machine

2021 ◽  
Author(s):  
Hongtao Yang ◽  
Mei Shen ◽  
Mengyao Zhang ◽  
Jingjing Cheng ◽  
Tingting Hu ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Angular Acceleration ◽  
Coordinate Measuring Machine ◽  
Position Error ◽  
Static Deformation ◽  
Theoretical Research ◽  
Positioning Error ◽  
Instantaneous Position ◽  
Measuring Machine ◽  
Space Range

Abstract To solve the problem that the traditional articulated arm coordinate measuring machine cannot measure automatically, a self-driven articulated arm coordinate measuring machine (AACMM) is proposed. The length of the connecting rods of the AACMM was allocated according to the design indicators. The AACMM virtual prototype was assembled based on the joint module selection and joint component design, and its measurement space range was also verified. The AACMM ideal measurement model was established based on MDH methodology. The static deformation of the structure and the influence of the dynamic flexible deformation on the positioning error of the probe of the measuring machine was analyzed, respectively. The results show that the measurement space range of the AACMM designed in this paper can meet the design index of the measuring radius. The probe position error caused by static deformation of the measuring machine after structural optimization was reduced by an order of magnitude. The positioning error of the probe caused by the dynamic deformation of the AACMM structure meets the positioning accuracy index. In the constant-speed touch measurement stage, the instantaneous position error of the probe changes linearly with time, and the optimal touch speed (6.6 mm/s, 6.4 mm/s) exists to minimize the probe positioning error. During the variable-speed approach stage, the influence of angular acceleration and velocity of each joint on the positioning error of the probe can be negligible when AACMM in the typical posture. In the extreme posture, , the inertial force of the measuring machine structure and the instantaneous position error of the probe are the smallest with the optimal joint angular acceleration ( ) and angular velocity ( ). The structural design and positioning error performance analysis of self-driving AACMM can provide a theoretical research foundation for subsequent trajectory planning and error compensation modeling.

scholarly journals An Adaptive Bluetooth/Wi-Fi Fingerprint Positioning Method based on Gaussian Process Regression and Relative Distance

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2784 ◽  
Author(s):  
Hongji Cao ◽  
Yunjia Wang ◽  
Jingxue Bi ◽  
Hongxia Qi
Keyword(s):  
Gaussian Process ◽  
Prediction Models ◽  
Gaussian Process Regression ◽  
Position Estimation ◽  
Relative Distance ◽  
Error Prediction ◽  
Estimation Result ◽  
Positioning Error ◽  
Positioning Method ◽  
Better Than

Trusted positioning data are very important for the fusion of Bluetooth fingerprint positioning (BFP) and Wi-Fi fingerprint positioning (WFP). This paper proposes an adaptive Bluetooth/Wi-Fi fingerprint positioning method based on Gaussian process regression (GPR) and relative distance (RD), which can choose trusted positioning results for fusion. In the offline stage, measurements of the Bluetooth and Wi-Fi received signal strength (RSS) were collected to construct Bluetooth and Wi-Fi fingerprint databases, respectively. Then, fingerprint positioning error prediction models were built with GPR and data from the fingerprint databases. In the online stage, online Bluetooth and Wi-Fi RSS readings were matched with the fingerprint databases to get a Bluetooth fingerprint positioning result (BFPR) and a Wi-Fi fingerprint positioning result (WFPR). Then, with the help of RD and fingerprint positioning error prediction models, whether the positioning results are trusted was determined. The trusted result is selected as the position estimation result when there is only one trusted positioning result among the BFPR and WFPR. The mean is chosen as the position estimation result when both the BFPR and WFPR results are trusted or untrusted. Experimental results showed that the proposed method was better than BFP and WFP, with a mean positioning error of 2.06 m and a root-mean-square error of 1.449 m.

Measurement of Movement Error and its Compensation for 6-DOF Parallel Mechanism Worktable

2012 ◽  
Vol 523-524 ◽  
pp. 463-468 ◽  
Author(s):  
Yuan Rui Zhang ◽  
Jiang Zhu ◽  
Tomohisa Tanaka ◽  
Yoshio Saito
Keyword(s):  
Machine Tool ◽  
Machine Tools ◽  
Parallel Mechanism ◽  
Coordinate Measuring Machine ◽  
Geometric Error ◽  
Degree Of Freedom ◽  
Positioning Error ◽  
Uncertainty Factor ◽  
Movement Error ◽  
Measuring Machine

In this study, a small, 6-DOF (degree of freedom) parallel mechanism worktable for machine tool was developed. There are a lot of factors that affect the positioning error and the accuracy of the machine tools. The uncertainty in position is mainly due to the rigidity of the structure, the geometric error of parts and assembly errors. It is very difficult to estimate the assembly errors and the link parameter of each part. In this paper, the uncertainty factor in positioning of the worktable was investigated and compensated based on measurement of movement error by using coordinate measuring machine (CMM).

Precision Compensation Method for Positioning Error of Working Table of Vision Measuring Machine Based on Machine Vision

2018 ◽  
Vol 55 (9) ◽  
pp. 091203
Author(s):  
邹华东 Zou Huadong ◽  
贾瑞清 Jia Ruiqing ◽  
张畅 Zhang Chang
Keyword(s):  
Machine Vision ◽  
Compensation Method ◽  
Positioning Error ◽  
Measuring Machine

Integrated Fractional white Noise as an Alternative to Multifractional Brownian Motion

2007 ◽  
Vol 44 (02) ◽  
pp. 393-408 ◽  
Author(s):  
Allan Sly
Keyword(s):  
Stochastic Process ◽  
Brownian Motion ◽  
White Noise ◽  
Gaussian Process ◽  
Discrete Data ◽  
Hölder Exponent ◽  
Scaling Properties ◽  
Multifractional Brownian Motion ◽  
Local Properties

Multifractional Brownian motion is a Gaussian process which has changing scaling properties generated by varying the local Hölder exponent. We show that multifractional Brownian motion is very sensitive to changes in the selected Hölder exponent and has extreme changes in magnitude. We suggest an alternative stochastic process, called integrated fractional white noise, which retains the important local properties but avoids the undesirable oscillations in magnitude. We also show how the Hölder exponent can be estimated locally from discrete data in this model.

Measurements of the Cape Astrometric Survey of the Southern Hemisphere

1978 ◽  
Vol 48 ◽  
pp. 515-521
Author(s):  
W. Nicholson
Keyword(s):  
Southern Hemisphere ◽  
Automatic Measuring ◽  
Measuring Machine ◽  
Final Selection ◽  
Selection Of

SummaryA routine has been developed for the processing of the 5820 plates of the survey. The plates are measured on the automatic measuring machine, GALAXY, and the measures are subsequently processed by computer, to edit and then refer them to the SAO catalogue. A start has been made on measuring the plates, but the final selection of stars to be made is still a matter for discussion.

A Stochastic Model for the Inheritance of the Cancer Proneness Phenotype

1987 ◽  
Vol 26 (03) ◽  
pp. 117-123
Author(s):  
P. Tautu ◽  
G. Wagner
Keyword(s):  
Stochastic Model ◽  
Gaussian Process ◽  
Covariance Function ◽  
Neoplastic Disease ◽  
Disease Phenotype ◽  
Probabilistic Representation ◽  
Temporal Behaviour ◽  
Continuous Trait ◽  
Continuous Parameter ◽  
Level Zero

SummaryA continuous parameter, stationary Gaussian process is introduced as a first approach to the probabilistic representation of the phenotype inheritance process. With some specific assumptions about the components of the covariance function, it may describe the temporal behaviour of the “cancer-proneness phenotype” (CPF) as a quantitative continuous trait. Upcrossing a fixed level (“threshold”) u and reaching level zero are the extremes of the Gaussian process considered; it is assumed that they might be interpreted as the transformation of CPF into a “neoplastic disease phenotype” or as the non-proneness to cancer, respectively.

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