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The purpose of this paper is to design a new type of magnetic suction wall-climbing robot suitable for the wall inspection of wind turbine towers to solve the problems in manual maintenance tasks.

By analyzing the shortcomings of existing wall-climbing robots, a magnetic suction integrated wheel structure is designed to effectively combine the adsorption structure and transmission structure. To enable the robot to adapt to the curvature of the wall surface of a wind turbine tower, a passive adaptive curvature structure is designed. The effects of the air gap, the thickness of the wheel plates on both sides, the size of permanent magnets and the size of aluminum rings on the adsorption force are studied. Through mechanical model analysis under different instability conditions, the magnetic circuit of the magnetic wheel is optimized and designed.

Applying the wall-climbing robot to engineering practice, experiments have shown that the developed wall-climbing robot can move safely and stably on the wall of the wind turbine tower. The robot can also carry a load of 20 kg, and the designed adaptive structure can cause the magnetic wheel to deflect up to 20° relative to the vehicle body, fully meeting the curvature requirements of the minimum diameter end of the wind turbine tower.

This paper proposes a magnetic suction integrated wheel structure through analysis of the working environment. And the parameters affecting the magnetic wheel adsorption performance were optimized. Meanwhile, a passive adaptive wind turbine tower curvature structure was proposed.

Continuum robots offer unique advantages in various specialized environments, particularly in confined or hard-to-reach spaces. Inverse kinematics and real-time shape estimation constitute crucial aspects of closed-loop control for continuum robots, presenting challenging problems. This paper aims to present an inverse kinematics and shape reconstruction method, which relies solely on the knowledge of base and end positions and orientations.

Based on the constant curvature assumption, continuum robots are regarded as spatial curves composed of circular arcs. Using geometric relationships, the mathematical relationships between the arc chords, points on the bisecting plane and the coordinate axes are established. On this basis, the analytical solution of the inverse kinematics of the continuum robots is derived. Using the positions and orientations of the base and end of the continuum robots, the Levenberg–Marquardt algorithm is used to solve the positions of the cubic Bezier curves, and a new method of spatial shape reconstruction of continuum robots is proposed.

The inverse kinematics and spatial shape reconstruction simulation of the continuum robot are carried out, and the spatial shape measurement experimental platform for the continuum robot is constructed to compare the measured and reconstructed spatial shapes. The results show that the maximum relative error between the actual shape and the reconstructed shape of the continuum robot is 2.08%, which verifies the inverse kinematics and shape reconstruction model. Additionally, when the bending angle of a single bending section of the continuum robot is less than 135°, the shape reconstruction accuracy is higher.

The proposed inverse kinematics solution method avoids iterative solving, and the shape reconstruction model does not rely on mechanical models. It has the advantages of being simple to solve, highly accurate and fast in computation, making it suitable for real-time control of continuum robots.

This paper aims to solve the problem of uncertain position and attitude between unstructured terrain robot and grasped target and insufficient control accuracy in extreme environment, a grasping mechanism based on attraction domain relationship is proposed, which can realize autonomous positioning, capturing and grasping of robot under low control accuracy.

The grasping mechanism was designed, taking inspiration from fishing behavior this mechanism introduces attraction domains and flexible-elastic structures through the active and passive ends to achieve automatic positioning and capture. After the capture is completed, the grasping mechanism connects the active end and the passive end, simultaneously relying on the gravity of the target object to achieve locking and release between the robot and the target object. This paper adopts theoretical, simulation and experimental verification methods to conduct theoretical and simulation analysis on the autonomous positioning and grasping process of the mechanism, and produces grasping experimental prototypes with different positions and postures.

The experiment shows that the gripping mechanism designed in this paper can achieve automatic positioning capture and gripping of large deviation situations under low control accuracy, with a displacement deviation of up to 10 mm (about 1/6 diameter of the end of the mechanism) and an angle deviation of up to 3°. The scientific research task in the extremely high altitude environment has finally been successfully accomplished.

Inspired by fishing behavior, this paper proposes a positioning, capturing and grasping mechanism. The attraction area built with permanent magnets, coupled with the flexible connection, enables precise capture under low control, while the grasping mechanism can also rely on gravity to self-lock and release.

The purpose of this paper is that the modular mobile robots reformed the multimachine joint mode to achieve obstacle-crossing, climbing and other multifunctional inspection in unstructured environment under the connection of the cone–hole docking mechanism.

An arc-shaped docking cone head with a posture-maintaining spring and two arc-shaped connecting rods that formed a ring round hole were designed to achieve large tolerance docking. Before active locking, the coordination between structures was used to achieve passive locking, which mitigated the docking impact of modular robots in unstructured environment. Using the locking ring composed of the two arc-shaped connecting rods, open-loop and closed-loop motion characteristics were obtained through the mutual motion of the connecting rod and the sliding block to achieve active locking, which not only ensured high precision docking, but also achieved super docking stability.

The cone–hole docking mechanism had the docking tolerance performance of position deviation of 6mm and pitch deviation of 8° to achieve docking of six degrees of freedom (6-DOF), which had a load capacity of 230 N to achieve super docking stability. Under the connection of the cone–hole docking mechanism, the modular mobile robots reformed the multimachine joint mode to achieve obstacle-crossing, climbing and other multifunctional inspection in unstructured environment.

Based on mechanical analysis of universal models, a cone–hole docking mechanism combining active and passive functions, six-dimensional constraints could be implemented, was proposed in this paper. The characteristics of the posture-maintaining spring in the cone docking head and the compression spring at the two ends of two arc-shaped connecting rods were used to achieve docking with large tolerance. Passive locking and active locking modules were designed, mitigating impact load and the locking did not require power to maintain, which not only ensured high precision docking, but also achieved super docking stability.

In view of the unknown environmental parameters and uncertain interference during gripping by the manipulator, it is difficult to obtain an effective gripping force with the traditional impedance control method. To avoid this dilemma, the purpose of this study is to propose an adaptive control strategy based on an adaptive neural network and a PID search optimization algorithm for unknown environments.

The method is based on a variable impedance model, and a new impedance model is established using a radial basis function (RBF) neural network to estimate unknown parameters of the impedance model. The approximation errors of the adaptive neural network and the uncertain disturbance are effectively suppressed by designing the adaptive rate. In the meantime, auxiliary variables are constructed for Lyapunov stability analysis and adaptive controller design, and PSA is used to ensure the stability of the adaptive impedance control system. Based on the Lyapunov stability criterion, the adaptive im-pedance control system is proved to have progressive tracking convergence property.

Through comparative simulations and experiments, the superiority of the proposed adaptive control strategy in position and force tracking has been verified. For objects with low flexibility and light-weight (such as a coke, a banana and a nectarine), this control method demonstrates errors of less than 10%.

This paper uses RBF neural networks to estimate unknown parameters of the impedance model in real-time, enhancing system adaptability. Neural network weights are updated online to suppress errors and disturbances. Auxiliary variables are designed for Lyapunov stability analysis. The PSA algorithm is used to adjust controller parameters in real-time. Additionally, comparative simulations and experi-ments are designed to analyze and validate the performance of controller.

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 the complex and unpredictable terrain environment of the Qinghai-Tibet Plateau scientific research station, such as cement road, wetland, gravel desert, snowfield, ice surface, grassland, slimy ground, steep slope, step, etc., a reconfigurable walking mechanism based on two movement modes of wheel and triangular crawler was proposed.

By analyzing the deformation mechanism of the walking mechanism, a reconfigurable wheel-crawler-integrated walking mechanism and the configuration scheme are designed. The analysis of the kinematics and mechanical properties of the swing arm system and the deformation mechanism of the walking mechanism.

The reconfigurable wheel-crawler-integrated walking mechanism can be switched between the wheel and triangular crawler modes by driving the deformation mechanism. Through the numerical simulation of its movement process, and the trial production and experiment of the prototype, indicates the validity of the reconfigurable wheel-crawler-integrated walking mechanism design.

The work of this paper provides a reconfigurable wheel-crawler-integrated-walking mechanism, which can be used by robots in the Qinghai-Tibet Plateau scientific research station. It has excellent reconfigurability and can effectively improve the robot’s adaptability to complex terrain.

The purpose of this paper is to introduce the design and the multi‐mode locomotion function of the new reconfigurable modular robotic system – UBot system – which combines the advantages from the chain‐based and lattice‐based self‐reconfigurable robots.

The UBot modules the authors have designed are based on the universal joint and of cubic shape with two rotational joints and reliable automatic connecting mechanism. The modules are compact and flexible enough for locomotion and reconfiguration. The system can move in different modes to satisfy different terrains, through changing the modules' local connections and rotation of modules' joints.

The UBot system can flexibly move in the modes of cross, loop, quadruped, snake‐type and other type of locomotion modes. All the locomotion has been implemented in the physical experiments.

The UBot module is the new reconfigurable module which has two joints in one unit of regular cubic space and four reliable automatic connecting surfaces. A group of the modules is able to change its connective configuration by changing their local connections and has functionality of the corresponding traditional robotic system. Since it can travel through terrains that may not be fully characterized ahead of time, the system can be used in a large variety of tasks, such as transportation, assembly, inspection and exploration.

The purpose of this study is to investigate the relationship between tourism development and urban housing prices in Chinese cities. Specifically, the study aimed to explore whether there is a relationship between the two variables in tourist and non-tourist cities and whether there is a non-linear relationship between them.

In this study, the entropy method was used to construct the China City Tourism Development Index, which provides a more comprehensive measure of the level of tourism development in different cities. In total, 45 major cities in China were studied using the panel data approach for the period of 2011 to 2019.

The empirical analysis conducted for this study found that tourism development affects urban house prices, and that there is an inverted U-shaped relationship. However, this varies across cities, with house prices in tourist cities tending to be more influenced by tourism development than non-tourist cities. Also, foreign direct investment, population size, fixed asset investment and disposable income per capita were found to have an impact on house prices in both tourism and non-tourism cities.

There are significant differences in tourism development and urban house prices in different cities in China. This study considers these differences when examining the impact of tourism on house prices in 45 major cities in China by dividing the sample cities into tourist and non-tourist cities.

By adopting learning theory and a guanxi perspective, this study aims to investigate the effects of interpersonal guanxi (interpersonal networks or connections) and relationship learning on companies’ business performance when operating in a large emerging market.

Using a sample of 294 sales managers and salespeople in the Chinese hotel sector, the authors empirically test the authors' arguments through a structural equation modeling (SEM) approach.

The authors' findings indicate that strong interpersonal guanxi tends to generate more positive business performance. Furthermore, the authors find that relationship learning plays a mediating role in the association between interpersonal guanxi and hotel companies’ business performance in a Chinese context. Finally, the authors empirically explore the moderating effect of inter-firm dependence on the contribution of interpersonal guanxi to relationship learning. Findings demonstrate that this effect varies significantly based on inter-firm dependence, with interpersonal guanxi exhibiting a greater positive impact if such dependence is high.

This study enriches our understanding of interpersonal guanxi and of how companies can enhance the companies' business performance in an emerging market context.