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This paper aims to present error compensation based on surface reconstruction to improve the positioning accuracy of industrial robots.

In previous research, it has been proved that the positioning error of industrial robots is continuous on the two-dimensional manifold of six-joint space. The point cloud generated by positioning error data can be used to fit the continuous surfaces, which makes it possible to apply surface reconstruction on error compensation. The moving least-squares interpolation and the B-spline method are used for the error surface reconstruction.

The results of experiments and simulations validate the effectiveness of error compensation by the moving least-squares interpolation and the B-spline method.

The proposed methods can control the average of compensated positioning error within 0.2 mm, which meets the requirement of a tolerance (±0.5 mm) for fastener hole drilling in aircraft assembly.

The error surface reconstruction based on the B-spline method has great superiority because fewer sample points are needed to use this method than others while keeping the compensation accuracy at the same level. The control points of the B-spline error surface can be adjusted with measured data, which can be applied for the error prediction in any temperature field.

This paper aims to propose a calibration model for kinematic parameters identification of serial robot to improve its positioning accuracy, which only requires position measurement of the end-effector.

The proposed model is established based on local frame representation of the product of exponentials (local POE) formula, which integrates all kinematic errors into the twist coordinates errors; then they are identified with the tool frame’ position deviations simultaneously by an iterative least squares algorithm.

To verify the effectiveness of the proposed method, extensive simulations and calibration experiments have been conducted on a 4DOF SCARA robot and a 5DOF drilling machine, respectively. The results indicate that the proposed model outperforms the existing model in convergence, accuracy, robustness and efficiency; fewer measurements are needed to gain an acceptable identification result.

This calibration method has been applied to a variable-radius circumferential drilling machine. The machine’s positioning accuracy can be significantly improved from 11.153 initially to 0.301 mm, which is well in the tolerance (±0.5 mm) for fastener hole drilling in aircraft assembly.

An accurate and efficient kinematic calibration model has been proposed, which satisfies the completeness, continuity and minimality requirements. Due to generality, this model can be widely used for serial robot kinematic calibration with any combination of revolute and prismatic joints.

To gain accurate support for large aircraft structures by ball joints in aircraft digital assembly, this paper aims to propose a novel approach based on visual servoing such that the positioner’s ball-socket can automatically and adaptively approach the ball-head fixed on the aircraft structures.

Image moments of circular marker labeled on the ball-head are selected as visual features to control the three translational degrees of freedom (DOFs) of the positioner, where the composite Jacobian matrix is full rank. Kalman–Bucy filter is adopted for its online estimation, which makes the control scheme more flexible without system calibration. A combination of proportional control with sliding mode control is proposed to improve the system stability and compensate uncertainties of the system.

The ball-socket can accurately and smoothly reach its desired position in a finite time (50 s). Positional deviations between the spherical centers of ball-head and ball-socket in the X-Y plane can be controlled within 0.05 mm which meets the design requirement.

The proposed approach has been integrated into the pose alignment system. It has shown great potential to be widely applied in the leading support for large aircraft structures in aircraft digital assembly.

An adaptive approach for accurate support of large aircraft structures is proposed, which possesses characteristics of high precision, high efficiency and excellent stability.

The purpose of this paper is to introduce a robotic boring system for intersection holes in aircraft assembly. The system is designed to improve the boring quality and position accuracy of the intersection holes.

To improve the boring quality of intersection holes, a robot posture optimization model is established. The target of the model is to maximize the robot stiffness and the variate is location of the robot on the guideway. The model is solved by the iterative IKP algorithm based on the Jacobian matrix. To improve the position accuracy of intersection holes, a robot positioning accuracy compensation method is introduced. In the method, a laser tracker is used to measure the actual position and orientation of the boring bar. Combined with the desired position and orientation, the error can be obtained and compensated.

In practical case of the robotic boring system, the robot stiffness is effectively improved and the surface roughness of intersection holes achieves a grade of Ra0.8. Besides, the robot end achieves a position accuracy of 0.05 mm and an orientation accuracy of 0.05°.

The robotic boring system has been applied successfully in one of the aircraft assembly projects in northwest China.

The robotic boring system can be applied for machining intersection holes in an aircraft assembly. With the robot posture optimization method and accuracy compensation method, the boring quality and position accuracy of the intersection holes can be guaranteed.

This paper aims to propose a roadmap to control the robot–subassembly (R–S) coordination errors in movable robotic drilling. Fastener hole drilling for multi-station aircraft assembly demands a robotic drilling system with expanded working volume and high positioning accuracy. However, coordination errors often exist between the robot and the subassembly to be drilled because of disturbances.

Mechanical pre-locating and vision-based robot base frame calibration are consecutively implemented to achieve in-process robot relocation after station transfer. Thus, coordination errors induced by robotic platform movements, inconsistent thermal effects, etc. are eliminated. The two-dimensional (2D) vision system is applied to measure the remainder of the R–S coordination errors, which is used to enhance the positioning accuracy of the robot. Accurate estimation of measured positioning errors is of great significance for evaluating the positioning accuracy. For well estimation of the positioning errors with small samples, a bootstrap approach is put forward.

A roadmap for R–S coordination error control using a 2D vision system, composed of in-process relocation, coordination error measurement and drilled position correction, is developed for the movable robotic drilling.

The proposed roadmap has been integrated into a drilling system for the assembly of flight control surfaces of a transport aircraft in Aviation Industry Corporation of China. The position accuracy of the drilled fastener holes is well ensured.

A complete roadmap for controlling coordination errors and improving positioning accuracy is proposed, which makes the high accuracy and efficiency available in movable robotic drilling for aircraft manufacturing.

Most previous studies have discussed the factors that drive the development of smart tourism or its benefits. Nevertheless, hindrances to smart tourism development have been largely ignored by previous studies. Hence, to bridge the aforementioned research gap, the present study identified the hindrances to smart tourism development in Hong Kong, which has been affected by the recent pandemic, based on the adjusted destination competitiveness conceptual models.

The purpose of this study is to investigate the hindrances to smart tourism development. A qualitative interview method using a semi-structured questionnaire with open-ended questions was adopted in the present study.

Findings of the present study indicated that the hindrances in developing smart tourism in Hong Kong are from four primary perspectives, namely, economic, sociocultural, technological and planning and management.

Seeking cooperation from popular tourist destinations can address the economic issues of market size and return on investment in technological infrastructure. Experiences, as the core product of travel, should be highlighted in smart tourism development and enterprises should be given support for the transformation.

Theoretically, based on the adjusted conceptual models for destination competitiveness, the present study identifies the macro factors that hinder the development of smart tourism in Hong Kong from different primary perspectives. Practically, factors such as policy support, which includes public funding or low-interest loans, are essential to supporting preliminary finances to attract entrepreneurs and young talents to participate in the development.

智慧旅游发展的障碍

之前的大多数研究都讨论了推动智慧旅游发展的因素或其好处。然而, 以往的研究在很大程度上忽略了智慧旅游发展的障碍。因此, 为弥合上述研究空白, 本研究基于调整后的目的地竞争力概念模型, 确定了受近期疫情影响的香港智慧旅游发展的障碍。

本研究采用了带有开放式问题的半结构化问卷的定性访谈方法来采集数据。

本研究结果表明, 香港发展智慧旅游的障碍主要来自四个方面, 即经济、社会文化、技术以及规划和管理。

寻求热门旅游目的地的合作可以解决市场规模和技术基础设施投资回报等经济问题。智慧旅游发展应突出体验作为旅游核心产品, 支持企业转型。

理论上, 本研究基于调整后的目的地竞争力概念模型, 从不同的首要视角识别阻碍香港智慧旅游发展的宏观因素。从实际意义上来讲, 公共资金或低息贷款在内的政策支持等因素对于支持初步资金, 以及吸引企业家和青年人才来参与发展至关重要。

The purpose of this study is to explore the impact of online reviews on answer adoption in virtual Q&A communities, with an eye toward extending knowledge exchange and community management.

Online reviews contain rich cognitive and emotional information about community members regarding the provided answers. As feedback information on answers, it is crucial to explore how online reviews affect answer adoption. Based on signaling theory, a research model reflecting the influence of online reviews on answer adoption is established and empirically examined by using secondary data with 69,597 Q&A data and user data collected from Zhihu. Meanwhile, the moderating effects of the informational and emotional consistency of reviews and answers are examined.

The negative binomial regression results show that both answer-related signals (informational support and emotional support) and answerers-related signals (answerers’ reputations and expertise) positively impact answer adoption. The informational consistency of reviews and answers negatively moderates the relationships among information support, emotional support and answer adoption but positively moderates the effect of answerers’ expertise on answer adoption. Furthermore, the emotional consistency of reviews and answers positively moderates the effect of information support and answerers’ reputations on answer adoption.

Although previous studies have investigated the impacts of answer content, answer source credibility and personal characteristics of knowledge seekers on answer adoption in virtual Q&A communities, few have examined the impact of online reviews on answer adoption. This study explores the impacts of informational and emotional feedback in online reviews on answer adoption from a signaling theory perspective. The results not only provide unique ideas for community managers to optimize community design and operation but also inspire community users to provide or utilize knowledge, thereby reducing knowledge search costs and improving knowledge exchange efficiency.

Knowledge adoption is the key to effective knowledge exchange in virtual question-and-answer (Q&A) communities. Although previous studies have examined the effects of knowledge content, knowledge source credibility and the personal characteristics of knowledge seekers on knowledge adoption in virtual Q&A communities from a static perspective, the impact of answer deviation on knowledge adoption has rarely been explored from a context-based perspective. The purpose of this study is to explore the impact of two-way deviation on knowledge adoption in virtual Q&A communities, with the aim of expanding the understanding of knowledge exchange and community management.

The same question and the same answerer often yield multiple answers. Knowledge seekers usually read multiple answers to make adoption decisions. The impact of deviations among answers on knowledge seekers' knowledge adoption is critical. From a context-based perspective, a research model of the impact of the deviation of horizontal and vertical answers on knowledge adoption is established based on the heuristic-systematic model (HSM) and empirically examined with 88,287 Q&A data points and answerer data collected from Zhihu. Additionally, the moderation effects of static factors such as answerer reputation and answer length are examined.

The negative binomial regression results show that the content and emotion deviation of horizontal answers negatively affect knowledge seekers' knowledge adoption. The content deviation of vertical answers is negatively associated with knowledge adoption, while the emotion deviation of vertical answers is positively related to knowledge adoption. Moreover, answerer reputation positively moderates the negative effect of the emotion deviation of horizontal answers on knowledge adoption. Answer length weakens the negative correlation between the content deviation of horizontal and vertical answers and knowledge adoption.

This study extends previous research on knowledge adoption from a static perspective to a context-based perspective. Moreover, information deviation is expanded from a one-way variable to a two-way variable. The combined effects of static and contextual factors on knowledge adoption are further uncovered. This study can not only help knowledge seekers identify the best answers but also help virtual Q&A community managers optimize community design and operation to reduce the cost of knowledge search and improve the efficiency of knowledge exchange.

Mindfulness, while being suggested as an important psychological cognitive capability of customers, has received insufficient attention in studies of transformative services characterized by challenging cocreation behaviors. It is unclear about the contributions of mindfulness to customers’ cocreation and transformative outcomes. This study aims to investigate the direct, indirect, mediating and moderating relationships to explain how mindfulness sustains cocreation effort, increases perceived service value and ultimately enhances the diffusion from the service value to customer well-being.

A structural model was developed and tested using the CB-SEM method. Data were surveyed from two transformative service industries, yoga training and higher education (N = 283 and 273 cases, respectively).

Customer mindfulness has a positive relationship with cocreation effort, which in turn positively associates with perceived value. Additionally, mindfulness has a direct relationship with perceived value, which then is the full mediator in the relationships between mindfulness, cocreation effort and life satisfaction. Mindfulness also moderates the transformation from service value (immediate outcome) to life satisfaction (long-term outcome).

Transformative service providers and policymakers should acknowledge and develop strategies to cultivate customers’ mindfulness, which subsequently fosters their value cocreation effort and enhances their well-being.

This research puts forward the concept of mindfulness, a trainable cognitive capability of customers, and shows its importance in transformative service cocreation. This paper provides a full structural mechanism explaining how mindfulness helps cocreate a transformative service and diffuse its immediate value to customer life satisfaction.