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To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles.

The system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method.

The obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage.

This paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.

The purpose of this paper is to examine the role of education in the labor market and to understand how returns to education change over time in rural China.

Using nationally representative survey data from 2004 to 2015, this study provides insights on wage determination in the labor market and examines how the returns to education in rural China differ with time and educational endowment. This study applies ordinary least squares estimation and the Heckman selection model to estimate the returns to education.

The returns to education decreased during the observed years from more than 6 percent in 2004 to only about 3 percent in 2011, rising to nearly 4 percent in 2015. The overall trend is robust and observed within groups defined by education. Additionally, the returns to education vary greatly with educational endowment. Tertiary education has always maintained a high rate of returns at nearly 10 percent, while returns to senior high school education and below have gradually diminished.

The authors believe that the results will not only enrich studies on the returns to education in rural China, but also provide a basis for diagnosing the changes of rural labor market in the early twenty-first century.

Unlike previous research that primarily utilized structural equation modelling (SEM) to evaluate safety hazards in subway projects, this research aims to utilize a hybrid approach to investigate and scrutinize the key indicators of safety hazards leading to accidents, thereby hindering the progress of subway projects in China, taking into cognizance the multiple stakeholder’s perspective.

By administering a survey questionnaire to 373 highly involved stakeholders in subway projects spanning Changsha, Beijing and Qingdao, China, our approach incorporated a four-staged composite amalgamation of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), covariance-based structural equation modelling (CB-SEM) and artificial neural network (ANN) to develop an optimized model that determines the causal relationships and interactions among safety hazards in subway construction projects.

The optimized model delineated the influence of individual safety hazards on subway projects. The feasibility and applicability of the model developed was demonstrated on an actual subway project under construction in Changsha city. The outcomes revealed that the progress of subway projects is significantly influenced by risks associated with project management, environmental factors, subterranean conditions and technical hazards. In contrast, risks related to construction and human factors did not exhibit a significant impact on subway construction progress.

While our study provides valuable insights, it is important to acknowledge the limitation of relying on theoretical approaches without empirical validation from experiments or the field. In future research, we plan to address this limitation by assessing the SEM using empirical data. This will involve a comprehensive comparison of outcomes derived from CB-SEM with those obtained through SEM-ANN methods. Such an empirical validation process is crucial for enhancing the overall efficiency and robustness of the proposed methodologies.

The established hybrid model revealed complex non-linear connections among indicators in the intricate project, enabling the recognition of primary hazards and offering direction to improve management of safety in the construction of subways.

To supply temporary pressure testing devices with favorable performance for emergency environments, this paper aims to present a pressure sensor with a central boss and straight-annular grooves. The structural feature is modeled and optimized by neural network-based method, and the device prototype is fabricated by 3D printing techniques.

The study initially compares mechanical properties of the proposed structure with two conventional designs using finite element analysis. The impacts from structural dimensions on sensor performance are modeled using a Backpropagation neural network and optimized through genetic algorithms. The sensing diaphragm is fabricated using stereolithography (SLA) 3D printing, while the piezoresistors and necessary interconnects are realized with screen printing techniques.

The experimental results demonstrate that the fabricated sensor exhibits a sensitivity of 2.8866 mV/kPa and a nonlinearity of 6.81% within the pressure range of 0–100 kPa. This performance is an improvement of 118% in sensitivity and a decrease of 54% in nonlinearity compared to flat diaphragm structure, highlighting the effectiveness of proposed diaphragm configuration.

This research offers a holistic methodology that encompasses the structural design, optimization and fabrication of pressure sensors. The proposed diaphragm and corresponding modelling method can provide a practical approach to enhance the measurement capabilities of pressure sensors. By leveraging SLA printing for diaphragm and screen printing for circuit, the prototype can be produced in a timely manner.

This paper aims to present a new method to analyze the robot’s obstacle negotiation based on the terramechanics, where the terrain physical parameters, the sinkage and the slippage of the robot are taken into account, to enhance the robot’s trafficability.

In this paper, terramechanics is used in motion planning for all-terrain obstacle negotiation. First, wheel/track-terrain interaction models are established and used to analyze traction performances in different locomotion modes of the reconfigurable robot. Next, several key steps of obstacle-climbing are reanalyzed and the sinkage, the slippage and the drawbar pull are obtained by the models in these steps. In addition, an obstacle negotiation analysis method on loose soil is proposed. Finally, experiments in different locomotion modes are conducted and the results demonstrate that the model is more suitable for practical applications than the center of gravity (CoG) kinematic model.

Using the traction performance experimental platform, the relationships between the drawbar pull and the slippage in different locomotion modes are obtained, and then the traction performances are obtained. The experimental results show that the relationships obtained by the models are in good agreement with the measured. The obstacle-climbing experiments are carried out to confirm the availability of the method, and the experimental results demonstrate that the model is more suitable for practical applications than the CoG kinematic model.

Comparing with the results without considering Terramechanics, obstacle-negotiation analysis based on the proposed track-terrain interaction model considering Terramechanics is much more accurate than without considering Terramechanics.

The purpose of this paper is to examine the occupational specialization in rural labor market by analyzing the nature of part-time farming in rural China and estimating the impact of off-farm experience on the individual’s persistence and exit of part-time farming as well as its heterogeneity.

Using the panel data collected in 100 villages and 2,000 households across five provinces in 2008, 2012 and 2016, this study provides insights on the nature of part-time farming in rural labor market and find the impact and mechanism of off-farm employment experience on exiting part-time farming by adopting event history analysis.

Part-time farming is a stable long-run occupation in rural labor market of China from 2008 to 2015. Off-farm employment experience generally has positive effects on long-term part-time farming and the probability of exiting part-time farming. It significantly promotes female to exit part-time farming.

Based on the two-sector model, this study builds a conceptual framework of off-farm experience and occupational specialization and sets a theoretical basis of hazard model when using event history analysis. This study contributes to identify the impact of off-farm experience on persistence and exiting part-time farming in recent years. The empirical findings support the policy of promoting off-farm employment to improve occupational specialization.

This paper aims to present a prototype of medical transportation robot whose positioning accuracy can reach millimeter-level in terms of patient transportation. By using this kind of mobile robot, a fully automatic image diagnosis process among independent CT/PET devices and the image fusion can be achieved.

Following a short introduction, a large-load 4WD-4WS (four-wheel driving and four-wheel steering) mobile robot for carrying patient among multiple medical imaging equipments is developed. At the same time, a specially designed bedplate with self-locking function is also introduced. For further improving the positioning accuracy, the authors proposed a calibration method based on Gaussian process regression (GPR) to process the measuring data of the sensors. The performance of this robot is verified by the calibration experiment and Image fusion experiment. Finally, concluding comments are drawn.

By calibrating the robot’s positioning system through the proposed GPR method, one can obtain the accuracy of the robot’s offset distance and deflection angle, which are 0.50 mm and +0.21°, respectively. Independent repeated trials were then set up to verify this result. Subsequent phantom experiment shows the accuracy of image fusion can be accurate within 0.57 mm in the front-rear direction and 0.83 in the left-right direction, respectively, while the clinical experiment shows that the proposed robot can practically realize the transportation of patient and image fusion between multiple imaging diagnosis devices.

The proposed robot offers an economical image fusion solution for medical institutions whose imaging diagnosis system basically comprises independent MRI, CT and PET devices. Also, a fully automatic diagnosis process can be achieved so that the patient’s suffering of getting in and out of the bed and the doctor’s radiation dose can be obviated.

The general bedplate presented in Section 2 that can be mounted on the CT and PET devices and the self-locking mechanism has realized the catching and releasing motion of the patient on different medical devices. They also provide a detailed method regarding patient handling and orientation maintenance, which was hardly mentioned in previous research. By establishing the positioning system between the robot and different medical equipment, a fully automatic diagnosis process can be achieved so that the patient’s suffering of getting in and out of the bed and the doctor’s radiation dose can be obviated.

The GPR-based method proposed in this paper offers a novel method for enhancing the positioning accuracy of the industrial AGV while the transportation robot proposed in this paper also offers a solution for modern imaging fusion diagnosis, which are basically predicated on the conjoint analysis between different kinds of medical devices.

The paper aims to improve the radiation-proof capability of the self-designed mobile robot with a 7-DOF manipulator, enabling the long-playing inspection and intervention under high-dose radiation environment. In this context, gamma-ray irradiation test for electronic components and specific hardness design have also been specifically presented and discussed.

The study’s hardness design mainly focuses on shielding protection, distance protection and time protection. Irradiation test is first carried out to investigate irradiation resistance of each electronic module. Then, modular deployment and shielding calculation are completed for the point-type nuclear accidents, respectively, to achieve a robust anti-radiation design scheme. Finally, the field experiment is conducted to validate system effectiveness and good mobility, and operational practices are acquired for the realization of time protection.

Coupled with modular redeployment and shielding design, irradiation results illustrate the effectiveness of robotic anti-radiation design. Meanwhile, experiences and reformed measures from the field exercise implement efficient operation and radiological time protection.

Considering the huge risks of high-dose source exposure, the radiation-resistance of the overall system cannot be verified in the field experiment. Fortunately, irradiation test and modular shielding calculation are conducted as a minimal validation.

The proposed anti-radiation design methods and the irradiated results can be applied to many other nuclear vehicles and manipulators for the feasible multi-layer protection and excellent mobility.

A nuclear intervention robot with specific hardness design is presented in detail in this paper. Enlightened by the idea of shielding and distance protection, a large number of electronic modules with multiple types and structures are treated and compared in irradiation experiments, while modular redeployment and retrofitting are completed to reduce irradiated damages. To achieve the effect of time protection, mobility performance and operational practices are discussed and validated in the field experiment based on the mobile system.

The paper aims to present a tracked robot comprised of several biochemical sampling instruments and a universal control architecture. In addition, a dynamic motion planning strategy and autonomous modules in sampling tasks are designed and illustrated at length.

Several sampling instruments with position tolerance and sealing property are specifically developed, and a robotic operation system (ROS)-based universal control architecture is established. Then, based on the system, two typical problems in sampling tasks, i.e. arm motion planning in unknown environment and autonomous modules, are discussed, implemented and tested. Inspired by the idea of Gaussian process classification (GPC) and Gaussian process (GP) information entropy, three-dimensional (3D) geometric modeling and arm obstacle avoidance strategy are implemented and proven successfully. Moreover, autonomous modules during sampling process are discussed and realized.

Smooth implementations of the two experiments justify the validity and extensibility of the robot control scheme. Furthermore, the former experiment proves the efficiency of arm obstacle avoidance strategy, while the later one demonstrates the time reduction and accuracy improvement in sampling tasks as the autonomous actions.

The proposed control architecture can be applied to more mobile and industrial robots for its feasible and extensible scheme, and the utility function in arm path planning strategy can also be utilized for other information-driven exploration tasks.

Several specific biochemical sampling instruments are presented in detail, while ROS and Moveit! are integrated into the system scheme, making the robot extensible, achievable and real-time. Based on the control scheme, an information-driven path planning algorithm and automation in sampling tasks are conceived and implemented.

As the infrastructure of the railway, the rail could sink or deform to different degrees due to the impact of train operation or the geological changing force for years, which will lead to the possibility that the facilities on both sides of the rail invade the rail clearance and bring hidden dangers to the safe operation of the train. The purpose of this paper is to design the gauge to measure the clearance parameters of rail.

Aiming at the problem, the gauge for clearance measurement was designed based on a combination measurement method in this paper. It consists of the measurement box and the rail measurement vehicle, which integrates a laser displacement sensor, inclination sensor, gauge sensor and mileage sensor. The measurement box was placed outside the rail vehicle. Through the design of a hardware circuit and software system, the movement measurement of the clearance parameters was realized.

In this paper, the measurement equations of horizontal distance and vertical height were established, the optimal solutions of the structural parameters in the equations were obtained by Levenberg–Marquardt method, then the parameter calibration problem was also solved.

The gauge has high precision; its measurement uncertainty reaches 1.27 mm. The gauge has manual and automatic working modes, which are convenient to operate and have practical popularization value.