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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.

To make the robot that have real autonomous ability is always the goal of mobile robot research. For mobile robots, simultaneous localization and mapping (SLAM) research is no longer satisfied with enabling robots to build maps by remote control, more needs will focus on the autonomous exploration of unknown areas, which refer to the low light, complex spatial features and a series of unstructured environment, lick underground special space (dark and multiintersection). This study aims to propose a novel robot structure with mapping and autonomous exploration algorithms. The experiment proves the detection ability of the robot.

A small bio-inspired mobile robot suitable for underground special space (dark and multiintersection) is designed, and the control system is set up based on STM32 and Jetson Nano. The robot is equipped with double laser sensor and Ackerman chassis structure, which can adapt to the practical requirements of exploration in underground special space. Based on the graph optimization SLAM method, an optimization method for map construction is proposed. The Iterative Closest Point (ICP) algorithm is used to match two frames of laser to recalculate the relative pose of the robot, which improves the sensor utilization rate of the robot in underground space and also increase the synchronous positioning accuracy. Moreover, based on boundary cells and rapidly-exploring random tree (RRT) algorithm, a new Bio-RRT method for robot autonomous exploration is proposed in addition.

According to the experimental results, it can be seen that the upgraded SLAM method proposed in this paper achieves better results in map construction. At the same time, the algorithm presents good real-time performance as well as high accuracy and strong maintainability, particularly it can update the map continuously with the passing of time and ensure the positioning accuracy in the process of map updating. The Bio-RRT method fused with the firing excitation mechanism of boundary cells has a more purposeful random tree growth. The number of random tree expansion nodes is less, and the amount of information to be processed is reduced, which leads to the path planning time shorter and the efficiency higher. In addition, the target bias makes the random tree grow directly toward the target point with a certain probability, and the obtained path nodes are basically distributed on or on both sides of the line between the initial point and the target point, which makes the path length shorter and reduces the moving cost of the mobile robot. The final experimental results demonstrate that the proposed upgraded SLAM and Bio-RRT methods can better complete the underground special space exploration task.

Based on the background of robot autonomous exploration in underground special space, a new bio-inspired mobile robot structure with mapping and autonomous exploration algorithm is proposed in this paper. The robot structure is constructed, and the perceptual unit, control unit, driving unit and communication unit are described in detail. The robot can satisfy the practical requirements of exploring the underground dark and multiintersection space. Then, the upgraded graph optimization laser SLAM algorithm and interframe matching optimization method are proposed in this paper. The Bio-RRT independent exploration method is finally proposed, which takes shorter time in equally open space and the search strategy for multiintersection space is more efficient. The experimental results demonstrate that the proposed upgrade SLAM and Bio-RRT methods can better complete the underground space exploration task.

The purpose of this paper is to design a climbing robot connected by a connecting rod mechanism to achieve multi-functional tasks such as obstacles crossing and climbing of power transmission towers.

A connecting rod type gripper has been designed to achieve stable grasping of angle steel. Before grasping, use coordination between structures to achieve stable docking and grasping. By using the alternating movements of two claws and the middle climbing mechanism, the climbing and obstacle crossing of the angle steel were achieved.

Through a simple linkage mechanism, a climbing robot has been designed, greatly reducing the overall mass of the robot. It can also carry a load of 1 kg, and the climbing mechanism can perform stable climbing. The maximum step distance of the climbing robot is 543 mm, which can achieve the crossing of angle steel obstacles.

A transmission tower climbing mechanism was proposed by analyzing the working environment. Through the locking ability of the screw nut, stable clamping of the angle steel is achieved, and a pitch mechanism is designed to adjust the posture of the hand claw.

This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations.

A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy.

The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems.

This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.

The purpose of this paper is to present a wall-climbing robot platform for heavy-load with negative pressure adsorption, which could be equipped with a six-degree of freedom (DOF) collaborative robot (Cobot) and detection device for inspecting the overwater part of concrete bridge towers/piers for large bridges.

By analyzing the shortcomings of existing wall-climbing robots in detecting concrete structures, a wall-climbing mobile manipulator (WCMM), which could be compatible with various detection devices, is proposed for detecting the concrete towers/piers of the Hong Kong-Zhuhai-Macao Bridge. The factors affecting the load capacity are obtained by analyzing the antislip and antioverturning conditions of the wall-climbing robot platform on the wall surface. Design strategies for each part of the structure of the wall-climbing robot are provided based on the influencing factors. By deriving the equivalent adsorption force equation, analyzed the influencing factors of equivalent adsorption force and provided schemes that could enhance the load capacity of the wall-climbing robot.

The adsorption test verifies the maximum negative pressure that the fan module could provide to the adsorption chamber. The load capacity test verifies it is feasible to achieve the expected bearing requirements of the wall-climbing robot. The motion tests prove that the developed climbing robot vehicle could move freely on the surface of the concrete structure after being equipped with a six-DOF Cobot.

The development of the heavy-load wall-climbing robot enables the Cobot to be installed and equipped on the wall-climbing robot, forming the WCMM, making them compatible with carrying various devices and expanding the application of the wall-climbing robot.

A heavy-load wall-climbing robot using negative pressure adsorption has been developed. The wall-climbing robot platform could carry a six-DOF Cobot, making it compatible with various detection devices for the inspection of concrete structures of large bridges. The WCMM could be expanded to detect the concretes with similar structures. The research and development process of the heavy-load wall-climbing robot could inspire the design of other negative-pressure wall-climbing robots.

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

Drawing on the cognitive–affective–conative framework, this study aims to develop a model of service robot acceptance in the hospitality sector by incorporating both cognitive evaluations and affective responses.

A mixed-method approach combining qualitative and quantitative methods was used to develop measurement and test research hypotheses.

The results show that five cognitive evaluations (i.e. cuteness, coolness, courtesy, utility and autonomy) significantly influence consumers’ positive affect, leading to customer acceptance intention. Four cognitive evaluations (cuteness, interactivity, courtesy and utility) significantly influence consumers’ negative affect, which in turn positively affects consumer acceptance intention.

This study provides significant implications for the design and implementation of service robots in the hospitality and tourism sector.

Different from traditional technology acceptance models, this study proposed a model based on the hierarchical relationships of cognition, affect and conation to enhance knowledge about human–robot interactions.

This study aims to propose a research model integrating technology acceptance model 3 (TAM3) constructs and human aspects of humanoid service robots (HSRs), measured by the Godspeed questionnaire series and tested across two hotel properties in Japan and the USA.

Potential participants were approached randomly by email invitation. A final sample size of 395 across two hotels, one in Japan and the other in the USA, was obtained, and the data were analysed using structural equation modelling.

The results confirm that perceived usefulness, driven by subjective norms and output quality, and perceived ease of use, driven by perceived enjoyment and absence of anxiety, are the immediate direct determinants of users’ re-patronage intentions for HSRs. Results also showed that users prefer anthropomorphism, perceived intelligence and the safety of an HSR for reusing it.

The findings have practical implications for the hospitality industry, suggesting multiple attributes of an HSRs that managers need to consider before deploying them in their properties.

The current study proposes an integrated model determining factors that affect the re-patronage of HSRs in hotels.

The purpose of this study is to evaluate service robots as an alternative service provider that can reduce customers’ social discomfort in hospitality service encounters. Specifically, the authors discuss when and in what scenarios service robots can alleviate such social discomfort and explain this effect from the perspective of dehumanization.

Following a social constructivist paradigm, the authors adopt a qualitative research design, gathering data through 21 semistructured interviews to explore why the presence of service employees causes customers’ social discomfort in hospitality service encounters and how service robots alleviate such discomfort.

This study’s results suggest that both the active and passive engagement of service employees are sources of customers’ social discomfort in hospitality service encounters; thus, adopting service robots can help reduce such discomfort in some scenarios. Customers’ differentiating behaviors, a downstream effect of social discomfort, are also addressed.

Service robots can reduce customers’ social discomfort in certain scenarios and influence their consumption behaviors. This finding offers actionable insights regarding the adoption of service robots in hospitality service encounters.

This research enhances the understanding of social discomfort in hospitality service encounters and expands the research on service robots. To the best of the authors’ knowledge, it is the first attempt to reveal the bright side of robots in service encounters from a dehumanization perspective.

With the trend of adopting and studying artificial intelligence (AI) service robots at restaurants, the authors’ understanding of how customers perceive robots differently across restaurant segments remains limited. Therefore, building upon expectancy theory, this study aims to propose a trust-based mechanism to explain customers’ support for AI-based service robots.

For cross-segment validation, data were collected from online survey participants under the scenarios of experiencing AI service robots in luxury (n = 428), fine-dining (n = 420), casual (n = 409) and quick-service (n = 410) restaurant scenarios.

In all four segments, trust in technology increased willingness to accept AI service robots, which was then positively related to customers’ support for AI-based service robots. Meanwhile, customers’ AI performance expectancy mediated the relationship between trust in technology and willingness to accept AI service robots. On the other hand, at luxury, fine-dining and casual restaurants, males perceived a stronger positive relationship between trust in technology and AI performance expectancy. No generational differences were found in the four restaurant segments between trust in technology and AI performance expectancy.

To the best of the authors’ knowledge, this study is one of the first attempts in hospitality research to examine cross-segment validation of customers’ responses to AI-based service robots in the luxury, fine-dining, casual and quick-service restaurant segments.