Search Results (68)

From the point of view of the intelligent operation and maintenance of high-speed train tracks, this paper examines the research status of high-speed train rail damage detection technology in the field of high-speed train track operation and maintenance detection in recent years, summarizes the damage detection methods for high-speed trains, and compares and analyzes different detection technologies and application research results. The analysis results show that the detection methods for high-speed train rail damage mainly focus on the research and application of non-destructive testing technology and methods, as well as testing platform equipment. Detection platforms and equipment include a new type of vortex meter, integrated track recording vehicles, laser rangefinders, thermal sensors, laser vision systems, LiDAR, new ultrasonic detectors, rail detection vehicles, rail detection robots, laser on-board rail detection systems, track recorders, self-moving trolleys, etc. The main research and application methods include electromagnetic detection, optical detection, ultrasonic guided wave detection, acoustic emission detection, ray detection, vortex detection, and vibration detection. In recent years, the most widely studied and applied methods have been rail detection based on LiDAR detection, ultrasonic detection, eddy current detection, and optical detection. The most important optical detection method is machine vision detection. Ultrasonic detection can detect internal damage of the rail. LiDAR detection can detect dirt around the rail and the surface, but the cost of this kind of equipment is very high. And the application cost is also very high. In the future, for high-speed railway rail damage detection, the damage standards must be followed first. In terms of rail geometric parameters, the domestic standard (TB 10754-2018) requires a gauge deviation of ±1 mm, a track direction deviation of 0.3 mm/10 m, and a height deviation of 0.5 mm/10 m, and some indicators are stricter than European standard EN-13848. In terms of damage detection, domestic flaw detection vehicles have achieved millimeter-level accuracy in crack detection in rail heads, rail waists, and other parts, with a damage detection rate of over 85%. The accuracy of identifying track components by the drone detection system is 93.6%, and the identification rate of potential safety hazards is 81.8%. There is a certain gap with international standards, and standards such as EN 13848 have stricter requirements for testing cycles and data storage, especially in quantifying damage detection requirements, real-time damage data, and safety, which will be the key research and development contents and directions in the future. Full article

As a knowledge-intensive activity, the Kansei engineering (KE) process encounters numerous challenges in the design knowledge flow, primarily due to issues related to information reliability, uncertainty, and subjectivity. Bridging this gap, this study introduces an advanced KE framework integrating a cloud model with Z-numbers (CMZ) and Bayesian elastic net regression (BENR). In stage-I of this KE, data mining techniques are employed to process online user reviews, coupled with a similarity analysis of affective word clusters to identify representative emotional descriptors. During stage-II, the CMZ algorithm refines K-means clustering outcomes for market-representative product forms, enabling precise feature characterization and experimental prototype development. Stage-III addresses linguistic uncertainties in affective modeling through CMZ-augmented semantic differential questionnaires, achieving a multi-granular representation of subjective evaluations. Subsequently, stage-IV employs BENR for automated hyperparameter optimization in design knowledge inference, eliminating manual intervention. The framework’s efficacy is empirically validated through a domestic cleaning robot case study, demonstrating superior performance in resolving multiple information processing challenges via comparative experiments. Results confirm that this KE framework significantly improves uncertainty management in design knowledge flow compared to conventional implementations. Furthermore, by leveraging the intrinsic symmetry of the normal cloud model with Z-numbers distributions and the balanced ℓ₁/ℓ₂ regularization of BENR, CMZ–BENR framework embodies the principle of structural harmony. Full article

Recently, with policies aimed at strengthening domestic manufacturing and technological innovation, the robotics industry has been growing rapidly, and its applications are expanding across various industrial fields. Accordingly, the importance of high-performance speed reducers with flexibility and precision is gradually increasing. The tooth profiles used in conventional harmonic reducers have structural limitations, such as meshing discontinuity, restrictions on the radius of curvature of the tooth base, and distortion of the contact trajectory, especially when the number of teeth is small. These problems limit the design freedom of the reducer and make it difficult to secure contact stability and durability under precision driving conditions. To solve these problems, this paper proposes a new tooth profile design equation, the IH (Involute Harmonic) tooth profiles and the HTPs (Hybrid Tooth Profiles), using the cycloid curve to overcome the structural limitations of the conventional harmonic tooth profile, which is difficult to design under small-tooth-number conditions, and to enable tooth design without restrictions on the number of teeth. HTP tooth profile is a new gear tooth profile design method that utilizes IH tooth profile and cycloid curve to optimize the meshing characteristics of gears. A tooth profile design tool based on the HTP equation was developed using Python 3.13.3. The tool’s effectiveness was validated through simulations assessing tooth meshing and interference. Using the tool, an R21_z3 reducer with a single-stage high reduction ratio was designed to evaluate practical applicability. A prototype was fabricated using 3D printing with PLA material, and experimental testing confirmed the absence of meshing or interference issues, consistent with simulation results. Through this study, we verified the usefulness of the HTP tooth profile design formula and design tool using the IH tooth profile and cycloid curve, and it is expected that the proposed HTP tooth profile can be utilized as a tooth profile applicable to various reducer designs. Full article

While deep neural networks demonstrate robust performance in visual tasks, the long-tail distribution of real-world data leads to significant recognition accuracy degradation in critical scenarios such as medical human–robot affective interaction, particularly the misidentification of low-frequency negative emotions (e.g., fear and disgust) that may trigger psychological resistance in patients. Here, we propose a method based on dynamic intra-class clustering (DICC) to optimize the class imbalance problem in facial expression recognition tasks. The DICC method dynamically adjusts the distribution of majority classes by clustering them into subclasses and generating pseudo-labels, which helps the model learn more discriminative features and improve classification accuracy. By comparing with existing methods, we demonstrate that the DICC method can help the model achieve superior performance across various facial expression datasets. In this study, we conducted an in-depth evaluation of the DICC method against baseline methods using the FER2013, MMAFEDB, and Emotion-Domestic datasets, achieving improvements in classification accuracy of 1.73%, 1.97%, and 5.48%, respectively. This indicates that the DICC method can effectively enhance classification precision, especially in the recognition of minority class samples. This approach provides a novel perspective for addressing the class imbalance challenge in facial expression recognition and offers a reference for future research and applications in related fields. Full article

This study investigates sustainable competitiveness in the wine industry using Romania and Portugal as comparative case studies within the conceptual frameworks of Industry 5.0 and the Triple Bottom Line (TBL). While sustainability, robotics, and performance indicators are explored directionally, the core empirical contribution focuses on evaluating key wine industry metrics and their impact on export value. Using data from the International Organisation of Vine and Wine (OIV) and the World Trade Map, we perform a one-way ANOVA to examine differences between the two countries across five variables: vineyard area, wine production volume, grape production, consumption, and export value. The results reveal statistically significant differences in all indicators except vineyard area, with Portugal significantly outperforming Romania in production, consumption, and exports (p < 0.001). To assess the drivers of export performance, we construct a Structural Equation Model (SEM) using SmartPLS. The model confirms that wine production volume and domestic consumption are the strongest positive predictors of export value (loading factors 1.003 and 0.909, respectively), while vineyard area has minimal influence. The model exhibits strong fit indices (e.g., SRMR = 0.009; NFI = 0.971), supporting the robustness of the results. The findings suggest that internal market strength and production efficiency, rather than land size, are critical for export competitiveness. Romania can enhance its performance by aligning production strategies with TBL principles and selectively adopting Industry 5.0 technologies in viticulture. Full article

Small to medium-sized shipyards play a crucial role in the European naval industry. However, the globalization of technology has increased competition, posing significant challenges to shipyards, particularly in domestic markets for short sea, work, and inland vessels. Many shipyard operations still rely on manual, labor-intensive tasks performed by highly skilled operators. In response, the adoption of new tools is essential to enhance efficiency and competitiveness. This paper presents a methodology for developing a human-centric portfolio of advanced technologies tailored for shipyard environments, covering processes such as shipbuilding, retrofitting, outfitting, and maintenance. The proposed technological solutions, which have achieved high technology readiness levels, include 3D modeling and digitalization, robotics, augmented and virtual reality, and occupational exoskeletons. Key findings from real-scale demonstrations are discussed, along with major development and implementation challenges. Finally, best practices and recommendations are provided to support both technology developers seeking fully tested tools and end users aiming for seamless adoption. Full article

Typical SLAM systems adhere to the assumption of environment rigidity, which limits their functionality when deployed in the dynamic indoor environments commonly encountered by household robots. Prevailing methods address this issue by employing semantic information for the identification and processing of dynamic objects in scenes. However, extracting reliable semantic information remains challenging due to the presence of motion blur. In this paper, a novel visual SLAM algorithm is proposed in which various approaches are integrated to obtain more reliable semantic information, consequently reducing the impact of motion blur on visual SLAM systems. Specifically, to accurately distinguish moving objects and static objects, we introduce a missed segmentation compensation mechanism into our SLAM system for predicting and restoring semantic information, and depth and semantic information is then leveraged to generate masks of dynamic objects. Additionally, to refine keypoint filtering, a probability-based algorithm for dynamic feature detection and elimination is incorporated into our SLAM system. Evaluation experiments using the TUM and Bonn RGB-D datasets demonstrated that our SLAM system achieves lower absolute trajectory error (ATE) than existing systems in different dynamic indoor environments, particularly those with large view angle variations. Our system can be applied to enhance the autonomous navigation and scene understanding capabilities of domestic robots. Full article

Robots are becoming an increasingly popular element of the developing technological reality. A companion (social) robot can be defined as an autonomous machine that recognises other robots and people and engages in social interactions. Robots of this type are designed to serve people, so they often act as guides, assistants, companions, guardians, teachers and domestic pets. This study aims to define and operationalise the components and synthetically measure the social awareness (index) concerning the usability of a companion robot in everyday life. A comprehensive new research tool was developed as part of the study in the form of an original standardised interview questionnaire by the CATI/CAWI technique. Empirical studies show that an increase in social acceptance requires that potential consumers be provided with the necessary information to know the technologies better and improve their digital skills related to social robots. Full article

Medical care for domestic dogs is now respected worldwide as being at a similar level to that of humans. We previously established a test method to determine whole mitochondrial DNA (mtDNA) using oral mucosal DNA that may be useful for medical care and welfare. However, the sample types tested in dogs are not limited to those obtained from the oral mucosa. Therefore, in the present study, we attempted to establish a test method to determine whole mtDNA sequences using feces, which represents the least invasive specimen. Two Japanese domestic dogs were used in the present study. DNA was extracted from approximately 100 mg of fresh feces from each dog, and PCRs were performed using four primer pairs that can amplify whole mtDNA. Following PCR, amplicons were pooled to create a DNA library using an experimental robot with an original program. Data were then acquired via NGS and data analysis was performed. The results showed that the whole mtDNA sequence of the two dogs was determined with high accuracy. Our results suggest that feces can be adapted for mitochondrial disease and individual identification testing and could serve as a useful testing method for the future medical care and welfare of domestic dogs. Full article

Premium tea holds a significant place in Chinese tea culture, enjoying immense popularity among domestic consumers and an esteemed reputation in the international market, thereby significantly impacting the Chinese economy. To tackle challenges associated with the labor-intensive and inefficient manual picking process of premium tea, and to elevate the competitiveness of the premium tea sector, our research team has developed and rigorously tested a premium tea-picking robot that harnesses deep learning and computer vision for precise leaf recognition. This innovative technology has been patented by the China National Intellectual Property Administration (ZL202111236676.7). In our study, we constructed a deep-learning model that, through comprehensive data training, enabled the robot to accurately recognize tea buds. By integrating computer vision techniques, we achieved exact positioning of the tea buds. From a hardware perspective, we employed a high-performance robotic arm to ensure stable and efficient picking operations even in complex environments. During the experimental phase, we conducted detailed validations on the practical application of the YOLOv8 algorithm in tea bud identification. When compared to the YOLOv5 algorithm, YOLOv8 exhibited superior accuracy and reliability. Furthermore, we performed comprehensive testing on the path planning for the picking robotic arm, evaluating various algorithms to determine the most effective path planning approach for the picking process. Ultimately, we conducted field tests to assess the robot’s performance. The results indicated a 62.02% success rate for the entire picking process of the premium tea-picking robot, with an average picking time of approximately 1.86 s per qualified tea bud. This study provides a solid foundation for further research, development, and deployment of premium tea-picking robots, serving as a valuable reference for the design of other crop-picking robots as well. Full article

Loneliness is increasingly common, especially among older adults. Technology like mobile telepresence robots can help people feel less lonely. However, such technology has challenges, and even if people use it in the short term, they may not accept it in the long term. Prior work shows that it can take up to six months for people to fully accept technology. This study focuses on exploring the nuances and fluidity of acceptance phases. This paper reports a case study of four older adult participants living with a mobile telepresence robot for seven months. In monthly interviews, we explore their progress through the acceptance phases. Results reveal the complexity and fluidity of the acceptance phases. We discuss what this means for technology acceptance. In this paper, we also make coding guidelines for interviews on acceptance phases more concrete. We take early steps in moving toward a more standard interview and coding method to improve our understanding of acceptance phases and how to help potential users progress through them. Full article

In this study, a portable magnetic adsorption amphibious robot which can operate on and below the waterline is developed for special curved environments, such as the pile legs of offshore platforms and the outer walls of ships. An open robot integrated control system based on a domestic chip is developed, and two operating modes of local control operation and remote wireless operation are realized. A permanent magnet adsorption scheme combining a magnetic adsorption track and a synchronous belt wheel is designed, static and dynamic analysis of the wall-climbing operation of the robot is carried out, and a kinematic model of the underwater robot is established. The experimental results show that the robot can effectively complete amphibious tasks and can realize the accurate control of attitude in water, proving it to be an effective tool for amphibious tasks, such as operating on the pile legs of offshore platforms and the outer walls of ships. Full article

This study explores the optimization of the TIAGo robot’s configuration for grasping operation, with a focus on the context of aging. In fact, featuring a mobile base and a robotic arm, the TIAGo robot can conveniently aid individuals with disabilities, including those with motor and cognitive impairments in both domestic and clinical settings. Its capabilities include recognizing visual targets such as faces or gestures using stereo cameras, as well as interpreting vocal commands through acoustic sensors to execute tasks. For example, the robot can grasp and lift objects such as a glass of water and navigate autonomously in order to fulfill a request. The paper presents the position and differential kinematics that form the basis for using the robot in numerous application contexts. In the present case, they are used to evaluate the kinematic performance of the robot relative to an assigned pose in the search for the optimal configuration with respect to the higher-order infinite possible configurations. Ultimately, the article provides insight into how to effectively use the robot in gripping operations, as well as presenting kinematic models of the TIAGo robot. Full article

In recent years, both domestic and international research on quadruped robots has advanced towards high dynamics and agility, with a focus on high-speed locomotion as a representative motion in high-dynamic activities. Quadruped animals like cheetahs exhibit high-speed running capabilities, attributed to the indispensable role played by their flexible spines during the flight phase motion. This paper establishes dynamic models of flexible spinal quadruped robots with different degrees of simplification, providing a parameterized description of the flight phase motion for both rigid-trunk and flexible-spine quadruped robots. By setting different initial values for the spine joint and calculating the flight phase results for both types of robots at various initial velocities, the study compares and analyzes the impact of a flexible spine on the flight phase motion of quadruped robots. Through comparative experiments, the research aims to validate the influence of a flexible spine during the flight phase motion, providing insights into how spine flexibility affects the flight phase motion of quadruped robots. Full article

Differential drive mobile robots, being widely used in several industrial and domestic applications, are increasingly demanding when concerning precision and satisfactory maneuverability. In the present paper, the problem of independently controlling the velocity and orientation angle of a differential drive mobile robot is investigated by developing an appropriate two stage nonlinear controller embedded on board and also by using the measurements of the speed and accelerator of the two wheels, as well as taking remote measurements of the orientation angle and its rate. The model of the system is presented in a nonlinear state space form that includes unknown additive terms arising from external disturbances and actuator faults. Based on the nonlinear model of the system, the respective I/O relation is derived, and a two-stage nonlinear measurable output feedback controller, analyzed into an internal and an external controller, is designed. The internal controller aims to produce a decoupled inner closed-loop system of linear form, regulating the linear velocity and angular velocity of the mobile robot independently. The internal controller is of the nonlinear PD type and uses real time measurements of the angular velocities of the active wheels of the vehicle, as well as the respective accelerations. The external controller aims toward the regulation of the orientation angle of the vehicle. It is of a linear, delayed PD feedback form, offering feedback from the remote measurements of the orientation angle and angular velocity of the vehicle, which are transmitted to the controller through a wireless network. Analytic formulae are derived for the parameters of the external controller to ensure the stability of the closed-loop system, even in the presence of the wireless transmission delays, as well as asymptotic command following for the orientation angle. To compensate for measurement noise, external disturbances, and actuator faults, a metaheuristic algorithm is proposed to evaluate the remaining free controller parameters. The performance of the proposed control scheme is evaluated through a series of computational experiments, demonstrating satisfactory behavior. Full article