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This study aims to investigate whether the increasing robot adoption will affect employment rate and wages to contribute to the economic cycle and sustainable development in the world.

The authors introduce a two-way fixed effect model and ordinary least-squares (OLS) model to evaluate the influence based on relevant data of the eighteen countries with the largest robot stocks and robot densities in the world from 2006 to 2019 to test the influences and do the robustness test and endogeneity test by using empirical models.

The authors’ research findings suggest that increasing robot adoption can cause strong negative impacts on employment for both males and females in these economies. Second, the effect of robots on reducing job opportunities has penetrated different industries. It means that this negative impact of robots is comprehensive for the industry. Third, robot adoption can have a strong positive influence on wages and increase workers' incomes.

The limitations of the study are that the influence of industrial intelligence technologies on the circular economy is diversities in different countries. Thus, this study should consider the development levels of different economies to do additional confirmatory studies.

This study makes out the correlations between industrial robots and the employment market from the circular economy perspective. The result proves the existence of this influence relationship, and the authors propose some suggestions to promote sustainable economic development.

This paper addresses the activity of industrial intelligence technologies in the labor market. The employment market is an important part of the circular economy, and it will benefit social development if the government provides appropriate guidance for social investment and industrial layout.

This study is one of the few studies which considered the impact of industrial robots on employment and wages from the perspective of different industries, and this is very important for the circular economy in the world. The results of this paper provide an instructive reference for government policymakers and other countries to stabilize the labor market and optimize human resources for sustainable economic development.

A new round of technological revolution is impacting various aspects of society. However, the importance of technology adoption in fostering firm innovation is underexplored. Therefore, this study aims to investigate whether robot adoption affects technological innovation and how human capital plays a role in this relationship in the era of circular economy.

Based on the robot adoption data from the International Federation of Robotics (IFR) and panel data of China's listed manufacturing firms from 2011 to 2020, this study uses regression models to test the impact of industrial robots on firm innovation and the mediating role of human capital.

The results demonstrate that the adoption of industrial robots can significantly promote high-quality innovation. Specifically, a one-unit increase in the number of robots per 100 employees is associated with a 13.52% increase in the number of invention patent applications in the following year. The mechanism tests show that industrial robots drive firm innovation by accumulating more highly educated workers and allocating more workers to R&D jobs. The findings are more significant for firms in industries with low market concentration, in labor-intensive industries and in regions with a shortage of high-end talent.

Due to data limitations, the sample of this study is limited to listed manufacturing firms, so the impact of industrial robots on promoting innovation may be underestimated. In addition, this study cannot observe the dynamic process of human capital management by firms after adopting robots.

The Chinese government should continue to promote the intelligent upgrading of the manufacturing industry and facilitate the promotion of robots in innovation. This implication can also be applied to developing countries that hope to learn from China's experience. In addition, this study emphasizes the role of human capital in the innovation-promoting process of robots. This highlights the importance of firms to strengthen employee education and training.

The adoption of industrial robots has profoundly influenced the production and lifestyle of human society. This study finds that the adoption of robots contributes to firm innovation, which helps people gain a deeper understanding of the positive impacts brought about by industrial intelligence.

By exploring the impact of industrial robots on firm innovation, this study offers crucial evidence at the firm level to comprehend the economic implications of robot adoption based on circular economy and human perspectives. Moreover, this study reveals that human capital is an important factor in how industrial robots affect firm innovation, providing an important complement to previous studies.

Industrial robots are of great significance to the long-term development of family firms. Drawing on the lens of the principal–principal conflict, this paper aims to investigate the influence of family non-executive directors on robot adoption in Chinese family firms.

This paper selects the family firms in China from 2011 to 2019 as the sample. Furthermore, the authors manually collected the family non-executive directors and constructed the robot adoption variable utilizing data sourced from the International Federation of Robotics. In brief, this paper constructs a comprehensive framework of the mechanisms and additional tests pertaining to the influence of family non-executive directors on robot adoption.

This paper finds that family non-executive directors can promote robot adoption in family firms. The underlying mechanism analysis shows that family non-executive directors promote robot adoption by exerting financial and human effects. This paper further finds that the characteristics of family non-executive directors, such as kinship, differential shareholding and excessive directors, affect the role of family non-executive directors. Finally, robot adoption can improve future performance, and the promotional effect is more evident when family members are non-executive directors.

This paper contributes to the related literature from the following two aspects. Firstly, this paper decomposes the types of family directors to understand the role of family non-executive directors, which challenges the assumption that family board members are homogeneous in family firms. Second, this paper expands the research on the factors that influence robot adoption in emerging economies from the micro-enterprise level. In addition, the findings in this paper have managerial implications for family firms to optimize their strategic decisions with the help of the mode of board right allocation.

Parallelogram linkages are used to increase the stiffness of manipulators and allow precise control of end-effectors. They help maintain the orientation of connected links when the manipulator changes its position. They are implemented in many palletizing robots connected with binary, ternary and quaternary links through both active and passive joints. This limits the motion of some joints and hence results in relative and negative joint angles when assigning coordinate axes. This study aims to provide a simplified accurate model for manipulators built with parllelogram linkages to ease the kinematics calculations.

This study introduces a simplified model, replacing each parallelogram linkage with a single (binary) link with an active and a passive joint at the ends. This replacement facilitates countering motion while preserving subsequent link orientations. Validation of kinematics is performed on palletizing manipulators from five different OEMs. The validation of Dobot Magician and ABB IRB1410 was carried out in real time and in their control software. Other robots from ABB, Yaskawa, Kuka and Fanuc were validated using control environments and simulators.

The proposed model enables the straightforward derivation of forward kinematics and transforms hybrid robots into equivalent serial-link robots. The model demonstrates high accuracy streamlining the derivation of kinematics.

The proposed model facilitates the use of classical methods like the Denavit–Hartenberg procedure with ease. It not only simplifies kinematics derivation but it also helps in robot control and motion planning within the workspace. The approach can also be implemented to simplify the parallelogram linkages of robots with higher degrees of freedom such as the IRB1410.

This paper aims to propose a hybrid force/position controller based on the adaptive variable impedance.

First, the working space is divided into a force control subspace and a position subspace, the force control subspace adopts the position impedance control strategy. At the same time, the contact force model between the robot and the surface is analyzed in this space. Second, based on the traditional position impedance, the model reference adaptive control is introduced to provide an accurate reference position for the impedance controller. Then, the BP neural network is used to adjust the impedance parameters online.

The experimental results show that compared with the traditional PI control method, the proposed method has a higher flexibility, the dynamic response accommodation time is reduced by 7.688 s and the steady-state error is reduced by 30.531%. The overshoot of the contact force between the end of robot and the workpiece is reduced by 34.325% comparing with the fixed impedance control method.

The proposed control method compares with a hybrid force/position based on PI control method and a position fixed impedance control method by simulation and experiment.

The adaptive variable impedance control method improves accuracy of force tracking and solves the problem of the large surfaces with robot grinding often over-polished at the protrusion and under-polished at the concave.

In typical model-based calibration, linearization errors are derived inevitably, and non-negligible negative impact will be induced on the identification results if the rotational kinematic errors are not small enough or the lengths of links are too long, which is common in the industrial cases. Thus, an accurate two-step kinematic calibration method minimizing the linearization errors is presented for a six-DoF serial robot to improve the calibration accuracy.

The negative impact of linearization on identification accuracy is minimized by removing the responsible linearized kinematic errors from the complete kinematic error model. Accordingly, the identification results of the dimension-reduced new model are accurate but not complete, so the complete kinematic error model, which achieves high identification accuracy of the rest of the error parameters, is combined with this new model to create a two-step calibration procedure capable of highly accurate identification of all the kinematic errors.

The proportions of linearization errors in measured pose errors are quantified and found to be non-negligible with the increase of rotational kinematic errors. Thus, negative impacts of linearization errors are analyzed quantitatively in different cases, providing the basis for allowed kinematic errors in the new model. Much more accurate results were obtained by using the new two-step calibration method, according to a comparison with the typical methods.

This new method achieves high accuracy with no compromise on completeness, is easy to operate and is consistent with the typical method because the second step with the new model is conveniently combined without changing the sensors or measurement instrument setup.

Anthropomorphism plays a crucial role in the deployment of human-like robots in hospitality and tourism. This study aims to propose an anthropomorphism-based typology of artificial intelligence (AI) robots, based on robot attributes, usage, function and application across different operational levels.

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist, the research was conducted in two stages. A search strategy was implemented to explore anthropomorphism-based AI robots and to develop a robot typology.

This study provides a comprehensive typology of anthropomorphism-based AI robots used in tourism and hospitality and classifies them into four types, namely, chatbots, mechanoids, humanoids and android robots. Each type features distinct functions and applications.

The findings can assist companies in using anthropomorphic robots to improve service and strengthen competitiveness. This study offers valuable insights to managers for deploying AI robots across diverse service sectors.

This research provides a novel typology of hospitality and tourism AI robots and extends the understanding of anthropomorphism in human–robot interaction. This typology encompasses both virtual and physical robots, providing clarity on their attributes, usage, functions and applications across diverse areas of hospitality operations.

拟人化在酒店和旅游业中的人类化机器人部署中起着至关重要的作用。本研究提出了基于拟人化的AI机器人分类学, 基于机器人的属性、使用、功能和在不同运营层面的应用。

按照系统评价和荟萃分析（PRISMA）检查表, 研究分为两个阶段进行。实施了搜索策略, 探索基于拟人化的AI机器人, 并开发了机器人分类学。

本研究提供了在旅游和酒店业中使用的基于拟人化的 AI 机器人的全面分类学, 并将它们分为四类, 即聊天机器人、机械机器人、人形机器人和仿生机器人。每种类型具有不同的功能和应用。

研究结果可以帮助企业利用拟人化机器人提升服务水平, 增强竞争力。研究为管理者在各种服务领域部署 AI 机器人提供了宝贵的见解。

本研究提供了一种新颖的酒店和旅游业 AI 机器人分类学, 并扩展了拟人化在人机交互中的理解。这种分类学涵盖了虚拟和实体机器人, 清晰地描述了它们在酒店业运营的各个领域中的属性、使用、功能。

Aiming at the problem of insufficient adaptability of robot motion planners under the diversity of end-effector constraints, this paper proposes Transformation Cross-sampling Framework (TC-Framework) that enables the planner to adapt to different end-effector constraints.

This work presents a standard constraint methodology for representing end-effector constraints as a collection of constraint primitives. The constraint primitives are merged sequentially into the planner, and a unified constraint input interface and constraint module are added to the standard sampling-based planner framework. This approach enables the realization of a generic planner framework that avoids the need to build separate planners for different end-effector constraints.

Simulation tests have demonstrated that the planner based on TC-framework can adapt to various end-effector constraints. Physical experiments have also confirmed that the framework can be used in real robotic systems to perform autonomous operational tasks. The framework’s strong compatibility with constraints allows for generalization to other tasks without modifying the scheduler, significantly reducing the difficulty of robot deployment in task-diverse scenarios.

This paper proposes a unified constraint method based on constraint primitives to enhance the sampling-based planner. The planner can now adapt to different end effector constraints by opening up the input interface for constraints. A series of simulation tests were conducted to evaluate the TC-Framework-based planner, which demonstrated its ability to adapt to various end-effector constraints. Tests on a physical experimental system show that the framework allows the robot to perform various operational tasks without requiring modifications to the planner. This enhances the value of robots for applications in fields with diverse tasks.

Regular cable trench inspection is crucial, and robotics automation provides an efficient and safer alternative to manual labor. However, existing robots have limited capabilities in traversing obstacles and lack a mechanical arm for detecting cables and equipment. This study aims to develop an intelligent robot for cable trench inspection, enhancing obstacle-crossing abilities and incorporating a mechanical arm for inspection tasks.

This study presents an intelligent robot for cable trench inspection, featuring a six-degree-of-freedom mechanical arm mounted on a six-track chassis with four flippers. The robot's climbing and obstacle-crossing stability, as well as the motion range of the mechanical arm, are analyzed. The positioning, navigation and remote monitoring systems are developed. Experiments, including climbing and obstacle-crossing performance tests, along with navigation and positioning system tests, are conducted. Finally, the robot's practicability is verified through field testing.

Equipped with flipper tracks, the cable trench inspection robot can traverse obstacles up to 30 cm high and maintain stable locomotion on 30° slopes. Its navigation system enables autonomous operation, while the mechanical arm performs cable current detection tasks. The remote monitoring system provides comprehensive control of the robot and environmental parameter monitoring in cable trenches.

The front and rear flipper tracks enhance the robot's ability to traverse obstacles in cable trenches. The mechanical arm addresses cable current and equipment contact detection issues. The navigation and remote monitoring systems improve the robot's autonomous operation and environmental monitoring capabilities. Implementing this robot can advance the automation and intelligence of cable trench inspections.

The paper focuses on the issue of manual rebar-binding tasks in the construction industry, which are marked by high labor intensity, high costs and inefficient operations. The rebar-binding robots that are currently available are not fully mature. Most of them can only bind one or two nodes in one position, which leads to significant time wastage in movement. Based on a new type of rebar-binding robot, this paper aims to propose a new movement and binding control that reduces manpower and enhances efficiency.

The robot is combined with photoelectric sensors, travel switches and other sensors. It is supposed to move accurately and run in a limited area on the rebar mesh through logical judgment, speed control and position control. Machine vision is used by the robot to locate the rebar nodes and then adjusts the binding-gun position to ensure that multiple rebar nodes are bound sequentially.

By moving on the rebar mesh with accuracy, the robot meets the positioning accuracy requirements of the binding module, with experimental testing accuracy within 5 mm. Furthermore, its ability to bind four rebar nodes in one place results in a high efficiency and a binding effect that meets building standards.

The innovative design of the robot can adapt itself to the rebar mesh, move accurately to the target position and bind four nodes at that position, which reduces the number of movements on the mesh. Repetitive and heavy rebar-binding tasks can be efficiently completed by the robot, which saves human resources, reduces worker labor intensity and reduces construction overhead. It provides a more feasible and practical solution for using robots to bind rebar nodes.