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This paper aims to enhance the machining accuracy of hybrid robots by treating the moving platform as the first joint of a serial robot for direct position measurement and integrating external grating sensors with motor encoders for real-time error compensation.

Initially, a spherical coordinate system is established using one linear and two circular grating sensors. This system enables direct acquisition of the moving platform’s position in the hybrid robot. Subsequently, during the coarse interpolation stage, the motor command for the next interpolation point is dynamically updated using error data from external grating sensors and motor encoders. Finally, fuzzy proportional integral derivative (PID) control is applied to maintain robot stability post-compensation.

Experiments were conducted on the TriMule-600 hybrid robot. The results indicate that the following errors of the five grating sensors are reduced by 94%, 93%, 80%, 75% and 88% respectively, after compensation. Using the fourth drive joint as an example, it was verified that fuzzy adaptive PID control performs better than traditional PID control.

The proposed online error compensation strategy significantly enhances the positional accuracy of the robot end, thereby improving the actual processing quality of the workpiece.

This method presents a technique for achieving online error compensation in hybrid robots, which promotes the advancement of the manufacturing industry.

This paper proposes a cost-effective and practical method for online error compensation in hybrid robots using grating sensors, which contributes to the advancement of hybrid robot technology.

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.

The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.

Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.

This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.

There are various uncertain and nonlinear problems in hydraulic legged robot systems, including parameter uncertainty, unmodeled dynamics and external disturbances. This study aims to eliminate uncertainties and improve the foot trajectory tracking control performance of hydraulic legged robots, a high-performance foot trajectory tracking control method based on fixed-time disturbance observers for hydraulic legged robots is proposed.

First, the robot leg mechanical system model and hydraulic system model of the hydraulic legged robot are established. Subsequently, two fixed-time disturbance observers are designed to address the unmatched lumped uncertainty and match lumped uncertainty in the system. Finally, the lumped uncertainties are compensated in the controller design, and the designed motion controller also achieves fixed-time stability.

Through simulation and experiments, it can be found that the proposed tracking control method based on fixed-time observers has better tracking control performance. The effectiveness and superiority of the proposed method have been verified.

Both the disturbance observers and the controller achieve fixed-time stability, effectively improving the performance of foot trajectory tracking control for hydraulic legged robots.

This study aims to address the issues of limited pipe diameter adaptability and low inspection efficiency of current pipeline inspection robots, a new type of pipeline inspection robot capable of adapting to various pipe diameters was designed.

The diameter-changing mechanism uses a multilink elastic telescopic structure consisting of telescopic rods, connecting rods and wheel frames, driven by a single motor with a helical drive scheme. A geometric model of the position relationships of the hinge points was established based on the two extreme positions of the diameter-changing mechanism.

A pipeline inspection robot was designed using a simple linkage agency, which significantly reduced the weight of the robot and enhanced its adaptive pipe diameter ability. The analysis determined that the robot could accommodate pipe diameters ranging from 332 mm to 438 mm. A static equilibrium equation was established for the robot in the hovering state, and the minimum pressing force of the wheels against the pipe wall was determined to be 36.68 N. After experimental testing, the robots could successfully pass a height of 15 mm, demonstrating the good obstacle capacity of the robot.

This paper explores and proposes a new type of multilink elastic telescopic variable diameter pipeline inspection robot, which has the characteristics of strong adaptability and flexible operation, which makes it more competitive in the field of pipeline inspection robots and has great potential market value.

The robot is characterized by the innovative design of a multilink elastic telescopic structure and the use of a single motor to drive the wheel for spiral motion. On the basis of reducing the weight of the robot, it has good pipeline adaptability, climbing ability and obstacle-crossing ability.

By analyzing the shortcomings of existing insulator robots, a novel ultra high voltage (UHV) insulator climbing robot, which could transfer between adjacent insulator strings, is proposed for operation on 800KV multiple-string insulators. An extended inchworm-like configuration was chosen and a stable gripping claw suitable for the insulator string was designed to enable the robot to multiple-string insulators. Then a set of nonheuristic structural parameters that can influence energy consumption was chosen to formulate a nonlinear optimization problem based on the configuration, which improved the energy efficiency of the robot during progressing along a string of insulator.

The purpose of this paper is to design an insulator climbing robot for operation on UHV multiple-string insulators, which could transfer between adjacent insulator strings and progressed along a string of insulator with high energy efficiency.

A physical prototype was constructed that can operate at the speed of six pieces per minute (approximately 1.44 meters per minute) on a single string and complete transference between adjacent strings in 45 s. The energy consumption of joints during progressed along a string of insulator had been reduced by 38.8% with the optimized parameters, demonstrating the consistency between the experimental and simulation results.

An insulator climbing robot for operation on UHV multiple-string insulators has been developed with energy consumption optimization design. The robot can transfer between adjacent insulator strings and progressed along a string of insulator with high energy efficiency. The CLIBOT could be expanded to detect or clean the insulators with similar specification.

Along with the development of the robotics industry, service robots have been gradually used in the hospitality industry. Nevertheless, service robot categorization and the fulfillment of the cognitive and emotional needs of consumers by hotel service robots have yet to be fully explored. Hence, the purpose of this study are to categorize hotel service robots, to explore consumers’ robot hotel experience, to identify the consumers’ preference of hotel service robot in general, to reveal consumers’ preference for hotel service robots based on their fulfillment of emotional needs and to examine the completion of cognitive–analytical and emotional–social tasks.

Through in-depth interviews with technology managers and questionnaire survey among consumers who have and have not had robot hotel stay experience to achieve the aforementioned research objectives.

Findings of in-depth interviews show that service robots can be categorized as check-in/out robots, artificial intelligence (AI) robots and service delivery robots. Results of questionnaire survey indicate that consumers prefer non-humanoid robots (n = 213, p = 47.87%) among check-in/out robots, the Xiaodu Smart Display (n = 163, p = 36. 63%) among the AI robots and the machine-shaped robot porter (I) (n = 178, p = 40.00%) among the service delivery robots.

This study provides implications, such as the adoption of robot-shaped AI with a screen display, to hotel managers to meet the needs of consumers regarding the completion of cognitive–analytical and emotional–social tasks of robots.

This study extends uncanny valley theory by identifying preference for the shape and functions of different categories of service robots and contributes to the limited literature on hotel robots.

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.

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。

This study explores how customers' individual characteristics and perceptions affect acceptance of service robots as a hotel workforce. The Interactive Technology Acceptance Model (iTAM) has inspired us to investigate effects of customers' technological self-efficacy, perceived interactivity, sense of utility, and enjoyment-level of acceptance related to hotel-service robots as staff.

Data were collected from 224 customers via an online questionnaire conducted in the period April–June 2022 by convenience sampling, and then analyzed by using partial least squares – structural equation modeling (PLS-SEM).

The findings show that customers' technological self-efficacy and perceived interactivity with service robots enhances perceived usefulness and perceived enjoyment, serving as functional and emotional value components of service robots. They also demonstrate that robot's interactivity outweighs other robot's value components, such as perceived usefulness and perceived enjoyment for acceptance of service robots as employees in hotels.

While empirically validating the iTAM, this study emphasizes service robot interactivity as the most important aspect for customers' acceptance, and it adds a new perspective regarding the underexplored role of the customer-robot interface. Combining specific dimensions from different technology acceptance models (functional/socio-emotional/relational; utilitarian/hedonic) the study contributes to the service robot literature currently missing a more holistic understanding of consumers' experience and adoption drivers, and it provides managerial guidance on how to successfully implement service robots in hotel environments.