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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.

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.

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.

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.

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.

On-orbit service technology is one of the key technologies of space manipulation activities such as spacecraft life extension, fault spacecraft capture, on-orbit debris removal and so on. It is known that the failure satellites, space debris and enemy spacecrafts in space are almost all non-cooperative targets. Relatively accurate pose estimation is critical to spatial operations, but also a recognized technical difficulty because of the undefined prior information of non-cooperative targets. With the rapid development of laser radar, the application of laser scanning equipment is increasing in the measurement of non-cooperative targets. It is necessary to research a new pose estimation method for non-cooperative targets based on 3D point cloud. The paper aims to discuss these issues.

In this paper, a method based on the inherent characteristics of a spacecraft is proposed for estimating the pose (position and attitude) of the spatial non-cooperative target. First, we need to preprocess the obtained point cloud to reduce noise and improve the quality of data. Second, according to the features of the satellite, a recognition system used for non-cooperative measurement is designed. The components which are common in the configuration of satellite are chosen as the recognized object. Finally, based on the identified object, the ICP algorithm is used to calculate the pose between two frames of point cloud in different times to finish pose estimation.

The new method enhances the matching speed and improves the accuracy of pose estimation compared with traditional methods by reducing the number of matching points. The recognition of components on non-cooperative spacecraft directly contributes to the space docking, on-orbit capture and relative navigation.

Limited to the measurement distance of the laser radar, this paper considers the pose estimation for non-cooperative spacecraft in the close range.

The pose estimation method for non-cooperative spacecraft in this paper is mainly applied to close proximity space operations such as final rendezvous phase of spacecraft or ultra-close approaching phase of target capture. The system can recognize components needed to be capture and provide the relative pose of non-cooperative spacecraft. The method in this paper is more robust compared with the traditional single component recognition method and overall matching method when scanning of laser radar is not complete or the components are blocked.

This paper introduces a new pose estimation method for non-cooperative spacecraft based on point cloud. The experimental results show that the proposed method can effectively identify the features of non-cooperative targets and track their position and attitude. The method is robust to the noise and greatly improves the speed of pose estimation while guarantee the accuracy.

This paper aims to propose a united kinematic calibration method for a dual-machine system in automatic drilling and riveting. The method takes both absolute and relative pose accuracy into account, which will largely influence the machining accuracy of the dual-machine system and assembly quality.

A comprehensive kinematic model of the dual-machine system is established by the superposition of sub-models with pose constraints, which involves base frame parameters, kinematic parameters and tool frame parameters. Based on the kinematic model and the actual pose error data measured by a laser tracker, the parameters of coordinated machines are identified by the Levenberg–Marquardt method as a multi-objective nonlinear optimization problem. The identified parameters of the coordinated machines will be used in the control system.

A new calibration method for the dual-machine system is developed, including a comprehensive kinematic model and an efficient parameter identification method. The experiment results show that with the proposed method, the pose accuracy of the dual-machine system was remarkably improved, especially the relative position and orientation errors.

This method has been used in an aircraft assembly project. The calibrated dual-machine system shows a good performance on system coordination and machining accuracy.

This paper proposes a new method with high accuracy and efficiency for the dual-machine system calibration. The research can be extended to multi-machine and multi-robot fields to improve the system precision.

With the improvement of modern aircraft requirements for safety, long life and economy, higher quality aircraft assembly is needed. However, due to the manufacturing and assembly errors of the posture adjustment mechanism (PAM) used in the digital assembly of aircraft large component (ALC), the posture alignment accuracy of ALC is difficult to be guaranteed, and the posture adjustment stress is easy to be generated. Aiming at these problems, this paper aims to propose a calibration method of redundant actuated parallel mechanism (RAPM) for posture adjustment.

First, the kinematics model of the PAM is established, and the influence of the coupling relationship between the axes of the numerical control locators (NCL) is analyzed. Second, the calibration method based on force closed-loop feedback is used to calibrate each branch chain (BC) of the PAM, and the solution of kinematic parameters is optimized by Random Sample Consensus (RANSAC). Third, the uncertainty of kinematic calibration is analyzed by Monte Carlo method. Finally, a simulated posture adjustment system was built to calibrate the kinematics parameters of PAM, and the posture adjustment experiment was carried out according to the calibration results.

The experiment results show that the proposed calibration method can significantly improve the posture adjustment accuracy and greatly reduce the posture adjustment stress.

In this paper, a calibration method based on force feedback is proposed to avoid the deformation of NCL and bracket caused by redundant driving during the calibration process, and RANSAC method is used to reduce the influence of large random error on the calibration accuracy.

Semiconductor manufacturing industry requires highly accurate robot operation with short install/setup downtime.

We develop a fast, low cost and easy‐to‐operate calibration system for wafer‐handling robots. The system is defined by a fixture and a simple compensation algorithm. Given robot repeatability, end effector uncertainties, and the tolerance requirements of wafer placement points, we derive fixture design and placement specifications based on a statistical tolerance model.

By employing the fixture‐based calibration, we successfully relax the tolerance requirement of the end effector by 20 times.

Semiconductor manufacturing requires fast and easy‐to‐operate calibration systems for wafer‐handling robots. In this paper, we describe a new methodology to solve this problem using fixtures. We develop fixture design criteria and a simple compensate algorithm to satisfy calibration requirements. We also verify our approach by a physical example.

This study examines opinion leadership's personal and social characteristics to see which one is more effective in opinion leadership in four different industries: fashion, travel and tourism, wellness and book and literature. The specific subject of this investigation is how largely openness, exhibitionism and competence in interpersonal relationships and status and attitude homophily affect the opinion leadership and the decision-making of opinion leaders' followers.

The proposed model was tested with the questionnaire shared via stories featured on Instagram among followers of four micro-influencers in different industries. For the purpose of testing the offered hypotheses of this study, the partial least squares method was used.

The findings show that openness, exhibitionism and competence in interpersonal relationships have a substantial effect on opinion leadership. It was also evident that status and attitude homophily impact opinion leadership. The model supports the effect of both personal and social characteristics on opinion leadership; however, based on the results, the effect of personal characteristics on opinion leadership is more remarkable, both in a direct relationship and through the mediating role of para-social interaction.

This study is novel in categorizing opinion leaders' attributes in two different extents of personal and social characteristics. The authors defined a model of the effectiveness of each personal and social characteristic on opinion leaders. The model investigates whether the personal or social characteristics have the most effect on opinion leadership, particularly with the mediating role of para-social interaction.

The paper ' s aim is to report a research study on the mediator and outcome variable sets in scenario planning.

This is a cannonical correlation analysis (CCA)

Twso sets of variables; one as a predictor set that explained a significant amount of variability in the second, or outcome set of variables were found.

The study did not involve random selection or assignment and used perception-based measures.

The findings support scenario planning as a tool to reinforce certain decision styles and learning organization culture.

A critical contribution to scenario planning research, this study brings some order to the variety of variables espoused to be involved in scenario work. Clear outcomes are a learning culture and intuitive/dependent decision styles. The study makes a real contribution to quantitative scenario studies.

The fast pace of innovation and disruption in business processes and technology today requires employees of organizations to be continuously up-skilled and be able to adapt to changing practices. Training needs are becoming more personalized. Micro-learning and byte-sized training modules, easily accessible to employees, as and when required, are some of the major organizational needs. Training and development programs should be designed keeping in mind factors of employee engagement, involvement and extent of training transfer. The purpose of this paper is to explore whether artificial intelligence (AI) can lead training and development processes in organizations in the years to come.

The author has interviewed 27 HR and training professionals, in person, from across eight organizations in the FMCG, oil and natural gas and clothing and apparel industries. All these organizations have an annual turnover of greater than US$14.5m. A formal questionnaire was not followed since this research explores a new field in academia. Open-ended questions were used in the interviews, of which eight were common across all interviews. The mean interview duration was 25 min 33 s. The objective being to capture ideas and identify future trends, the analysis was done on a percentage basis and served as the foundation for a new training and development needs model for organizations.

Among the 27 HR/training professionals interviewed, 92.6 percent respondents believed that their organization/department requires knowledge management practices while 40.7 percent require the training content delivered to a fixed category of employees, to be updated continuously. Personalized learning was mentioned as a requirement by 63 percent of the respondents. In total, 92.6 percent HR/training professionals believed training programs should involve high employee engagement. In total, 51.9 percent would prefer on-the-go learning tools for their employees, while 33.33 percent respondents believed an intuitive e-learning interface would be useful for their organization/department. The findings also led to the foundation of an SIP model, which shall be useful in providing direction to AI systems in training and development practices.

The paper opens up avenues for further research to be conducted in identifying the areas of impact of AI in training and development. It paves the way for researchers to quantify training effectiveness and measure it with the help of AI.

The objective of the paper is to explore the opportunities for AI in training and development practices. Having identified the opportunities, it shall drive the practice of using AI across industries.

The thoughts in the paper have been ideated by the authors organically. Relevant data points from referred sources have been cited to back up those thoughts.

The coupling process between the loading mechanism and the tank car mouth is a crucial step in the tank car loading process. The purpose of this paper is to design a method to accurately measure the pose of the tanker car.

The collected image is first subjected to a gray enhancement operation, and the black parts of the image are extracted using Otsu’s threshold segmentation and morphological processing. The edge pixels are then filtered to remove outliers and noise, and the remaining effective points are used to fit the contour information of the tank car mouth. Using the successfully extracted contour information, the pose information of the tank car mouth in the camera coordinate system is obtained by establishing a binocular projection elliptical cone model, and the pixel position of the real circle center is obtained through the projection section. Finally, the binocular triangulation method is used to determine the position information of the tank car mouth in space.

Experimental results have shown that this method for measuring the position and orientation of the tank car mouth is highly accurate and can meet the requirements for industrial loading accuracy.

A method for extracting the contours of various types of complex tanker mouth is proposed. This method can accurately extract the contour of the tanker mouth when the contour is occluded or disturbed. Based on the binocular elliptic conical model and perspective projection theory, an innovative method for measuring the pose of the tanker mouth is proposed, and according to the space characteristics of the tanker mouth itself, the ambiguity of understanding is removed. This provides a new idea for the automatic loading of ash tank cars.