Yang Chuangui, Mi Liang, Liu Xingbao, Xia Yangqiu, Qiang Teng and Lin Han
This paper aims to propose a reasonable method to evaluate uncertainty of measurement of industrial robots’ orientation repeatability and solve the non-linear problem existing in…
Abstract
Purpose
This paper aims to propose a reasonable method to evaluate uncertainty of measurement of industrial robots’ orientation repeatability and solve the non-linear problem existing in its evaluation procedure.
Design/methodology/approach
Firstly, a measurement model of orientation repeatability, based on laser tracker, is established. Secondly, some factors, influencing the measurement result of orientation repeatability, are identified, and their probability distribution functions are modelled. Thirdly, based on Monte Carlo method, an uncertainty evaluation model and algorithm of measurement of industrial robot’s orientation repeatability are built. Finally, an industrial robot is taken as the research object to validate the rationality of proposed method.
Findings
Results show that the measurement model of orientation repeatability of industrial robot is non-linear, and the proposed method can reasonably and objectively estimate uncertainty of measurement of industrial robots’ orientation repeatability.
Originality/value
This paper, based on Monte Carlo method and experimental work, proposes an uncertainty evaluation method of measurement of industrial robots’ orientation repeatability which can solve the non-linear problem and provide a reasonable and objective evaluation. And the stochastic ellipsoid approach is firstly taken to model the repeatability of laser tracker. Additionally, this research is beneficial to decide whether the orientation repeatability of the industrial robot meets its requirements.
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Keywords
Chuangui Yang, Junwen Wang, Liang Mi, Xingbao Liu, Yangqiu Xia, Yilei Li, Shaoxing Ma and Qiang Teng
This paper aims to propose a four-point measurement model for directly measuring the pose (i.e. position and orientation) of industrial robot and reducing its calculating error…
Abstract
Purpose
This paper aims to propose a four-point measurement model for directly measuring the pose (i.e. position and orientation) of industrial robot and reducing its calculating error and measurement uncertainty.
Design/methodology/approach
A four-point measurement model is proposed for directly measuring poses of industrial robots. First, this model consists of a position measurement model and an orientation model gotten by the position of spherically mounted reflector (SMR). Second, an influence factor analysis, simulated by Monte Carlo simulation, is performed to investigate the influence of certain factors on the accuracy and uncertainty. Third, comparisons with the common method are carried out to verify the advantage of this model. Finally, a test is carried out for evaluating the repeatability of five poses of an industrial robot.
Findings
In this paper, results show that the proposed model is better than the three-SMRs model in measurement accuracy, measurement uncertainty and computational efficiency. Moreover, both measurement accuracy and measurement uncertainty can be improved by using the proposed influence laws of its key parameters on the proposed model.
Originality/value
The proposed model can measure poses of industrial robots directly, accurately and effectively. Additionally, influence laws of key factors on the accuracy and uncertainty of the proposed model are given to provide some guidelines for improving the performance of the proposed model.
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Yang Chuangui, Liu Xingbao, Yue Xiaobin, Mi Liang, Wang Junwen, Xia Yangqiu, Yu Hailian and Chen Heng
This paper aims to solve the nonlinear problem in the uncertainty evaluation of the measurement of the positioning repeatability (RP) of industrial robots and provide guidance to…
Abstract
Purpose
This paper aims to solve the nonlinear problem in the uncertainty evaluation of the measurement of the positioning repeatability (RP) of industrial robots and provide guidance to restrict the uncertainty of measurement of RP (uRP).
Design/methodology/approach
Firstly, some uncertain sources existing in the measurement procedure of RP are identified. Secondly, the probability distribution function (PDF) of every source is established on the basis of its measurements. Some spatial combined normal distributions are adopted. Then, a method, based on Monte Carlo method (MCM) and established measurement model, is developed for the estimation of uRP. Thirdly, some tests are developed for the identification and validation of the selected PDFs of uncertain sources. Afterwards, the proposed method is applied for the evaluation and validation of the uRP. Finally, influence analyses of some key factors are proposed for the quantification of their relative contributions to uRP.
Findings
Results show that the proposed method can reasonably and objectively estimate the uRP of the selected industrial robot, and changes of the industrial robots’ position and the laser trackers measurement are correlated. Additionally, the uRP of the selected industrial robot can be restricted by using the results of its key factors on uRP.
Originality/value
This paper proposes the spatial combined normal distribution to model the uncertainty of the repeatability of the laser tracker and industrial robot. Meanwhile, the proposed method and influence analyses can be used in estimating and restricting the uRP and thus useful in determining whether the RP of a tested industrial robot meets its requirements.
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Xingbao (Simon) Hu, Yang Yang and Sangwon Park
Online ratings (review valence) have been found to exert a strong influence on hotel room prices. This study aims to systematically synthesize research estimating the impact of…
Abstract
Purpose
Online ratings (review valence) have been found to exert a strong influence on hotel room prices. This study aims to systematically synthesize research estimating the impact of online ratings on room rates using a meta-analytical method.
Design/methodology/approach
From major academic databases, a total of 163 estimates of the effects of online ratings on room rates were coded from 22 studies across different countries through a systematic review of relevant literature. All estimates were converted into elasticity-type effect sizes, and a hierarchical linear meta-regression was used to investigate factors explaining variations in the effect sizes.
Findings
The median elasticity of online ratings on hotel room rates was estimated to be 0.851. Meta-regression results highlighted four categories of factors moderating the size of this elasticity: data characteristics, research settings, variable measures and publication outlet. Among sub-ratings, results revealed value rating and room rating to exert the largest impact on room rates, whereas staff and cleanliness ratings demonstrated non-significant impacts.
Practical implications
This study provides practical implications on the relative importance of different types of online ratings for online reputation and revenue management.
Originality/value
This study represents the first research effort to understand factors moderating the effects of online ratings on hotel room rates based on a quantitative review of the literature. Moreover, this study provides beneficial insights into the specification of empirical hedonic pricing models and data-collection strategies, such as the selection of price variables and choices of model functional forms.