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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.

In large equipment and highly complex confined workspaces, the maintenance is usually carried out by snake-arm robots with equal cross-sections. However, the equal cross-sectional design results in the snake arm suffering from stress concentration and restricted working space. The purpose of this paper is to design a variable cross-section elephant trunk robot (ETR) that can address these shortcomings through bionic principles.

This paper proposes a cable-driven ETR to explore the advantages and inspiration of variable cross-section features for hyper-redundant robot design. For the kinematic characteristics, the influence of the variable cross-section design on the maximum joint angle of the ETR is analysed using the control variables method and the structural parameters are selected. Based on the biological inspiration of the whole elephant trunk following the movement of the trunk tip, a trajectory-tracking algorithm is designed to solve the inverse kinematics of the ETR.

Simulation and test results show the unique advantages of the proposed variable cross-section ETR in kinematics and forces, which can reduce stress concentrations and increase the flexibility of movement.

This paper presents a design method for a variable cross-section ETR for confined working spaces, analyses the kinematic characteristics and develops a targeted trajectory control algorithm.

Real-time monitoring of the critical physical fields of core components in complex equipment is of great significance as it can predict potential failures, provide reasonable preventive maintenance strategies and thereby ensure the service performance of the equipment. This research aims to propose a hierarchical explicit–implicit combined sensing-based real-time monitoring method to achieve the sensing of critical physical field information of core components in complex equipment.

Sensor deployable and non-deployable areas are divided based on the dynamic and static constraints in actual service. An integrated method of measurement point layout and performance evaluation is used to optimize sensor placement, and an association mapping between information in non-deployable and deployable areas is established, achieving hierarchical explicit–implicit combined sensing of key sensor information for core components. Finally, the critical physical fields of core components are reconstructed and visualized.

The proposed method is applied to the spindle system of CNC machine tools, and the result shows that this method can effectively monitor the spindle system temperature field.

This research provides an effective method for monitoring the service performance of complex equipment, especially considering the dynamic and static constraints during the service process and detecting critical information in non-deployable areas.

As complex analysis of contact models is required in the traditional assembly strategy, it is still a challenge for a robot to complete the multiple peg-in-hole assembly tasks autonomously. This paper aims to enable the robot to complete the assembly tasks autonomously and more efficiently, with the strategies learned by reinforcement learning (RL), a learning-accelerated deep deterministic policy gradient (LADDPG) algorithm is proposed.

The multiple peg-in-hole assembly strategy is designed in two modules: an advanced planning module and a bottom control module. The advanced module is completed by the LADDPG agent, which is used to derive advanced commands based on geometric and environmental constraints, that is, the desired contact force. The bottom-level control module will drive the robot to complete the compliant assembly task through the adaptive impedance algorithm according to the command set issued by the advanced module. In addition, a set of safety assurance mechanisms is developed to safely train a collaborative robot to complete autonomous learning.

The method can complete the assembly tasks well through RL, and it can realize satisfactory compliance of the robot to the environment. Compared with the original DDPG algorithm, the average values of the instantaneous maximum contact force and contact torque during the assembly process are reduced by approximately 38% and 74%, respectively.

The entire algorithm can also be applied to other robots and the assembly strategy can be applied in the field of the automatic assembly.

A compliant assembly strategy based on the LADDPG algorithm is proposed to complete the automated multiple peg-in-hole assembly tasks.

The purpose of this paper is to accurately obtain the deformation of a hybrid robot and rapidly enable real-time compensation in friction stir welding (FSW). In this paper, a prediction algorithm based on the back-propagation neural network (BPNN) optimized by the adaptive genetic algorithm (GA) is presented.

Via the algorithm, the deformations of a five-degree-of-freedom (5-DOF) hybrid robot TriMule800 at a limited number of positions are taken as the training set. The current position of the robot and the axial force it is subjected to are used as the input; the deformation of the robot is taken as the output to construct a BPNN; and an adaptive GA is adopted to optimize the weights and thresholds of the BPNN.

This algorithm can quickly predict the deformation of a robot at any point in the workspace. In this study, a force-deformation experiment bench is built, and the experiment proves that the correspondence between the simulated and actual deformations is as high as 98%; therefore, the simulation data can be used as the actual deformation. Finally, 40 sets of data are taken as examples for the prediction, the errors of predicted and simulated deformations are calculated and the accuracy of the prediction algorithm is verified.

The entire algorithm is verified by the laboratory-developed 5-DOF hybrid robot, and it can be applied to other hybrid robots as well.

Robots have been widely used in FSW. Traditional series robots cannot bear the large axial force during welding, and the deformation of the robot will affect the machining quality. In some research studies, hybrid robots have been used in FSW. However, the deformation of a hybrid robot in thick-plate welding applications cannot be ignored. Presently, there is no research on the deformation of hybrid robots in FSW, let alone the analysis and prediction of their deformation. This research provides a feasible methodology for analysing the deformation and compensation of hybrid robots in FSW. This makes it possible to calculate the deformation of the hybrid robot in FSW without external sensors.

With the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers’ economic models to help drive online sales. Initially, the purpose of this paper is to undertake an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this paper reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps and eventually propose new research solutions to benefit apparel retailing market.

This study utilizes empirical research drawing on 130 academic publications indexed from online databases. The authors introduce a three-layer descriptor for searching articles, and analyse retrieval results via distribution graphics of years, publications and keywords.

This study classified high-tech integrated apparel systems into 3D CAD systems, personalised design systems and recommendation systems. The authors’ research interest is focussed on recommendation system. Four types of models were found, namely clothes searching/retrieval, wardrobe recommendation, fashion coordination and intelligent recommendation systems. The forth type, smart systems, has raised more awareness in apparel research as it is equipped with advanced functions and application scenarios to satisfy customers. Despite various computational algorithms tested in system modelling, existing research is lacking in terms of apparel and users profiles research. Thus, from the review, the authors have identified and proposed a more complete set of key features for describing both apparel and users profiles in a recommendation system.

Based on previous studies, this is the first review paper on this topic in this subject field. The summarised work and the proposed new research will inspire future researchers with various knowledge backgrounds, especially, from a design perspective.

The Indian hospitality and tourism industries, major economic growth drivers and employment generators, have been greatly affected by the outbreak of the COVID-19 pandemic. In FY 2020, the Indian tourism sector created 39 million jobs and contributed nearly US$194.3bn, or 6.8%, to India’s gross domestic product. The purpose of this study is to focus on ranking 22 listed hotels and 9 listed travel agencies in India based on their performance across 14 selected financial parameters in both the pre-COVID-19 year ending in March 2019 and the post-COVID-19 year ending in March 2021 to understand how the pandemic affected their businesses.

This research proposes to analyze the impact of the COVID-19 pandemic on the financial performance of 22 listed Indian hotels and 9 listed travel agencies evaluated over 14 financial parameters using a pipeline of two recently developed multicriteria decision-making techniques, method based on the removal effects of criteria (MEREC) and grey-based combined compromised solution (CoCoSo). First, the criteria weights are objectively determined using MEREC, and then the financial performances of the selected companies in both the hospitality and tourism industries are separately assessed using CoCoSo to get their overall performance score, based on which the companies are ranked in order of preference.

It was observed that Westlife Development, Lemon Tree Hotels, Indian Tourism Development Corporation, Royal Orchid and Country Club performed significantly poorer than their peers in the aftermath of the pandemic, whereas EIH, Advani Hotels and Resorts and TGB Banquets performed relatively better. Travel agencies Easy Trip and International Travel House performed particularly poorly because of the pandemic, but VMV Holidays performed relatively better in FY 2021.

The findings of the analysis will aid portfolio construction, corporate investment decisions, competition research, government policymaking and industrial analysis.

The proposed model is novel because it fills the research gap in the application of the integrated MEREC–CoCoSo method to study the impact of COVID-19 on the hospitality and tourism sectors in India.

In the era of the digital economy, higher demands are placed on versatile talents, and the cultivation of students with innovative and entrepreneurial abilities has become an important issue for the further development of higher education, thus leading to extensive and in-depth research by many scholars. The study summarizes the characteristics and patterns of dual-innovation education at different stages of development, hoping to provide a systematic model for the development of dual-innovation education in China and make up for the shortcomings.

This paper uses Citespace software to visualize and analyze the relevant literature in CNKI and Web of Science databases from a bibliometric perspective, focusing on quantitative analysis in terms of article trends, topic clustering, keyword co-linear networks and topic time evolution, etc., to summarize and sort out the development of innovation and entrepreneurship education research at home and abroad.

The study found that the external characteristics of the literature published in the field of bi-innovation education in China and abroad are slightly different, mainly in that foreign publishers are more closely connected and have formed a more stable ecosystem. In terms of research hotspots, China is still in a critical period of reforming its curriculum and teaching model, and research on the integration of specialization and creative education is in full swing, while foreign countries focus more on the cultivation of students' entrepreneurial awareness and the enhancement of individual effectiveness. In terms of cutting-edge analysis, the main research directions in China are “creative education”, “new engineering”, “integration of industry and education” and “rural revitalization”.

Innovation and entrepreneurship education in China is still in its infancy, and most of the studies lack an overall overview and comparison of foreign studies. Based on the econometric analysis of domestic and foreign literature, this paper proposes a path for domestic innovation and entrepreneurship education reform that can make China's future education reform more effective.