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With the popularity of high-rise buildings, wall inspection and cleaning are becoming more difficult and associated with danger. The best solution is to replace manual work with wall-climbing robots. Therefore, this paper proposes a design method for a rolling-adsorption wall-climbing robot (RWCR) based on vacuum negative pressure adsorption of the crawler. It can improve the operation efficiency while solving the safety problems.

The pulleys and tracks are used to form a dynamic sealing chamber to improve the dynamic adsorption effect and motion flexibility of the RWCR. The mapping relationship between the critical minimum adsorption force required for RWCR downward slip, longitudinal tipping and lateral overturning conditions for tipping and the wall inclination angle is calculated using the ultimate force method. The pressure and gas flow rate distribution of the negative pressure chamber under different slit heights of the negative pressure mechanism is analysed by the fluid dynamics software to derive the minimum negative pressure value that the fan needs to provide.

Simulation and test results show that the load capacity of the RWCR can reach up to 6.2 kg on the smooth glass wall, and the maximum load in the case of lateral movement is 4.2 kg, which verifies the rationality and effectiveness of the design.

This paper presents a new design method of a RWCR for different rough wall surfaces and analyses the ultimate force state and hydrodynamic characteristics.

This paper aims to propose a bilateral robotic system for lower extremity hemiparesis rehabilitation. The hemiplegic patients can complete rehabilitation exercise voluntarily with the assistance of the robot. The reinforcement learning is included in the robot control system, enhancing the muscle activation of the impaired limbs (ILs) efficiently with ensuring the patients’ safety.

A bilateral leader–follower robotic system is constructed for lower extremity hemiparesis rehabilitation, where the leader robot interacts with the healthy limb (HL) and the follow robot is worn by the IL. The therapeutic training is transferred from the HL to the IL with the assistance of the robot, and the IL follows the motion trajectory prescribed by the HL, which is called the mirror therapy. The model reference adaptive impedance control is used for the leader robot, and the reinforcement learning controller is designed for the follower robot. The reinforcement learning aims to increase the muscle activation of the IL and ensure that its motion can be mastered by the HL for safety. An asynchronous algorithm is designed by improving experience relay to run in parallel on multiple robotic platforms to reduce learning time.

Through clinical tests, the lower extremity hemiplegic patients can rehabilitate with high efficiency using the robotic system. Also, the proposed scheme outperforms other state-of-the-art methods in tracking performance, muscle activation, learning efficiency and rehabilitation efficacy.

Using the aimed robotic system, the lower extremity hemiplegic patients with different movement abilities can obtain better rehabilitation efficacy.

This paper aims to develop a robotic mirror therapy system for lower limb rehabilitation, which is applicable for different patients with individual movement disability levels.

This paper puts forward a novel system that includes a four-degree-of-freedom sitting/lying lower limb rehabilitation robot and a wireless motion data acquisition system based on mirror therapy principle. The magnetorheological (MR) actuators are designed and manufactured, whose characteristics are detected theoretically and experimentally. The passive training control strategy is proposed, and the trajectory tracking experiments verify its feasibility. Also, the active training controller that is adapt to the human motor ability is designed and evaluated by the comparison experiments.

The MR actuators produce continuously variable and compliant torque for robotic joints by adjusting excitation current. The reference limb joint position data collected by the wireless motion data acquisition system can be used as the motion trajectory of the robot to drive the affected limb. The passive training strategy based on proportional-integral control proves to have great trajectory tracking performance through experiments. In the active training mode, by comparing the real-time parameters adjustment in two phases, it is certified that the proposed fuzzy-based regulated impedance controller can adjust assistance torque according to the motor ability of the affected limb.

The system developed in this paper fulfills the needs of robot-assisted mirror therapy for hemiplegic patients.

Social manufacturing has emerged. It aims to integrate the manufacturing resources of micro- and small-scale manufacturing enterprises (MSMEs) and help MSMEs cope with the dynamic, service-oriented and personalized market demands. In social manufacturing, MSMEs cooperate with each other through manufacturing resource sharing. However, because MSMEs are distributed and decentralized, the efficiency of establishing reliable cooperation between MSMEs is relatively low. Therefore, this paper presents a blockchain-driven cyber-credit evaluation system (BCCES) to implement distributed cyber-credit evaluation. BCCES can provide reliable cyber-credit for distributed MSMEs without the trusted third party. This can improve the efficiency of establishing reliable cooperation among unauthentic MSMEs.

The paper proposes a BCCES to evaluate MSMEs' cyber-credit in decentralized environment. In BCCES, a cyber-credit evaluation model is proposed by improving set pair analysis (SPA) method, and cyber-credit smart contract and distributed consensus mechanism are designed according to the runtime logic of distributed cyber-credit evaluation.

The results confirmed that BCCES is feasible and effective to implement cyber-credit evaluation without the trusted third party. With the advantages of blockchain, BCCES can automatically realize cyber-credit evaluation through smart contract and distributed consensus. At the same time, BCCES can evaluate the real-time cyber-credit of MSMEs based on their latest service evaluation. In addition, we can design corresponding smart contracts according to actual requirements, which makes blockchain applicable to different distributed scenarios.

The paper combines blockchain and SPA to implement cyber-credit evaluation in social manufacturing and provides a new feasible idea for cyber-credit evaluation without the trusted third party. This can also provide MSMEs a reference of applying blockchain to other distributed scenarios through combining smart contract and different algorithms.

This paper aims to present an intelligent motion attitude control algorithm, which is used to solve the poor precision problems of motion-manipulation control and the problems of motion balance of humanoid robots. Aiming at the problems of a few physical training samples and low efficiency, this paper proposes an offline pre-training of the attitude controller using the identification model as a priori knowledge of online training in the real physical environment.

The deep reinforcement learning (DRL) of continuous motion and continuous state space is applied to motion attitude control of humanoid robots and the robot motion intelligent attitude controller is constructed. Combined with the stability analysis of the training process and control process, the stability constraints of the training process and control process are established and the correctness of the constraints is demonstrated in the experiment.

Comparing with the proportion integration differentiation (PID) controller, PID + MPC controller and MPC + DOB controller in the humanoid robots environment transition walking experiment, the standard deviation of the tracking error of robots’ upper body pitch attitude trajectory under the control of the intelligent attitude controller is reduced by 60.37 per cent, 44.17 per cent and 26.58 per cent.

Using an intelligent motion attitude control algorithm to deal with the strong coupling nonlinear problem in biped robots walking can simplify the control process. The offline pre-training of the attitude controller using the identification model as a priori knowledge of online training in the real physical environment makes up the problems of a few physical training samples and low efficiency. The result of using the theory described in this paper shows the performance of the motion-manipulation control precision and motion balance of humanoid robots and provides some inspiration for the application of using DRL in biped robots walking attitude control.

The purpose of this paper is to extend the usage of stroke gestures in manipulation tasks to make the interaction between human and robot more efficient.

In this paper, a set of stroke gestures is designed for typical manipulation tasks. A gesture recognition and parameter extraction system is proposed to exploit the information in stroke gestures drawn by the users.

The results show that the designed gesture recognition subsystem can reach a recognition accuracy of 99.00 per cent. The parameter extraction subsystem can successfully extract parameters needed for typical manipulation tasks with a success rate about 86.30 per cent. The system shows an acceptable performance in the experiments.

Using stroke gesture in manipulation tasks can make the transmission of human intentions to the robots more efficient. The proposed gesture recognition subsystem is based on convolutional neural network which is robust to different input. The parameter extraction subsystem can extract the spatial information encoded in stroke gestures.

The author designs stroke gestures for manipulation tasks which is an extension of the usage of stroke gestures. The proposed gesture recognition and parameter extraction system can make use of stroke gestures to get the type of the task and important parameters for the task simultaneously.

The purpose of this paper is to study the behavior of a single ridge passing through elastohydrodynamic lubrication of point contacts problem for different ridge shapes and sizes, including flat-top, triangular and cosine wave pattern to get an optimal ridge profile.

The time-dependent Reynolds’ equation is solved using Newton–Raphson technique. Several shapes of surface feature are simulated and the film thickness and pressure distribution are obtained at every time step by simultaneous solution of the Reynolds’ equation and film thickness equation, including elastic deformation. Film thickness and pressure distribution are chosen to be the criteria in the comparisons.

The geometrical characteristics of the ridge play an important role in the formation of lubricant film thickness profile and the pressure distribution through the contact zone. To minimize wear, friction and fatigue life, an optimal ridge profile should have smooth shape with small ridge size. Obtained results are compared with other published numerical results and show a good agreement.

The study evaluates the performance of different surface features of a single ridge with different shapes and sizes passing through elastohydrodynamic of point contact problem in relation to film thickness and pressure profile.

This paper aims to examine how the law may play a role in mediation by paying special attention to how the law is excluded from and included in the process of court mediation in China.

Hundred model court mediation cases selected by the Supreme People’s Court of China were analysed and reviewed.

The law is relevant in Chinese-style court mediation in four ways: first, judge-mediators are intended to use mediation to avoid resolving legal difficulties or challenges; second, judge-mediators consult the law to anticipate the losing party and the potential negative effects that might result from the adjudication; third, judge-mediators refer to the law to propose a mediation scheme or plan to guide the parties to settle; and fourth, judge-mediators would use the law as a bargaining chip in various ways to induce the parties to settle.

Standards should be set out for the use of law in the mediation process to standardise judge-mediators’ actions, to ensure that the law is not used coercively to push settlement, which would undermine the parties’ self-determination in mediation.

This paper provides an original understanding of how law affects the process, the outcomes and, ultimately, the nature of settlements that parties achieve through court mediation in China. This study contributes to the literature that argues that ethical norms and legal standards should be set to direct those legal evaluations.

The purpose of this paper is to use personal fuzzy demand, assisted by system computing to find a job, using job search systems to achieve this goal.

The search system uses the fuzzy goal programming (FGP) method by setting personal preferences as property values and screening the data for comparison and calculation. By presenting information sorted by the inputted property values, the methodology suggests the best job options.

FGP algorithms make job-searching systems meet the needs of users better, which can really affect jobseekers’ approaches to pursuing work.

As it has only considered the local cultural environment, this paper’s findings are limited by being confined to Taiwanese samples.

The experimental results of the proposed method have been compared with other websites to show their effectiveness.

This paper has assisted personal decision making using FGP applied to the internet which has seldom been studied previously.

In recent years, deep learning and extended reality (XR) technologies have gained popularity in the built environment, especially in construction engineering and management. A significant amount of research efforts has been thus dedicated to the automation of construction-related activities and visualization of the construction process. The purpose of this study is to investigate potential research opportunities in the integration of deep learning and XR technologies in construction engineering and management.

This study presents a literature review of 164 research articles published in Scopus from 2006 to 2021, based on strict data acquisition criteria. A mixed review method, consisting of a scientometric analysis and systematic review, is conducted in this study to identify research gaps and propose future research directions.

The proposed research directions can be categorized into four areas, including realism of training simulations; integration of visual and audio-based classification; automated hazard detection in head-mounted displays (HMDs); and context awareness in HMDs.

This study contributes to the body of knowledge by identifying the necessity of integrating deep learning and XR technologies in facilitating the construction engineering and management process.