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ABSTRACT A robust approximate constraint‐following control (RACC) approach is proposed in this article for collaborative robots with inequality constraints. The trajectory‐following control and boundary control of the robot are investigated. First, an explicit constraint equation for the collaborative robot system is established based on the Udwadia–Kalaba (U‐K) theory. Second, due to the monotone unbounded property of the tangent function, a special function is constructed to transform the joint output angles of the constrained robot into unconstrained state variables, and a new form of the robot constraint equation is obtained. Through this transformation, the joint motion of the robot will always be confined to specified angles and follow the desired trajectory. The constraint equation ensures the safety of the robot at the algorithmic level and innovatively solves the control problem of the equality and inequality of the robot's motion. According to theoretical analysis, the control approach can deal with uncertainty and satisfy both uniform boundedness (UB) and uniform ultimate boundedness (UUB) requirements. Finally, based on the rapid controller prototype CSPACE and a two‐degree‐of‐freedom collaborative robot platform, experimental verification is carried out. Numerical simulation and experimental results demonstrate that the proposed RACC approach with state transformation exhibits significant advantages in trajectory tracking performance and safety for collaborative robots. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive investigation of the impact and mechanism of industrial robot utilization on total factor productivity (TFP) using panel data from A‐share listed companies in China's manufacturing industry. Our findings reveal the significant contribution of industrial robots to enhancing TFP in microenterprises, with this influence persisting over time. Mechanism tests demonstrate that industrial robot utilization effectively reduces labor costs, mitigates financing constraints, and enhances innovation capabilities, thereby improving enterprises' TFP. Additionally, a heterogeneity analysis indicates a more pronounced impact of industrial robot utilization on TFP in labor‐intensive, non‐high‐tech enterprises, as well as in enterprises operating in highly competitive markets. This study deepens our understanding of industrial robot utilization and TFP, which broadens the scope of this field. This has significant practical implications for transforming traditional factors, integrating new technologies, and facilitating digital and intelligent transformations of manufacturing enterprises in emerging economies. [ABSTRACT FROM AUTHOR]

This paper aims to develop a miniature mobile robot suitable to assist archeologists in their first exploration of unknown underground tombs. Due to the rather complex and irregular terrains in the tombs and inspired by the classic RHex design, we have developed a two‐segment articulated robot (A‐RHex) with two RHex design units. The robot is compact and lightweight, with dimensions of 25 cm long, 6.5 cm wide, 7 cm high, and weighs 283 g. To assist the robot in entering the tomb, we have also designed a deployment platform that can take the robots underground through a 10‐cm exploration hole. We introduce the overall design, control, and communication methods of A‐RHex, and theoretically analyze how the articulated design can improve the stability of the robot on slopes. Laboratory experiments and field testings at two real archeological excavation sites in China have validated A‐RHex's mechanical design, control strategies, communications, and capabilities for pre‐exploration of open and closed tombs. We believe that this kind of robot with high terrain adaptability and a small profile may become an important tool for field archeology in the future. [ABSTRACT FROM AUTHOR]

With the increasing development of industry, the market demand for manufacturing has shifted to large‐scale customized production. This poses new challenges to the production flexibility of industrial robots. The offline programming method can perfectly meet this challenge. But its disadvantage is that it relies heavily on the absolute positioning accuracy of industrial robots. In recent years, there has been an increasing number of studies using neural networks (NN) to predict the positioning errors of industrial robots to improve their absolute positioning accuracy. However, most of these studies only focus on the application of NNs, and do not compare the prediction results and performance of different kinds of NNs. This paper selects three typical network models: backpropagation neural network (BPNN), particle swarm algorithm optimization BPNN (PSO‐BPNN), and radial basis function neural network (RBFNN). Through in‐depth experiments and analysis of these networks, the purpose is to reveal their respective prediction effects and characteristics and to summarize their advantages and disadvantages. Experimental results show that BPNN performs poorly in predicting positioning errors. As an optimization method, the particle swarm algorithm can effectively improve the prediction performance of BPNN. In contrast, the RBFNN performs well, which makes it very suitable for predicting the positioning error of industrial robots. [ABSTRACT FROM AUTHOR]

With the advancement of industrial automation, the frequency of human–robot interaction (HRI) has significantly increased, necessitating a paramount focus on ensuring human safety throughout this process. This paper proposes a simulation‐assisted neural network for point cloud segmentation in HRI, specifically distinguishing humans from various surrounding objects. During HRI, readily accessible prior information, such as the positions of background objects and the robot's posture, can generate a simulated point cloud and assist in point cloud segmentation. The simulation‐assisted neural network utilizes simulated and actual point clouds as dual inputs. A simulation‐assisted edge convolution module in the network facilitates the combination of features from the actual and simulated point clouds, updating the features of the actual point cloud to incorporate simulation information. Experiments of point cloud segmentation in industrial environments verify the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

The convergence of Internet of Things (IoT), Cloud computing, and Fog computing, termed as Interconnected Intelligence (II), has revolutionised data management and real‐time decision‐making across various industries. This study introduces a hybrid architecture that integrates these technologies to optimise resource allocation, reduce latency, and improve decision accuracy. Unlike traditional models that rely heavily on centralised Cloud computing, our approach distributes computational tasks between IoT devices, Fog nodes, and Cloud servers, ensuring efficient real‐time processing closer to the data source. The proposed system demonstrated a 20%–30% reduction in latency compared to Cloud‐only architectures, and a 25% improvement in resource utilisation through dynamic load balancing between Fog and Cloud layers. Additionally, the system showed an increase in decision accuracy by 15%, enhancing real‐time decision‐making capabilities in critical applications such as industrial automation, healthcare, and smart urban environments. Data security and privacy were also significantly improved, achieving a 20% reduction in energy consumption by reducing reliance on centralised Cloud resources. These results were validated using real‐world datasets from industrial, healthcare, and urban environments, underscoring the architecture's capability to support large‐scale IoT deployments. Future research will focus on real‐world validation and the development of enhanced dynamic resource management techniques. [ABSTRACT FROM AUTHOR]

Concerns over widespread technological unemployment are often dismissed with the argument that human labour is not destroyed by automation but rather reallocated to other tasks, occupations or sectors. When focusing on pure employment levels, the idea that workers are not permanently excluded but 'just' reallocated might be reassuring. However, while attention has been devoted to the impact of automation on employment levels, little has been said about the quality of new job matches for displaced workers. Using an administrative longitudinal panel covering a large sample of Spanish workers from 2001 to 2017, we investigate the short‐ and medium‐term re‐employment prospects of workers displaced from sectors with an increasing density of industrial robots. Furthermore, we examine the role of reallocation to other sectors or local labour markets as adjustment mechanisms. Our analysis suggests that exposed middle‐ and low‐skilled workers are more likely than non‐exposed workers to remain unemployed 6 months after displacement. Among those who find a new occupation, an additional robot per 1000 workers increases the probability of being re‐employed in a lower paying job by about 1.9 percentage points for middle‐ and low‐skilled workers, with significantly higher penalties for those who relocate to a different sector. Moreover, these workers tend to face a qualification downgrading in the new job and are more likely to be re‐employed through temporary employment agencies. High‐skilled workers are less negatively affected by exposure, although they can also incur a penalty when changing sectors. [ABSTRACT FROM AUTHOR]

Maintenance manuals are crucial information sources for maintenance and repair. Prior studies explored factual knowledge extraction from textual documents. However, maintenance knowledge in manuals is more task‐centric rather than factual knowledge and often documented in an unstructured Portable Document Format (PDF), posing challenges for knowledge extraction. Addressing this, this research develops effective methods to extract task‐centric maintenance knowledge from unstructured PDF manuals. A new Task‐centric Knowledge Graph (TCKG) schema centralized on maintenance task components (MTCs) is proposed to address the need for structured knowledge representation. A method (Heterogeneous Graph‐based Method, HGM) for knowledge extraction is then proposed, which is enhanced by incorporating visual and spatial information. In the experiments, the proposed HGM exhibits robust performance in the knowledge extraction process, surpassing the baseline Graph‐based Interaction Model with a Tracker (GIT) method in MTCs extraction by 13.3%, and the baseline Translate Embedding (TransE) method in MTCs' relation extraction by 3.8%. A series of ablation studies also prove that including visual and spatial information through the proposed method can improve the relation extraction performance by over 10%. This research supplies valuable insights for future developments in information extraction from maintenance manuals. [ABSTRACT FROM AUTHOR]

Summary: Obstacle avoidance planning is the primary element in ensuring safe robot applications such as welding, assembly, and drilling. The states in the configuration space (C‐space) provide the pose information of any part of the manipulator and are preferentially considered in motion planning. However, it is difficult to express the environmental information directly in the high dimensional C‐space, limiting the application of C‐space obstacle avoidance planning. This paper proposes a singular manifold splitting C‐space method and designs a compatible obstacle avoidance strategy. The specific method is as follows: first, according to the specific structure of industrial robots, arm‐wrist separation obstacle avoidance planning is proposed to fix the robot as a 3R manipulator to reduce the dimension of C‐space. Next, the C‐space is segmented according to the singular manifolds, and the unique domain is delineated to complete the streamlining of the volume of the C‐space. Then, with the help of the point cloud, the obstacles are enveloped and mapped to the unique domain to construct the pseudo‐obstacle map. Industrial robots' obstacle avoidance planning is completed based on the pseudo‐obstacle map combined with an improved Rapidly‐Exploring Random Trees (RRT) algorithm. This method dramatically improves the efficiency of obstacle avoidance planning in the C‐space and avoids the effect of singularities on industrial robots. Finally, the effectiveness of the method is verified by physical experiments. [ABSTRACT FROM AUTHOR]

In response to the growing demands for faster production and cost‐efficiency, collaborative and autonomous robots are playing increasingly important roles in various industries. However, ensuring safe interactions between robots and humans in shared workspaces continues to pose significant challenges. This paper provides a detailed review of motion planning algorithms designed for robotic manipulators working in dynamic environments, with a focus on efficiently adapting to changing conditions while ensuring human operator safety. The algorithms covered in this survey—identified through a comprehensive review of the literature and validated experimentally—include sampling‐based methods, artificial potential field (APF) methods, optimization‐based approaches, and learning‐based techniques. Experimental evaluations offer a practical assessment of these algorithms' real‐time performance. By comparing and analysing the experimental results, this article highlights the relative efficiency of the algorithms and suggests strategies for improving motion planning in dynamic environments. [ABSTRACT FROM AUTHOR]

The rotary vector (RV) reducer, as a highly precise transmission mechanism, can lead to a series of consequential hazards when experiencing local faults. Conducting corresponding research on performance monitoring and fault diagnosis holds significant importance. In order to achieve higher diagnostic accuracy, better classification, prediction effect, and less use of sensors, this paper proposes a local fault diagnosis method of RV reducer based on Motor current signature analysis (MCSA). Firstly, the centre gear local fault electromechanical coupling model is created by combining the working principle of the servo motor, the working characteristics of the RV reducer, and the influencing factors of motor current. Then, according to the actual operating conditions of industrial robots (IRs), the corresponding experimental platform is designed and constructed, and current signals in four different modes are collected. Fast Fourier transform (FFT) is performed on the current signals to obtain frequency domain features, and the correctness of the local fault coupling model of the centre gear is experimentally verified. Finally, the time‐domain statistical features, time–frequency domain features, and CNN features of servo‐feedback current signals under different rotational speeds are extracted and used in the implementation of local fault diagnosis for the RV reducer, respectively. In this paper, it is confirmed that MCSA can be used for localized fault diagnosis of the RV reducer, and combined with a deep learning network, it can effectively predict the fault modes with an average accuracy of more than 96%. [ABSTRACT FROM AUTHOR]

Construction robots play a pivotal role in enabling intelligent processes within the construction industry. User‐friendly interfaces that facilitate efficient human–robot collaboration are essential for promoting robot adoption. However, most of the existing interfaces do not consider contextual information in the collaborative environment. The situation where humans and robots work together in the same jobsite creates a unique environmental context. Overlooking contextual information would limit the potential to optimize interaction efficiency. This paper proposes a novel context‐aware method that utilizes a two‐stream network to enhance human–robot interaction in construction settings. In the proposed network, the first‐person view‐based stream focuses on the relevant spatiotemporal regions for context extraction, while the motion sensory data‐based stream obtains features related to hand motions. By fusing the vision context and motion data, the method achieves gesture recognition for efficient communication between construction workers and robots. Experimental evaluation on a dataset from five construction sites demonstrates an overall classification accuracy of 92.6%, underscoring the practicality and potential benefits of the proposed method. [ABSTRACT FROM AUTHOR]

Collaborative robots are becoming popular because they can operate without safety fences and can work in close proximity to humans. Proximity and tactile sensors on the robot's surface are important safety measures for existing robots. In this paper, we propose a string‐like ToF and self‐capacitance combined sensor that can be easily wrapped around various types of existing robots. The sensor can be freely connected to other sensors, and the length of the connected sensors attached can be adjusted depending on the shape of the robot. Prototype sensors were fabricated, and four connected sensors were wrapped around the robotic arm. Sensors on the robot's surface can detect objects within the proximity range and determine contact. In addition, a robotic arm with sensors can be controlled in real‐time using measured data from the sensors. It was confirmed that the proposed sensor can be used for proximity and tactile sensing on existing robots. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Accurate dynamic models that update in real‐time are vital for industrial robots to adapt flexibly and robustly to evolving environments and tasks, enabling precise model‐based control, motion planning and disturbance estimation. However, the identification of a dynamic robot model involves multiple challenges. First, existing friction models cannot accurately characterize torque variance during prolonged robot operations. Second, traditional offline identification methods such as the least squares (LS) method, suffer from over‐fitting when the inertia parameters are unknown a priori. Furthermore, existing online identification approaches, such as sliding LS, fail to meet the accuracy and real‐time requirements of industrial robots. To address these challenges, this article proposes a two‐stage Bayesian framework for rapid offline identification and online updating of industrial robot dynamics. In the offline stage, we developed a recursive sparse Bayesian learning (RSBL) method to select the dominant parameters and discover a thermal‐related nonlinear dynamic friction model from data, which was then used to find a sparser and simpler inertia model. The obtained friction and inertia models were used as prior knowledge for online updating. The RSBL method significantly reduces the computational complexity and runtime, while it also accelerates online model updating. Experimental results for a 6‐DOF medium‐load robot validate dual offline improvements in prediction accuracy and model sparsity, along with higher online prediction accuracy and shorter computational time. [ABSTRACT FROM AUTHOR]

Summary: The application of computer technologies associated with sensors and artificial intelligence (AI) in the quantification and qualification of quality parameters of meat products of various domestic species is an area of research, development, and innovation of great relevance in the agri‐food industry. This review covers the most recent advances in this area, highlighting the importance of computer vision, artificial intelligence, and ultrasonography in evaluating quality and efficiency in meat products' production and monitoring processes. Various techniques and methodologies used to evaluate quality parameters such as colour, water holding capacity (WHC), pH, moisture, texture, and intramuscular fat, among others related to animal origin, breed and handling, are discussed. In addition, the benefits and practical applications of the technology in the meat industry are examined, such as the automation of inspection processes, accurate product classification, traceability, and food safety. While the potential of artificial intelligence associated with sensor development in the meat industry is promising, it is crucial to recognise that this is an evolving field. This technology offers innovative solutions that enable efficient, cost‐effective, and consumer‐oriented production. However, it also underlines the urgent need for further research and development of new techniques and tools such as artificial intelligence algorithms, the development of more sensitive and accurate multispectral sensors, advances in computer vision for 3D image analysis and automated detection, and the integration of advanced ultrasonography with other technologies. Also crucial is the development of autonomous robotic systems for the automation of inspection processes, the implementation of real‐time monitoring systems for traceability and food safety, and the creation of intuitive interfaces for human‐machine interaction. In addition, the automation of sensory analysis and the optimisation of sustainability and energy efficiency are key areas that require immediate attention to address the current challenges in this agri‐food and agri‐industrial sector, highlighting and emphasising the importance of ongoing innovation in the field. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI)—the field of computer science that designs machines to perform tasks that typically require human intelligence—has seen rapid advances in the development of foundation systems such as large language models. In the workplace, the adoption of AI technologies can result in a broad range of hazards and risks to workers, as illustrated by the recent growth in industrial robotics and algorithmic management. Sources of risk from deployment of AI technologies across society and in the workplace have led to numerous government and private sector guidelines that propose principles governing the design and use of trustworthy and ethical AI. As AI capabilities become integrated in devices, machines, and systems across industry sectors, employers, workers, and occupational safety and health practitioners will be challenged to manage AI risks to worker health, safety, and well‐being. Five risk management options are presented as ways to assure that only trustworthy and ethical AI enables workplace devices, machinery, and processes. AI technologies will play a significant role in the future of work. The occupational safety and health practice and research communities need to ensure that the promise of these new AI technologies results in benefit, not harm, to workers. [ABSTRACT FROM AUTHOR]

This article examines the challenges surrounding the adoption of robotic automation and 'AgTech' in glasshouse agrifood production and considers ways in which growers and technology capital are seeking to transform the organisation of work at the point of production because of digital technological interventions. The article does so by examining the deployment by growers of 'AgTech' in the glasshouse agrifood sector in the context of the labour regime centred around seasonal migrant labour supply in the UK. The article makes three main contributions. Conceptually it centres technological transformation of the workplace within the context of wider labour regimes. Second, it argues that transformations of the labour process at the point of production through the introduction of 'AgTech' are one response by growers to labour supply shortages. Third, it examines the attempts by grower capital to intensify and fragment the nature of work as a result of the introduction of 'AgTech'. [ABSTRACT FROM AUTHOR]

As robotics and intelligence increasingly integrate into surgery, the pivotal role of human–robot interaction (HRI) in surgical procedures and outcomes becomes evident. However, debate rages over whether increasing robot autonomy will result in less human involvement. Some scholars assert that autonomy will reduce human participation, whereas others contend it will result in more complex interactions. To reveal the role of HRI in the evolution of autonomous surgery, this review systematically explores the HRI of robotic surgery with various levels of autonomy. The HRI is examined from both robotic science and clinical practice perspectives, incorporating relevant case studies. Two key components, intention detection and situation awareness, are especially concerned with a brief description of the interfaces and control strategies they rely on. Additional insights are drawn from analogous technologies in aviation, industrial robotics, and autonomous vehicles. The analysis suggests that HRI complexity tends to increase as the robot transitions from no autonomy to conditional autonomy and is predicted to subsequently decrease with a substantial shift in the interaction form when moving toward full autonomy. It is concluded by highlighting challenges from technical and clinical perspectives and delineating research trends in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

This article introduces a novel six‐axis parallel manipulator co‐driven by motors with and without geared reducers. The design leverages the high stiffness provided by the geared motors and the high force‐sensing capability of the direct drives. When the geared motors move synchronously with their direct‐drive counterparts, the manipulator mimics a Delta robot's spatial translation, while the direct drives double as high‐performance force sensors. An asymmetric design of the manipulator is proposed to allocate a larger portion of torque to the geared motors, raising the overall stiffness of the mechanism. Relations between the external force to the driving torque are derived and the torque redistribution effect of the asymmetric design is illustrated numerically. Experimental results on prototypes of both the symmetric design and the asymmetric design show that direct drives consistently exhibit much higher linearity in the force‐to‐torque relationship compared to the geared drives, and also confirm that asymmetric design alleviates the torque burden on the direct drives. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Cooperative robots that perform safety functions as part of cooperative work with humans have been recently drawing attention. Proximity and tactile sensors that can be retrofitted to a robot's surface are important safety measures for existing robots that are not manufactured as cooperative robots to be used as cooperative robots. In this paper, a string‐like self‐capacitance‐based proximity and tactile sensor that can be easily wrapped around various types of existing robots is proposed. The novel and useful point in this paper is that it can be easily mounted on various robots by a flexible string‐like proximity and tactile sensor. The sensor module can be adjusted to the required length by connecting different numbers of modules depending on the mounting surface. As a demonstration, six prototype sensor modules were connected by each connector and wrapped around a robotic arm. The sensor modules on the robot's surface could detect an object within its proximity range and detect the rough pressure after contact. In addition, the robotic arm with the sensor modules could be controlled in real time using the data obtained from the sensor that are object detection data at proximity range. These results confirm that the proposed sensor module can be used in the proximity and tactile sensors of existing robots. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In industrial robot welding heat exchanger plate route planning, there are drawbacks to using the traditional ant colony algorithm (ACA), including poor search efficiency and a propensity to trend toward the local optimum. To address the above issues, first of all, the article introduces the improved pheromone volatilization factor, which is adjustable based on iteration times in the ACA. Secondly, it combines the new perturbation strategy and the cross‐mutation operation, proposes an improved genetic algorithm, and fuses it into the ACA, which increases the diversity of the ACA's path searching and improves the ability of the local and global search. Finally, the improved ACA mechanism is verified by experiments and compared with six existing path planning algorithms (including three ACA variant algorithms and three mainstream algorithms). The experimental results show that the IACAG algorithm has excellent performance in welding heat exchanger path planning and can achieve lower calculation time and iteration times under the conditions of optimal solution and zero deviation, showing comprehensive advantages in solution quality, stability, and efficiency. [ABSTRACT FROM AUTHOR]

Frequency‐modulated continuous‐wave (FMCW) light detection and ranging (LiDAR) has a huge potential for developing the next generation of LiDAR applied in autonomous driving, industrial automation, environmental monitoring, and so on. An ideal laser for the FMCW LiDAR system should simultaneously feature fast chirp repetition frequency, a large chirp bandwidth, high linearity, a compact footprint, and low‐cost. In this study, such a laser based on thin‐film lithium niobate (TFLN) photonics is proposed and demonstrated. The laser can achieve a chirp bandwidth of 3.44 GHz, a tuning efficiency of 574 MHz V−1, and a chirp rate of 3.44 × 107 GHz s−1, which are the best values compared with other TFLN‐based lasers. A FMCW LiDAR system built by their laser is also experimentally demonstrated, showing that it can achieve a ranging precision of 4.9 mm, a velocity precision of 0.054 m s−1, and a sampling rate of 5 MSa s−1. [ABSTRACT FROM AUTHOR]

This work tackles the tracking control problem of robotic manipulators where the robot dynamics contains uncertain parameters and joint velocity measurements are not available. Specifically when the robotic manipulator is required to perform a periodic task repetitively, as in most industrial applications, a repetitive learning controller is proposed that does not require joint velocity measurements and can compensate the uncertainties of the robot dynamical parameters and additive disturbances caused due to the periodic joint motion. The proposed solution is achieved via the use of a novel learning component in the controller design in conjunction with a novel model‐free joint velocity observer design. The stability of the closed‐loop system and the convergence of both the joint position tracking error and the joint velocity observation error to the origin are guaranteed via Lyapunov based arguments. Experimental results performed on a 2 degree of freedom robot manipulator are presented to demonstrate the performance of the proposed observer–controller couple. [ABSTRACT FROM AUTHOR]

A striking feature of the contemporary U.S. economy is the widespread adoption of technologies associated with the 4th Industrial Revolution (IR) including sensors, industrial robotics, artificial intelligence, and machine learning. Empirical work investigating the labor market impacts of 4th IR technological advances reveals they are skill‐biased, primarily advancing job opportunities for those with high levels of formal of education, unlike prior industrial revolutions that were skill neutral. This paper explains how the 4th IR has fostered job polarization, greater technological unemployment, and increasing wage inequality. Moreover, we delineate why these developments are worrisome and likely to expand over time. We assert that policy intervention to promote reinstatement of technologically unemployed persons is warranted, because displacement of labor caused by automation creates negative externalities. Consequently, we provide an example of how tax policies could be used to curtail technological unemployment—without creating hurdles for the adoption of 4th IR innovations. We advance and describe a new, innovative way, for fiscal policy to foster employment opportunities for those at greatest risk of job loss through automation. The initiative entails government subsidies to promote the provision and acquisition of skill certificates that employers view as complementary with 4th Industrial Revolution Technologies. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) are essential in environmental monitoring, healthcare, and industrial automation. Persistent connectivity and coverage challenges in WSN stem from intermittent node connectivity due to obstacles, signal interference, node failures, and compromised data reliability. Existing solutions, while useful, exhibit limitations in fully addressing these concerns. To confront these challenges, a proposed system introduces the Hybrid Grey Optimizer–Harmony Search Algorithm (Hybrid GWO‐HSA), merging adaptive routing protocols and efficient deployment techniques. The Hybrid GWO‐HSA system conducts an initial environmental analysis to pinpoint factors affecting node communication. It strategically deploys additional nodes to bridge coverage gaps, using the Grey Wolf Algorithm's capabilities to optimize node placement. Moreover, it employs the Harmony Search Algorithm to dynamically adjust communication paths based on real‐time network conditions, ensuring robust data transmission. The system workflow involves an environmental assessment followed by node deployment guided by the Grey Wolf Algorithm. Subsequently, the Harmony Search adapts communication paths to enhance connectivity. Simulations and practical experiments across diverse environments validate the Hybrid GWO‐HSA system's effectiveness. Results showcase substantial improvements: network lifetime of 13,200 s, a network delay of 37 ms, a coverage rate of 0.88, and an energy consumption of 590 J. This Hybrid GWO‐HSA‐based system establishes a resilient and efficient WSN infrastructure vital for reliable data collection and transmission in challenging settings. The Hybrid GWO‐HSA system offers a comprehensive approach to WSN connectivity and coverage issues by leveraging firefly and genetic algorithms, and it significantly enhances WSN performance and reliability across multifaceted application domains. [ABSTRACT FROM AUTHOR]

The use of collaborative robots in industries is growing rapidly. To ensure the successful implementation of these devices, it is essential to consider the user experience (UX) during their design process. This study is aimed at testing the UX goals that emerge when users interact with a collaborative robot during the programming and collaborating phases. A framework on UX goals will be tested, in the geographical context of Portugal. For that, an experimental setup was introduced in the form of a laboratory case study in which the human–robot collaboration (HRC) was evaluated by the combination of both quantitative (applying the User Experience Questionnaire [UEQ]) and qualitative (semistructured interviews) metrics. The sample was constituted by 19 university students. The quantitative approach showed positive overall ratings for the programming phase UX, with attractiveness having the highest average value (M = 2.21; SD = 0.59) and dependability the lowest (M = 1.64; SD = 0.65). For the collaboration phase, all UX ratings were positive, with attractiveness having the highest average value (M = 2.46; SD = 0.78) and efficiency the lowest (M = 1.93; SD = 0.77). Only perspicuity showed significant differences between the two phases (t(18) = −4.335, p = 0.002). The qualitative approach, at the light of the framework used, showed that efficiency, inspiration, and usability are the most mentioned UX goals emerging from the content analysis. These findings enhance manufacturing workers' well‐being by improving cobot design in organizations. [ABSTRACT FROM AUTHOR]

The article discusses the innovative architectural design of MONITOR, a mobile studio created by James Capper, Thomas Pearce, and Greg Storrar. MONITOR is a sculptural, inhabitable machine that utilizes robotic fabrication technologies and can be used as a creative studio. The design of MONITOR draws inspiration from the post-industrial landscapes of Kyiv and incorporates elements of alchemy, Arcadia, and machines. The article highlights the unique robotic wall fabrication process used in creating MONITOR, emphasizing its adaptability and multivalency in accommodating various functions and activities. [Extracted from the article]

The application of industrial robots has emerged as a significant influencing factor on international trade. Based on product export stability data from 49 major exporting countries and corresponding industrial robot application data between 2010 and 2019, this study investigates the impact and mechanisms of industrial robot application on export stability in these countries. The findings demonstrate that the application of industrial robots enhances export stability. The mechanism results indicate that the application of industrial robots principally impacts trade stability by lowering export costs and raising the technological complexity of export products. Moreover, this effect can be enhanced by increased financial development, an innovative environment, and the empowerment of women. This study provides valuable policy implications for advancing the application of industrial robots and improving export stability. [ABSTRACT FROM AUTHOR]

In order to improve the trajectory control effect of multi‐degree‐of‐freedom industrial robots, this paper combines visual image technology to conduct research on trajectory control of multi‐degree‐of‐freedom industrial robots. Aiming at the problem of video segmentation under sudden illumination changes, this paper uses a Gaussian mixture model based on the global illumination function to adopt a variety of illumination invariant features, and proposes a scene segmentation algorithm suitable for sudden illumination changes. Moreover, this paper compares and verifies the algorithm from the subjective and objective perspectives through experiments, which shows that the algorithm in this paper can segment the scene more accurately even in the environment of sudden changes in illumination. In addition, the results of the accuracy test and the trajectory control test show that the research method of the multi‐degree‐of‐freedom industrial robot trajectory control based on the visual image proposed in this paper can effectively improve the trajectory control effect of the robot. [ABSTRACT FROM AUTHOR]

The increasing introduction of intelligent, interactive robots in the service industry raises concerns about the potential dehumanization of service provision and its influences on corporate brand perceptions. To avoid adverse effects, new service development (NSD) managers seemingly favor service robots that feature anthropomorphic design metaphors, so they appear more human‐like. The current research investigates explicitly how customers' perception of a robot's anthropomorphic design metaphors might spill over to affect corporate brand perceptions. Study 1, a picture‐based scenario study with 109 participants, reveals the impact of anthropomorphic design metaphors on untested corporate brand outcomes, such as brand trust and brand experience. Then Study 2, a video‐based scenario study with 530 participants, addresses whether these effects depend on the service context. In Study 3, a field study of 393 participants, the authors examine how anthropomorphic design metaphors influence other firm‐related outcomes (e.g., shopping enjoyment, sales). The combined results confirm that anthropomorphic design metaphors strongly affect brand trust and brand experience, as well as other critical firm‐related outcomes; they also reveal notable context effects, such that customers of people‐processing (e.g., care services) and mental‐stimulus‐processing (e.g., shopping assistance) services appear more likely to use anthropomorphic design metaphors as corporate brand cues. Our research encourages NSD managers and scholars to consider the effects of introducing anthropomorphic service robots on corporate brands. [ABSTRACT FROM AUTHOR]

The emergence of Industry 5.0 marks a transformative era in manufacturing industry that is driven by the integration of advanced technologies. It is a renewed focus on human–robot collaboration. At the forefront of this industrial revolution stands Cobotics, a field that emphasizes the cooperative interaction between humans and robots in shared workspaces. This research paper delves into the critical role and implications of Cobotics in the context of Industry 5.0. The study addresses the research problem of effectively integrating Cobots into industrial processes, considering technical, economic, and social challenges. The paper's objectives include surveying existing literature, analyzing developments and trends, showcasing real‐world examples, and proposing future research directions. A comprehensive literature review explores Cobotics' foundations and its alignment with Industry 5.0 principles. The research presents real‐world examples of successful Cobot integration across various industries, demonstrating benefits such as increased productivity, improved quality, and enhanced worker safety. The paper identifies gaps in existing knowledge and contributions to the field, emphasizing the need for research in simplified programming, cost–benefit analysis, and ethical frameworks. This research paper illuminates the central role of Cobotics in Industry 5.0, where human–robot collaboration, innovation, and inclusivity are paramount. It anticipates a future where Cobotics will continue to evolve, shaping industries, empowering the workforce, and driving sustainable, data‐driven industrial transformations. [ABSTRACT FROM AUTHOR]

Flexible pressure sensors with high sensitivity over a broad sensing range are of great value in daily life and highly desired in various extreme environment, from human motion inspection to heavy industrial robots to high energy radiation and extremely cold/high temperature environments. However, it remains a significant challenge for a single pressure sensor to simultaneously possess high sensitivity and broad detection range due to the trade‐off between these two properties. Here, a high‐performance pressure sensor is developed based on flexible modified silicon rubber/functionalized carbon nanotube (MSR/FCNT). The designed nested‐cell architecture and molecular surface modification strategy endow the pressure sensor with high sensitivity (>28 kPa−1) over 10 MPa sensing range, an ultralow detection limit (≈0.94 Pa), an ultrahigh pressure resolution (0.0075%) at a pressure of 3 MPa, and a low fatigue over 10 000 repetitive cycles even at an extremely high pressure of 5 MPa. Furthermore, the resulting sensor presents excellent durability after freezing at extreme cold temperature (−80 °C) as well as resistance to high temperature (200 °C) and high‐energy X‐ray radiation. The proposed nested‐cell architecture is a generic strategy for porous materials to achieve broad range high sensitivity. [ABSTRACT FROM AUTHOR]

Recent advances in robotics have enabled robots to collaborate with workers in shared, fenceless workplaces in construction and civil engineering, which can improve productivity and address labor shortages. However, this collaboration may lead to collisions between workers and robots. Targeting safe collaboration, this study proposes an intention‐aware motion planning method for robots to avoid collisions. This method involves two novel deep networks that allow robots to anticipate the motions of workers based on inferences about workers' motion intentions. Then, a probabilistic collision‐checking mechanism is developed that enables robots to estimate the collision probability with the motions of workers and generate collision‐free adjustments. The results verify that the method enables robots to predict workers' intended motions 1 s in advance and generate adjustments with a collision probability of less than 5.0% during collaborative masonry tasks. This study facilitates the safe implementation of collaborative robots in construction and civil engineering. [ABSTRACT FROM AUTHOR]

This paper presents the development and validation of a custom MATLAB library, designed to facilitate seamless and efficient real‐time communication between the Siemens S7‐PLCSIM Advanced simulator and MATLAB/Simulink. The library uses Siemens.Simulation.Runtime.API to enable this integration and its architecture is structured into three classes—PLCSimAdv, PLCSR, and PLCSW. To demonstrate the library's capabilities, we have chosen the validation parameters and conducted functional experiments including real‐time communication at the millisecond level, integration and control of a simulated industrial process, and simultaneous operation with multiple virtual controllers. One of the library's preliminary conditions was to avoid the requests for regenerating of any version of control program or Simulink model as it is presently by S‐functions in TIA portal. The results suggest that the proposed library provides a robust tool for various applications in industrial automation, from the digital twin modelling or simulation of advanced technologies to the operation of traditional control systems. This work has the potential to significantly streamline the simulation, control, and validation of automated processes and opens new ways for research and development in automation and control systems. [ABSTRACT FROM AUTHOR]

Enforcement of advanced deep learning methods in hand‐object pose estimation is an imperative method for grasping the objects safely during the human–robot collaborative tasks. The position and orientation of a hand‐object from a two‐dimensional image is still a crucial problem under various circumstances like occlusion, critical lighting, and salient region detection and blur images. In this paper, the proposed method uses an enhanced MobileNetV3 with single shot detection (SSD) and YOLOv5 to ensure the improvement in accuracy and without compromising the latency in the detection of hand‐object pose and its orientation. To overcome the limitations of higher computation cost, latency and accuracy, the Network Architecture Search and NetAdapt Algorithm is used in MobileNetV3 that perform the network search for parameter tuning and adaptive learning for multiscale feature extraction and anchor box offset adjustment due to auto‐variance of weight in the level of each layers. The squeeze‐and‐excitation block reduces the computation and latency of the model. Hard‐swish activation function and feature pyramid networks are used to prevent over fitting the data and stabilizing the training. Based on the comparative analysis of MobileNetV3 with its predecessor and YOLOV5 are carried out, the obtained results are 92.8% and 89.7% of precision value, recall value of 93.1% and 90.2%, mAP value of 93.3% and 89.2%, respectively. The proposed methods ensure better grasping for robots by providing the pose estimation and orientation of hand‐objects with tolerance of −1.9 to 2.15 mm along x, −1.55 to 2.21 mm along y, −0.833 to 1.51 mm along z axis and −0.233° to 0.273° along z‐axis. [ABSTRACT FROM AUTHOR]

The Industrial Internet has emerged as a key technology in the field of industrial automation, revolutionizing traditional manufacturing processes and enabling advanced control systems by integrating machines, sensors, and software systems through network connectivity, allowing for real‐time data exchange, analysis, and decision‐making in industrial environments. Programmable Logic Controllers (PLCs) play a critical role in industrial automation systems, which are specialized digital computers designed to control various manufacturing processes and machinery. As a promising language, Structured Text (ST) is of paramount importance for industrial Internet, and it is essential to translate ST into C, which is widely used in various industries for implementing control systems. However, due to differences in syntax, data types, and programming paradigms between the two languages, the translation process is facing several challenges. In this paper, we present a domain‐specific translation tool that automates the process of converting ST code to C code. This tool incorporates advanced AI techniques, particularly leveraging the capabilities of NL‐PL LLMs, to facilitate accurate and efficient translation while generating comprehensive documentation to assist developers. Finally, the evaluation and case studies conducted validate the effectiveness of the tool, demonstrating its practical applicability and benefits in real‐world industrial settings. [ABSTRACT FROM AUTHOR]

Highly reliable mechanical systems can lead to significant losses in the event of failure, and the lack of comprehensive failure data presents challenges for developing techniques such as critical part identification and failure prediction. In light of this, this paper proposes a meta‐action‐based fault prediction method that effectively addresses the issue of limited fault data. Initially, the mechanical system is decomposed utilizing the "Function‐Motion‐Action" (FMA) methodology to derive individual meta‐action units (MAUs). Subsequently, the limited sample of fault data from the mechanical system is combined with processed expert knowledge to construct the corresponding fault propagation‐directed graph. Furthermore, the key MAUs are determined by applying the Decision–Making Trial and Evaluation Laboratory (DEMATEL) method. Last, the degradation data of the key MAUs is acquired by monitoring them, and a non‐homogeneous discrete grey model (DNGM) integrated with an improved BP neural network is proposed to facilitate the fault prediction of MAUs. Using an industrial robot as a case study, the prediction results demonstrate the superiority of the method proposed in this paper over a single gray model and neural network, thereby providing a reliable prediction approach for anticipating the future trends of data‐deficient mechanical systems. [ABSTRACT FROM AUTHOR]

Many real‐world applications, such as smart cities, industrial automation, health care, and so forth, utilize IoT‐enabled devices as a part of wireless networks. IoT devices must have low power and low computation complexity requirements with proper measures of security challenges. Since traditional cryptography techniques are not computationally efficient, other alternatives, such as physical layer key generation (PLKG), is one of the attractive means to achieve security. In this paper, we propose to use a fast independent component analysis (FICA) based KuFaI (Kurtosis and FICA) algorithm for a secured PLKG system. This method reduces data dimensions and improves system performance regarding Bit Disagreement Rate (BDR) and randomness. In KuFaI, we rearrange the received signal strength indicator (RSSI) data set using FICA and then select components based on the kurtosis function. Results demonstrate that the performance of the proposed algorithm is far better than the previously used PCA‐based algorithm. [ABSTRACT FROM AUTHOR]

This empirical study delves into the intricate factors that shape firms' choices regarding the adoption of robots within the Spanish context. Using a dataset encompassing a diverse set of industries, we employ an empirical analysis to uncover the determinants of robot adoption and investigate the associated outcomes on market variables. Our findings reveal several key factors that significantly influence a firm's likelihood of adopting robots. We find that firm profitability, exporter status, the control variables including share of R&D, and capital intensity exhibit strong positive relationships with robot adoption. Conversely, the impact of the level of human capital on adoption decisions is less pronounced. Furthermore, our study explores the impact of robot adoption on firm performance. We observe that firms embracing robotisation experience notable improvements in the output, exporting activities, and reduction in labour cost share. This study incorporates a gradient boosting‐based machine‐learning model, specifically XGBoost, along with instrumental variable regression models, to conduct rigorous robustness analyses and validate the obtained results. These findings contribute to the understanding of the dynamics and implications of robot adoption in the manufacturing sector, explaining the factors that drive firms' decisions and the subsequent market effects. [ABSTRACT FROM AUTHOR]

Soft material‐based robots, known for their safety and compliance, are expected to play an irreplaceable role in human‐robot collaboration. However, this expectation is far from real industrial applications due to their complex programmability and poor motion precision, brought by the super elasticity and large hysteresis of soft materials. Here, a soft collaborative robot (Soft Co‐bot) with intuitive and easy programming by contact‐based drag teaching, and also with exceptional motion repeatability (< 0.30% of body length) and ultra‐low hysteresis (< 2.0%) is reported. Such an unprecedented capability is achieved by a biomimetic antagonistic design within a pneumatic soft robot, in which cables are threaded to servo motors through tension sensors to form a self‐sensing system, thus providing both precise actuation and dragging‐aware collaboration. Hence, the Soft Co‐bots can be first taught by human drag and then precisely repeat various tasks on their own, such as electronics assembling, machine tool installation, etc. The proposed Soft Co‐bots exhibit a high potential for safe and intuitive human‐robot collaboration in unstructured environments, promoting the immediate practical application of soft robots. [ABSTRACT FROM AUTHOR]

Summary: The sorting efficiency of the warehouse management and handling robots (automated guided vehicles [AGVs]) in an unmanned warehouse affects the delivery and lead time of orders. Studying the collaborative sorting of multiple AGVs can significantly improve work efficiency. The goal of this paper is to minimize the overall shortest transportation path and time while ensuring the completion of all orders and corresponding recycling or return tasks. This paper conducts experiments on global path planning for multiple AGVs based on the A* algorithm, both without considering collision and deadlock situations, and considering collision and deadlock situations. The results show that without considering collision and deadlock situations, the method of matching fixed storage locations and picking stations can improve overall efficiency, while considering collision and deadlock problems will result in reduced efficiency. In the future, the model will be further optimized taking into account actual conditions to improve overall efficiency. [ABSTRACT FROM AUTHOR]

In recent years, Internet of Things (IoT) systems have changed the way we live, work and do businesses in many areas, even those that until recently seemed unlikely. Some areas that can benefit from their application are, among others, healthcare, smart cities, industrial automation, smart agriculture, intelligent transportation systems, smart logistics, and emergency response. This research work proposes a novel alternative that allows the creation of IoT systems capable of making remote changes in devices' communication in a fast and agile manner. Our proposal gives way to some of the most common changes in communication made during the development and maintenance phase in IoT systems, like changing the destination of data transmission, sending the data to multiple destinations, and changing the frequency of sending data. Our solution, which is used in the programs, is loaded on the device. When the device starts, it connects to a configuration server in the background and listens for changes. The changes are sent to the configuration server using specified commands. When a change is detected, the command is processed, and the change in communication is applied without stopping the running program. We designed experiments to evaluate the complexity of the programs developed using our proposal and the actions needed to make a change. [ABSTRACT FROM AUTHOR]

China has experienced a boom of industrial robots in the past decade. Under the shock of robotization on labor market, migration is a critical way to rebalance the economy. While many studies have investigated the influence of robotization on labor market in the automation‐advanced countries, few works shed light on the situation in the emerging market. We provide empirical evidence on the effect of industrial robots on intercity migration in China. We find that, industrial robot adoption has a significant negative effect on the net inflow migration by reducing population inflows, while has little effect on population outflows. The decline in population inflows is concentrated among low‐skilled migrants who are younger, less educated and in manufacturing sectors, because they are more likely to experience unemployment and wage declines in the face of industrial robots. The analysis of migration cost demonstrates that the negative impact of industrial robots on population inflows increases with the longer migration distances, higher living costs, and greater institutional entry barriers caused by Hukou Registration System in China. [ABSTRACT FROM AUTHOR]

In the contemporary field of optimal trajectory planning for industrial robots, it is customary to construct trajectories through the manual predefinition of interpolation functions. Unfortunately, this method frequently overlooks the influence of the interpolation function itself on the optimization objectives, resulting in suboptimal outcomes. To remedy this limitation, an optimal trajectory planning method with coupled interpolation function selection is proposed, in which the total task time and the integral squared jerk are defined as optimization objectives. This method minimizes the optimization objectives while also factoring in the optimal interpolation function, and avoiding subjective interference. To address the aforementioned biobjective optimization problem better, an Improved MultiObjective Golden Eagle Optimizer is introduced. Population diversity and the ability to escape local optima are enhanced through the incorporation of Chaotic Mapping, Opposition‐Based Learning, Differential Evolution, and adaptive inertia weight strategy into the algorithm. The superiority of the algorithm is validated through a series of simulations on 17 benchmark functions. In the context of the robotic stirring operation within the automated block cast charging process, the proposed method is utilized to derive the time–jerk optimal trajectory. The results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The literature and practitioners both emphasise the need to involve universities in the promotion and implementation of SDGs. On the other hand, the lack of practical solutions to include SDGs in university activity is constantly emphasised. The objective of the article is to investigate how project‐based learning innovations used in student education affect the achievement of sustainable development goals. We analyse whether innovative project based–learning projects at a leading technology university in Poland pursued from 2018 to 2021 contribute to achieving sustainable development goals, and we study the matter of interdisciplinarity of the project teams. To this end, we employ a qualitative content analysis of reports for 108 projects that involved 324 researchers, 607 students, and 233 experts from the university's business environment. The categorical data yielded by the analysis are analysed with quantitative methods: frequency analysis, correspondence analysis, and data visualisation. The analyses demonstrated that the most commonly pursued sustainable development goals were: SDG 9: Industry Innovation and Infrastructure, SDG 3: Good Health and Well‐Being, and SDG 12: Responsible Production and Construction. Biomedical Engineering experts pursued projects dedicated to SDG 3: Good Health and Well‐Being, while Automation and Robotics specialists focused on SDGs 9: Industry Innovation and Infrastructure and SDG 11: Sustainable Cities and Communities. The primary sustainable development goal for Environmental Engineering teams was SDG 12: Responsible Consumption and Production followed by SDG 6: Clean Water and Sanitation and 7: Affordable and Clean Energy. The most common interdisciplinarity in the teams was of the second type: interdisciplinarity bringing together experts from two research areas. The article presents the results of research on the process of promoting sustainable development through project‐based learning implemented under an interdisciplinary collaboration of teams of university staff, students, and experts from businesses associated with the university. The results contribute to the academic debate on the ability of universities to teach, implement, and promote sustainable development and its goals. [ABSTRACT FROM AUTHOR]

Industrial robot is a and multi‐output complex system with strong coupling and high nonlinearity. The motion control accuracy of the system is affected by many factors. To solve the difficulty in establishing the input and output characteristics of robot dynamics modeling, the robot motion model is established through the Lagrangian energy function. At the same time, the nonlinear relationship between angular velocity, angular acceleration, and robot torque is accurately expressed through improved cascaded neural network. In addition, the optimal time planning of the robot's trajectory in joint space is studied using multinomial interpolation method and the particle swarm optimization (PSO). In the simulation experiment, the effect of the proposed dynamic model fitting was outstanding. Under the mixed multinomial difference calculation planning, the angular position trajectories of the three joints changed very smoothly. In the data set application test, the average error of the PSO algorithm was 0.4061 mm and the average task time was 9.101 s, which were lower than other planning algorithms. Experiments showed that the Lagrangian dynamic model analysis based on genetic algorithm cascaded neural network and PSO trajectory scheduling method under mixed multinomial difference had better trajectory planning performance in handling tasks. [ABSTRACT FROM AUTHOR]

In this study, a novel autonomous sensing method of multi‐degree‐of‐freedom industrial robot arm trajectory is proposed. The research takes the distance sensor to collect environmental data, and takes the point cloud data scanned by 3D laser as the basis. The environment model of multi‐degree‐of‐freedom industrial robotic arm is established by Iterative Closest Point (ICP). Then the target object is calibrated by binocular imaging technology. Subsequently, angle of each joint of multi‐degree‐of‐freedom industrial robotic arm is calculated to determine the spatial attitude of the robotic arm. In addition, 3D LiDAR is installed at the end of the robotic arm, and the end trajectory of multi‐degree‐of‐freedom industrial robotic arm is sensed autonomously by using the optimal function. The proposed method has advantages of high accuracy and short sensing time in autonomous sensing of multi‐degree‐of‐freedom industrial robot arm trajectory. [ABSTRACT FROM AUTHOR]

This paper presents a new framework for analyzing automation, robotics, and high‐tech, which differs from the canonical model of technological progress by incorporating the higher education system. The main difference is that there is not just one type of skilled workers, but two types, and there is not one type of education but two ‐ elite universities and standard ones. The gap between these two types of education is called 'elitism gap'. The 'elitism gap' in the higher‐education sector enables a separation of individuals by their abilities. Since the economy is divided between low‐tech and high‐tech sectors, the elitism gap leads to a separating equilibrium in which, high‐ability workers graduating from top universities work in the high‐tech sector, while low‐ability workers, graduate from standard universities and work in the low‐tech industries. In consequence, human capital in both industries is different, which leads to wage inequality. We then analyze the effects of an increased use of robotics on inequality. We show that robots affect the "matching effect" between abilities and education, and in consequence, inequality increases. We also show that wages and productivity gaps between high‐tech and low‐tech sectors are fueled by the elitism gap in higher education. This leads to heterogeneity in human capital, and therefore to an increase in wage inequality. We develop an index of the elitism gap, and show a positive correlation between the index of elitism gap and inequality in OECD countries. [ABSTRACT FROM AUTHOR]

With their unique point‐contact ability with the ground and exceptional adaptability to complex terrains, quadruped robots have become a focal point in the fields of automation and robotic engineering. Significant research progress has been made in aspects such as structural design, motion planning, and balance control of these robots. However, a primary challenge in current research lies in further enhancing the dynamic performance, environmental adaptability, and payload capacity. In this article, research achievements in key technical areas of quadruped robots, encompassing structural design, gait planning, traditional control strategies, intelligent control strategies, and autonomous movement, are comprehensively discussed. The focus of the article is on analyzing trends in intelligence and technological innovation within the aforementioned areas, aiming to provide robust theoretical support and forward‐looking technical guidance for quadruped robots research. Additionally, it offers valuable references for scholars engaged in this field. [ABSTRACT FROM AUTHOR]