The challenge of wastewater treatment facilities is growing as they strive to enhance compliance strength and minimize energy consumption,chemical usage,downtime,and labor requirements in the face of increasingly vari...The challenge of wastewater treatment facilities is growing as they strive to enhance compliance strength and minimize energy consumption,chemical usage,downtime,and labor requirements in the face of increasingly variable influent and climate-related disruptions.The use of recent developments in Internet of Things(IoT),artificial intelligence,and robotics enables a transition to a less reactive mode of operation and more closed-loop automation.This review leads to an understanding of the demonstrations of networked sensing and edge data architecture to enhance observability,transform heterogeneous time-series and multimodal data into monitoring,forecasting,and risk intelligent decision knowledge,and extends robotics ability to measure and intervene in hazardous,distributed,or intermittently observed plant environments.We structure the literature on a deployable sense-think-act structure between unit processes,sensing strategies,Artificial Intelligence(AI)tasks,and execution pathways based on supervisory control and robotic operations.The applications of high leverage are evaluated,such as aeration and nutrient removal optimization,chemical dosing and disinfection control,prediction of membrane fouling and cleaning schedules,solids line stabilization,and predictive maintenance of the important assets.In these areas,we highlight aspects of quality of evidence,benchmarking issues,and operational circumstances that will define persistence of reported efficiency improvements after pilots,such as sensor drift and biofouling control,constraint-based control in service of Supervisory Control and Data Acquisition(SCADA)/Programmable Logic Controller(PLC)systems,cybersecurity-by-design,and model life cycle governance.We bring it to the maturity perspective of resilient,interoperable,and conscientiously independent Wastewater Treatment Plants(WWTPs)with a research requirement of standardized datasets,hybrid digital twins,uncertainty intentional optimization,and adaptive sampling and inspection by robotized techniques.展开更多
Soft robots,characterized by compliance,adaptability,and multimodal responsiveness,represent a rapidly advancing frontier in biomedical applications,wearable technologies,and environmental exploration.This review summ...Soft robots,characterized by compliance,adaptability,and multimodal responsiveness,represent a rapidly advancing frontier in biomedical applications,wearable technologies,and environmental exploration.This review summarizes recent progress in soft robotics with a focus on material innovation,structural design,functional integration,and intelligent responsiveness.Emphasis is placed on the development of bioinspired and stimuli-responsive materials,the construction of modular and reconfigurable architectures,and the integration of actuation,sensing,and energy systems.Microneedle array-based soft robots and hydrogel-based 4D-printed systems are introduced as representative platforms for drug delivery,wound healing,and environmental monitoring.Key challenges,including limited durability,power autonomy,and multifunctional synergy,are critically analyzed in relation to practical operation and long-term reliability.Future directions involve the convergence of self-healing materials,intelligent control algorithms,and multiscale integration strategies to achieve enhanced adaptability and clinical translation.This review provides a comprehensive overview of the interdisciplinary development of next-generation soft robots that bridge materials science,biomedical engineering,and intelligent systems,paving the way toward real-world applications.展开更多
Intelligent techniques foster the dissemination of new discoveries and novel technologies that advance the ability of robots to assist and support humans. The human-centered intelligent robot has become an important r...Intelligent techniques foster the dissemination of new discoveries and novel technologies that advance the ability of robots to assist and support humans. The human-centered intelligent robot has become an important research field that spans all of the robot capabilities including navigation, intelligent control, pattern recognition and human-robot interaction. This paper focuses on the recent achievements and presents a survey of existing works on human-centered robots. Furthermore, we provide a comprehensive survey of the recent development of the human-centered intelligent robot and discuss the issues and challenges in the field.展开更多
As embodied intelligence(EI),large language models(LLMs),and cloud computing continue to advance,Industry5.0 facilitates the development of industrial artificial intelligence(Ind AI)through cyber-physical-social syste...As embodied intelligence(EI),large language models(LLMs),and cloud computing continue to advance,Industry5.0 facilitates the development of industrial artificial intelligence(Ind AI)through cyber-physical-social systems(CPSSs)with a human-centric focus.These technologies are organized by the system-wide approach of Industry 5.0,in order to empower the manufacturing industry to achieve broader societal goals of job creation,economic growth,and green production.This survey first provides a general framework of smart manufacturing in the context of Industry 5.0.Wherein,the embodied agents,like robots,sensors,and actuators,are the carriers for Ind AI,facilitating the development of the self-learning intelligence in individual entities,the collaborative intelligence in production lines and factories(smart systems),and the swarm intelligence within industrial clusters(systems of smart systems).Through the framework of CPSSs,the key technologies and their possible applications for supporting the single-agent,multi-agent and swarm-agent embodied Ind AI have been reviewed,such as the embodied perception,interaction,scheduling,multi-mode large language models,and collaborative training.Finally,to stimulate future research in this area,the open challenges and opportunities of applying Industry 5.0 to smart manufacturing are identified and discussed.The perspective of Industry 5.0-driven manufacturing industry aims to enhance operational productivity and efficiency by seamlessly integrating the virtual and physical worlds in a human-centered manner,thereby fostering an intelligent,sustainable,and resilient industrial landscape.展开更多
The special issue aims to address a broad spectrum of topics ranging from human-centered intelligent robots acting as a servant,secretary,or companion to intelligent robotic functions.The special issue publishes origi...The special issue aims to address a broad spectrum of topics ranging from human-centered intelligent robots acting as a servant,secretary,or companion to intelligent robotic functions.The special issue publishes original papers of innovative ideas and concepts,new discoveries,and novel applications and business models relevant to the field of human-centered intelligent robots.In this special issue,modeling,intelligent control,展开更多
Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and langua...Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.展开更多
Robots are key to expanding the scope of space applications.The end-to-end training for robot vision-based detection and precision operations is challenging owing to constraints such as extreme environments and high c...Robots are key to expanding the scope of space applications.The end-to-end training for robot vision-based detection and precision operations is challenging owing to constraints such as extreme environments and high computational overhead.This study proposes a lightweight integrated framework for grasp detection and imitation learning,named GD-IL;it comprises a grasp detection algorithm based on manipulability and Gaussian mixture model(manipulability-GMM),and a grasp trajectory generation algorithm based on a two-stage robot imitation learning algorithm(TS-RIL).In the manipulability-GMM algorithm,we apply GMM clustering and ellipse regression to the object point cloud,propose two judgment criteria to generate multiple candidate grasp bounding boxes for the robot,and use manipulability as a metric for selecting the optimal grasp bounding box.The stages of the TS-RIL algorithm are grasp trajectory learning and robot pose optimization.In the first stage,the robot grasp trajectory is characterized using a second-order dynamic movement primitive model and Gaussian mixture regression(GMM).By adjusting the function form of the forcing term,the robot closely approximates the target-grasping trajectory.In the second stage,a robot pose optimization model is built based on the derived pose error formula and manipulability metric.This model allows the robot to adjust its configuration in real time while grasping,thereby effectively avoiding singularities.Finally,an algorithm verification platform is developed based on a Robot Operating System and a series of comparative experiments are conducted in real-world scenarios.The experimental results demonstrate that GD-IL significantly improves the effectiveness and robustness of grasp detection and trajectory imitation learning,outperforming existing state-of-the-art methods in execution efficiency,manipulability,and success rate.展开更多
The 2025 World Robot Conference,held in Beijing from August 8 to 12,offered a vivid glimpse into the future of the global robotics industry,where breakthroughs in artificial intelligence(AI)are injecting new momentum ...The 2025 World Robot Conference,held in Beijing from August 8 to 12,offered a vivid glimpse into the future of the global robotics industry,where breakthroughs in artificial intelligence(AI)are injecting new momentum into growth.Drawing ove r 1.3 million attendees.展开更多
Generative Artificial Intelligence(GenAI)systems have achieved remarkable capabilities across text,code,and image generation;however,their outputs remain prone to errors,hallucinations,and biases.Users often overtrust...Generative Artificial Intelligence(GenAI)systems have achieved remarkable capabilities across text,code,and image generation;however,their outputs remain prone to errors,hallucinations,and biases.Users often overtrust these outputs due to limited transparency,which can lead to misuse and decision errors.This study addresses the challenge of calibrating trust in GenAI through a human centered testing framework enhanced with adaptive explainability.We introduce a methodology that adjusts explanations dynamically according to user expertise,model output confidence,and contextual risk factors,providing guidance that is informative but not overwhelming.The framework was evaluated using outputs from OpenAI’s Generative Pretrained Transformer 4(GPT-4)for text and code generation and Stable Diffusion,a deep generative image model,for image synthesis.The evaluation covered text,code,and visual modalities.A dataset of 5000 GenAI outputs was created and reviewed by a diverse participant group of 360 individuals categorized by expertise level.Results show that adaptive explanations improve error detection rates,reduce the mean squared trust calibration error,and maintain efficient decision making compared with both static and no explanation conditions.Theframework increased error detection by up to 16% across expertise levels,a gain that can provide practical benefits in high stakes fields.For example,in healthcare it may help identify diagnostic errors earlier,and in law it may prevent reliance on flawed evidence in judicial work.These improvements highlight the framework’s potential to make Artificial Intelligence(AI)deployment safer and more accountable.Visual analyses,including trust accuracy plots,reliability diagrams,and misconception maps,show that the adaptive approach reduces overtrust and reveals patterns of misunderstanding across modalities.Statistical results confirmthe robustness of thesefindings across novice,intermediate,and expert users.The study offers insights for designing explanations that balance completeness and simplicity to improve trust calibration and cognitive load.The approach has implications for safe and transparent GenAI deployment and can inform both AI interface design and policy development for responsible AI use.展开更多
In a late January 2025 program broadcast to a television audience of more than a billion viewers,16 Unitree(Hangzhou,China)H1 humanoid robots joined human performers in a synchronized Yangko dance at the Chinese Sprin...In a late January 2025 program broadcast to a television audience of more than a billion viewers,16 Unitree(Hangzhou,China)H1 humanoid robots joined human performers in a synchronized Yangko dance at the Chinese Spring Festival Gala[1].Each robot featured more than 40 degrees of freedom(DOF)in its limbs,real-time motion planning,and stable gait control using terrainagnostic algorithms.As they danced,the robots mimicked the human dancers in skillfully manipulating handkerchiefs(Fig.1).展开更多
This study presents a systematic review of applications of artificial intelligence(abbreviated as AI)and blockchain in supply chain provenance traceability and legal forensics cover five sectors:integrated circuits(ab...This study presents a systematic review of applications of artificial intelligence(abbreviated as AI)and blockchain in supply chain provenance traceability and legal forensics cover five sectors:integrated circuits(abbreviated as ICs),pharmaceuticals,electric vehicles(abbreviated as EVs),drones(abbreviated as UAVs),and robotics—in response to rising trade tensions and geopolitical conflicts,which have heightened concerns over product origin fraud and information security.While previous literature often focuses on single-industry contexts or isolated technologies,this reviewcomprehensively surveys these sectors and categorizes 116 peer-reviewed studies by application domain,technical architecture,and functional objective.Special attention is given to traceability control mechanisms,data integrity,and the use of forensic technologies to detect origin fraud.The study further evaluates real-world implementations,including blockchain-enabled drug tracking systems,EV battery raw material traceability,and UAV authentication frameworks,demonstrating the practical value of these technologies.By identifying technological challenges and policy implications,this research provides a comprehensive foundation for future academic inquiry,industrial adoption,and regulatory development aimed at enhancing transparency,resilience,and trust in global supply chains.展开更多
The embodied artificial intelligence(EAI)is driving a significant transformation in robotics,enhancing their autonomy,efficiency and evolution ability.In this rapidly evolving technological landscape,robots need numer...The embodied artificial intelligence(EAI)is driving a significant transformation in robotics,enhancing their autonomy,efficiency and evolution ability.In this rapidly evolving technological landscape,robots need numerous sensors to realize high levels of perception,precision,safety,adaptability,and intelligence.Triboelectric and piezoelectric sensors address these needs by providing high sensitivity,flexibility,and the capability of self-powered sensing,leveraging the revolutionary nature of nanogenerators to convert mechanical energy into electrical energy on basis of Maxwell’s displacement current.These sensors surpass externally powered passive sensors by offering continuous operation,reduced maintenance,and the capability to function in remote or harsh environments.The integration of EAI with advanced nanogenerators sensors could position robotics to perform autonomously,efficiently,and safely,paving the way for innovative applications in various domains such as industrial automation,environmental monitoring,healthcare,and smart homes.In this paper,the fundamental theories,design,manufacturing,and applications of nanogenerators are comprehensively reviewed as afoundation of the advanced sensors for intelligent robotics in the new era,with three major application fields:sensing(including human–robot interaction,exteroceptive sensing and proprioceptive sensing),computing and actuating.Perspectives are addressed for nanogenerators systems in future development.展开更多
Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challeng...Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challenge due to factors such as light scattering,absorption,restricted visibility,and ambient noise.The advancement of deep learning has introduced powerful techniques for processing large amounts of unstructured and imperfect data collected from underwater environments.This study evaluated the efficacy of the You Only Look Once(YOLO)algorithm,a real-time object detection and localization model based on convolutional neural networks,in identifying and classifying various types of pipeline defects in underwater settings.YOLOv8,the latest evolution in the YOLO family,integrates advanced capabilities,such as anchor-free detection,a cross-stage partial network backbone for efficient feature extraction,and a feature pyramid network+path aggregation network neck for robust multi-scale object detection,which make it particularly well-suited for complex underwater environments.Due to the lack of suitable open-access datasets for underwater pipeline defects,a custom dataset was captured using a remotely operated vehicle in a controlled environment.This application has the following assets available for use.Extensive experimentation demonstrated that YOLOv8 X-Large consistently outperformed other models in terms of pipe defect detection and classification and achieved a strong balance between precision and recall in identifying pipeline cracks,rust,corners,defective welds,flanges,tapes,and holes.This research establishes the baseline performance of YOLOv8 for underwater defect detection and showcases its potential to enhance the reliability and efficiency of pipeline inspection tasks in challenging underwater environments.展开更多
Extreme environments are unstructured and change rapidly,making human exploration in unfamiliar areas difficult.Construction robotics can help reduce risks to human safety and property in these environments by integra...Extreme environments are unstructured and change rapidly,making human exploration in unfamiliar areas difficult.Construction robotics can help reduce risks to human safety and property in these environments by integrating digital technology and artificial intelligence.This technology has the potential to significantly improve the quality and efficiency of construction,making it a key area for future research.Extreme environments include hazardous work sites,polluted areas,and harsh natural conditions.Our review of construction robotics in these settings highlights several knowledge gaps.We focused on four main areas:mechanism design,perception,planning,and control.Our analysis reveals challenges in practical applications,such as creating adaptable mechanisms,accurately perceiving changing environments,planning for unstructured sites,and optimizing control models.Future research should explore:biomimetic designs inspired by nature,multimodal data fusion for perception,adaptive planning strategies,and hybrid control models that combine data-driven and mechanism-based approaches.展开更多
This review explores the current state and future prospects of tactile sensing technologies in space robotics,addressing the unique challenges posed by harsh space environments such as extreme temperatures,radiation,m...This review explores the current state and future prospects of tactile sensing technologies in space robotics,addressing the unique challenges posed by harsh space environments such as extreme temperatures,radiation,microgravity,and vacuum conditions,which necessitate specialized sensor designs.We provide a detailed analysis of four primary types of tactile sensors:resistive,capacitive,piezoelectric,and optical,evaluating their operating principles,advantages,limitations,and specific applications in space exploration.Recent advancements in materials science,including the development of radiation-hardened components and flexible sensor materials,are discussed alongside innovations in sensor design and integration techniques that enhance performance and durability under space conditions.Through case studies of various space robotic systems,such as Mars rovers,robotic arms like Canadarm,humanoid robots like Robonaut,and specialized robots like Astrobee and LEMUR 3,this review highlights the crucial role of tactile sensing in enabling precise manipulation,environmental interaction,and autonomous operations in space.Moreover,it synthesizes current research and applications to underscore the transformative impact of tactile sensing technologies on space robotics and highlights their pivotal role in expanding human presence and scientific understanding in space,offering strategic insights and recommendations to guide future research and development in this critical field.展开更多
1.Introduction The continuous integration of advanced technologies into medicine has brought profound changes across nearly all specialties.In urology,a field traditionally characterized by its reliance on delicate,pr...1.Introduction The continuous integration of advanced technologies into medicine has brought profound changes across nearly all specialties.In urology,a field traditionally characterized by its reliance on delicate,precision-driven procedures,the impact of innovations such as robotics,artificial intelligence(AI),telepresence,and telesurgery has been transformative.展开更多
With the acceleration of the global aging process and the increase of cardiovascular ancerebrovascular diseases,more and more patients are paralyzed due to accidents,so theexoskeleton robot began to appear in people&#...With the acceleration of the global aging process and the increase of cardiovascular ancerebrovascular diseases,more and more patients are paralyzed due to accidents,so theexoskeleton robot began to appear in people's sight,and the lower limb exoskeleton robot withrehabilitation training is also favored by more and more people.In this paper,the structural designand analysis of the lower limb exoskeleton robot are carried out in view of the patients'expectation ofnormal walking.First,gait analysis and structural design of lower limb exoskeleton robot.Based onthe analysis of the walking gait of normal people,the freedom of the three key joints of the lower limbexoskeleton robot hip joint,knee joint and ankle joint is determined.at the same time,according tothe structuralcharacteristics of each joint,the three key joints are modeled respectively,and theoverall model assembly of the lower limb exoskeleton robot is completed.Secondly,the kinematicsanalysis of the lower limb exoskeleton robot was carried out to obtain the relationship between thelinear displacement,linear speed and acceleration of each joint,so as to ensure the coordination ofthe model with the human lower limb movement.Thirdly,the static analysis of typical gait of hipjoint,knee joint and ankle joint is carried out to verify the safety of the design model under thepremise of ensuring the structural strength requirements.Finally,the parts of the model were 3Dprinted,and the rationality of the design was further verified in the process of assembling the model.展开更多
This review synthesises and assesses the most recent developments in Unmanned Aerial Vehicles(UAVs)and swarm robotics,with a specific emphasis on optimisation strategies,path planning,and formation control.The study i...This review synthesises and assesses the most recent developments in Unmanned Aerial Vehicles(UAVs)and swarm robotics,with a specific emphasis on optimisation strategies,path planning,and formation control.The study identifies key methodologies that are driving progress in the field by conducting a comprehensive analysis of seven critical publications.The following are included:sensor-based platforms that facilitate effective obstacle avoidance,cluster-based hierarchical path planning for efficient navigation,and adaptive hybrid controllers for dynamic environments.The review emphasises the substantial contribution of optimisation techniques,including Max-Min Ant Colony Optimisation(MMACO),to the improvement of convergence rates and the enhancement of path efficiency.The effectiveness of various navigation systems in diverse operational contexts is demonstrated through comparative analysis,which provides valuable insights into the system’s adaptability and performance.The primary findings underscore the strengths and limitations of current methodologies,thereby identifying voids in research and practical applications.This review offers actionable insights for academicians and practitioners who are striving to advance UAV and swarm robotics technology by addressing these challenges.The study concludes with a discussion of future directions,which underscores the potential for innovative solutions to enhance UAV systems in complex,dynamic environments.展开更多
文摘The challenge of wastewater treatment facilities is growing as they strive to enhance compliance strength and minimize energy consumption,chemical usage,downtime,and labor requirements in the face of increasingly variable influent and climate-related disruptions.The use of recent developments in Internet of Things(IoT),artificial intelligence,and robotics enables a transition to a less reactive mode of operation and more closed-loop automation.This review leads to an understanding of the demonstrations of networked sensing and edge data architecture to enhance observability,transform heterogeneous time-series and multimodal data into monitoring,forecasting,and risk intelligent decision knowledge,and extends robotics ability to measure and intervene in hazardous,distributed,or intermittently observed plant environments.We structure the literature on a deployable sense-think-act structure between unit processes,sensing strategies,Artificial Intelligence(AI)tasks,and execution pathways based on supervisory control and robotic operations.The applications of high leverage are evaluated,such as aeration and nutrient removal optimization,chemical dosing and disinfection control,prediction of membrane fouling and cleaning schedules,solids line stabilization,and predictive maintenance of the important assets.In these areas,we highlight aspects of quality of evidence,benchmarking issues,and operational circumstances that will define persistence of reported efficiency improvements after pilots,such as sensor drift and biofouling control,constraint-based control in service of Supervisory Control and Data Acquisition(SCADA)/Programmable Logic Controller(PLC)systems,cybersecurity-by-design,and model life cycle governance.We bring it to the maturity perspective of resilient,interoperable,and conscientiously independent Wastewater Treatment Plants(WWTPs)with a research requirement of standardized datasets,hybrid digital twins,uncertainty intentional optimization,and adaptive sampling and inspection by robotized techniques.
基金financial support from the National Key Research and Development Program of China(2024YFA0919100)the National Natural Science Foundation of China(32371435)+2 种基金the Qinglan Project of Jiangsu Province(2025 Excellent Young Scholar,Bingbing Gao)the Jiangsu government scholarship for overseas studies(Bingbing Gao)the Nanjing Tech University Teaching Reform Project(20250281)。
文摘Soft robots,characterized by compliance,adaptability,and multimodal responsiveness,represent a rapidly advancing frontier in biomedical applications,wearable technologies,and environmental exploration.This review summarizes recent progress in soft robotics with a focus on material innovation,structural design,functional integration,and intelligent responsiveness.Emphasis is placed on the development of bioinspired and stimuli-responsive materials,the construction of modular and reconfigurable architectures,and the integration of actuation,sensing,and energy systems.Microneedle array-based soft robots and hydrogel-based 4D-printed systems are introduced as representative platforms for drug delivery,wound healing,and environmental monitoring.Key challenges,including limited durability,power autonomy,and multifunctional synergy,are critically analyzed in relation to practical operation and long-term reliability.Future directions involve the convergence of self-healing materials,intelligent control algorithms,and multiscale integration strategies to achieve enhanced adaptability and clinical translation.This review provides a comprehensive overview of the interdisciplinary development of next-generation soft robots that bridge materials science,biomedical engineering,and intelligent systems,paving the way toward real-world applications.
基金supported in part by the National Natural Science Foundation of China(61573147,91520201,61625303,61522302,61761130080)Guangzhou Research Collaborative Innovation Projects(2014Y2-00507)+2 种基金Guangdong Science and Technology Research Collaborative Innovation Projects(20138010102010,20148090901056,20158020214003)Guangdong Science and Technology Plan Project(Application Technology Research Foundation)(2015B020233006)National High-Tech Research and De-velopment Program of China(863 Program)(2015AA042303)
文摘Intelligent techniques foster the dissemination of new discoveries and novel technologies that advance the ability of robots to assist and support humans. The human-centered intelligent robot has become an important research field that spans all of the robot capabilities including navigation, intelligent control, pattern recognition and human-robot interaction. This paper focuses on the recent achievements and presents a survey of existing works on human-centered robots. Furthermore, we provide a comprehensive survey of the recent development of the human-centered intelligent robot and discuss the issues and challenges in the field.
基金supported by the National Key Research and Development Program of China(2021YFB1714300)the National Natural Science Foundation of China(62233005,U2441245,62173141)+3 种基金CNPC Innovation Found(2024DQ02-0507)Shanghai Natural Science(24ZR1416400)Shanghai Baiyu Lan Talent Program Pujiang Project(24PJD020)the Programme of Introducing Talents of Discipline to Universities(the 111 Project)(B17017)
文摘As embodied intelligence(EI),large language models(LLMs),and cloud computing continue to advance,Industry5.0 facilitates the development of industrial artificial intelligence(Ind AI)through cyber-physical-social systems(CPSSs)with a human-centric focus.These technologies are organized by the system-wide approach of Industry 5.0,in order to empower the manufacturing industry to achieve broader societal goals of job creation,economic growth,and green production.This survey first provides a general framework of smart manufacturing in the context of Industry 5.0.Wherein,the embodied agents,like robots,sensors,and actuators,are the carriers for Ind AI,facilitating the development of the self-learning intelligence in individual entities,the collaborative intelligence in production lines and factories(smart systems),and the swarm intelligence within industrial clusters(systems of smart systems).Through the framework of CPSSs,the key technologies and their possible applications for supporting the single-agent,multi-agent and swarm-agent embodied Ind AI have been reviewed,such as the embodied perception,interaction,scheduling,multi-mode large language models,and collaborative training.Finally,to stimulate future research in this area,the open challenges and opportunities of applying Industry 5.0 to smart manufacturing are identified and discussed.The perspective of Industry 5.0-driven manufacturing industry aims to enhance operational productivity and efficiency by seamlessly integrating the virtual and physical worlds in a human-centered manner,thereby fostering an intelligent,sustainable,and resilient industrial landscape.
文摘The special issue aims to address a broad spectrum of topics ranging from human-centered intelligent robots acting as a servant,secretary,or companion to intelligent robotic functions.The special issue publishes original papers of innovative ideas and concepts,new discoveries,and novel applications and business models relevant to the field of human-centered intelligent robots.In this special issue,modeling,intelligent control,
基金supported by National Natural Science Foundation of China(62376219 and 62006194)Foundational Research Project in Specialized Discipline(Grant No.G2024WD0146)Faculty Construction Project(Grant No.24GH0201148).
文摘Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.
基金Supported by National Natural Science Foundation of China(Grant No.52475280)Shaanxi Provincial Natural Science Basic Research Program(Grant No.2025SYSSYSZD-105).
文摘Robots are key to expanding the scope of space applications.The end-to-end training for robot vision-based detection and precision operations is challenging owing to constraints such as extreme environments and high computational overhead.This study proposes a lightweight integrated framework for grasp detection and imitation learning,named GD-IL;it comprises a grasp detection algorithm based on manipulability and Gaussian mixture model(manipulability-GMM),and a grasp trajectory generation algorithm based on a two-stage robot imitation learning algorithm(TS-RIL).In the manipulability-GMM algorithm,we apply GMM clustering and ellipse regression to the object point cloud,propose two judgment criteria to generate multiple candidate grasp bounding boxes for the robot,and use manipulability as a metric for selecting the optimal grasp bounding box.The stages of the TS-RIL algorithm are grasp trajectory learning and robot pose optimization.In the first stage,the robot grasp trajectory is characterized using a second-order dynamic movement primitive model and Gaussian mixture regression(GMM).By adjusting the function form of the forcing term,the robot closely approximates the target-grasping trajectory.In the second stage,a robot pose optimization model is built based on the derived pose error formula and manipulability metric.This model allows the robot to adjust its configuration in real time while grasping,thereby effectively avoiding singularities.Finally,an algorithm verification platform is developed based on a Robot Operating System and a series of comparative experiments are conducted in real-world scenarios.The experimental results demonstrate that GD-IL significantly improves the effectiveness and robustness of grasp detection and trajectory imitation learning,outperforming existing state-of-the-art methods in execution efficiency,manipulability,and success rate.
文摘The 2025 World Robot Conference,held in Beijing from August 8 to 12,offered a vivid glimpse into the future of the global robotics industry,where breakthroughs in artificial intelligence(AI)are injecting new momentum into growth.Drawing ove r 1.3 million attendees.
文摘Generative Artificial Intelligence(GenAI)systems have achieved remarkable capabilities across text,code,and image generation;however,their outputs remain prone to errors,hallucinations,and biases.Users often overtrust these outputs due to limited transparency,which can lead to misuse and decision errors.This study addresses the challenge of calibrating trust in GenAI through a human centered testing framework enhanced with adaptive explainability.We introduce a methodology that adjusts explanations dynamically according to user expertise,model output confidence,and contextual risk factors,providing guidance that is informative but not overwhelming.The framework was evaluated using outputs from OpenAI’s Generative Pretrained Transformer 4(GPT-4)for text and code generation and Stable Diffusion,a deep generative image model,for image synthesis.The evaluation covered text,code,and visual modalities.A dataset of 5000 GenAI outputs was created and reviewed by a diverse participant group of 360 individuals categorized by expertise level.Results show that adaptive explanations improve error detection rates,reduce the mean squared trust calibration error,and maintain efficient decision making compared with both static and no explanation conditions.Theframework increased error detection by up to 16% across expertise levels,a gain that can provide practical benefits in high stakes fields.For example,in healthcare it may help identify diagnostic errors earlier,and in law it may prevent reliance on flawed evidence in judicial work.These improvements highlight the framework’s potential to make Artificial Intelligence(AI)deployment safer and more accountable.Visual analyses,including trust accuracy plots,reliability diagrams,and misconception maps,show that the adaptive approach reduces overtrust and reveals patterns of misunderstanding across modalities.Statistical results confirmthe robustness of thesefindings across novice,intermediate,and expert users.The study offers insights for designing explanations that balance completeness and simplicity to improve trust calibration and cognitive load.The approach has implications for safe and transparent GenAI deployment and can inform both AI interface design and policy development for responsible AI use.
文摘In a late January 2025 program broadcast to a television audience of more than a billion viewers,16 Unitree(Hangzhou,China)H1 humanoid robots joined human performers in a synchronized Yangko dance at the Chinese Spring Festival Gala[1].Each robot featured more than 40 degrees of freedom(DOF)in its limbs,real-time motion planning,and stable gait control using terrainagnostic algorithms.As they danced,the robots mimicked the human dancers in skillfully manipulating handkerchiefs(Fig.1).
文摘This study presents a systematic review of applications of artificial intelligence(abbreviated as AI)and blockchain in supply chain provenance traceability and legal forensics cover five sectors:integrated circuits(abbreviated as ICs),pharmaceuticals,electric vehicles(abbreviated as EVs),drones(abbreviated as UAVs),and robotics—in response to rising trade tensions and geopolitical conflicts,which have heightened concerns over product origin fraud and information security.While previous literature often focuses on single-industry contexts or isolated technologies,this reviewcomprehensively surveys these sectors and categorizes 116 peer-reviewed studies by application domain,technical architecture,and functional objective.Special attention is given to traceability control mechanisms,data integrity,and the use of forensic technologies to detect origin fraud.The study further evaluates real-world implementations,including blockchain-enabled drug tracking systems,EV battery raw material traceability,and UAV authentication frameworks,demonstrating the practical value of these technologies.By identifying technological challenges and policy implications,this research provides a comprehensive foundation for future academic inquiry,industrial adoption,and regulatory development aimed at enhancing transparency,resilience,and trust in global supply chains.
基金supported by the National Natural Science Foundation of China(Grants Nos.62104125and 62311530102)Shenzhen Science and Technology Program(Grant Nos.JCYJ20220530143013030 and JCYJ20240813111910014)+1 种基金Guangdong Innovative and Entrepreneurial Research Team Program(Grant No.2021ZT09L197)Tsinghua Shenzhen International Graduate School-Shenzhen Pengrui Young Faculty Program of Shenzhen Pengrui Foundation(Grant No.SZPR2023005)。
文摘The embodied artificial intelligence(EAI)is driving a significant transformation in robotics,enhancing their autonomy,efficiency and evolution ability.In this rapidly evolving technological landscape,robots need numerous sensors to realize high levels of perception,precision,safety,adaptability,and intelligence.Triboelectric and piezoelectric sensors address these needs by providing high sensitivity,flexibility,and the capability of self-powered sensing,leveraging the revolutionary nature of nanogenerators to convert mechanical energy into electrical energy on basis of Maxwell’s displacement current.These sensors surpass externally powered passive sensors by offering continuous operation,reduced maintenance,and the capability to function in remote or harsh environments.The integration of EAI with advanced nanogenerators sensors could position robotics to perform autonomously,efficiently,and safely,paving the way for innovative applications in various domains such as industrial automation,environmental monitoring,healthcare,and smart homes.In this paper,the fundamental theories,design,manufacturing,and applications of nanogenerators are comprehensively reviewed as afoundation of the advanced sensors for intelligent robotics in the new era,with three major application fields:sensing(including human–robot interaction,exteroceptive sensing and proprioceptive sensing),computing and actuating.Perspectives are addressed for nanogenerators systems in future development.
文摘Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challenge due to factors such as light scattering,absorption,restricted visibility,and ambient noise.The advancement of deep learning has introduced powerful techniques for processing large amounts of unstructured and imperfect data collected from underwater environments.This study evaluated the efficacy of the You Only Look Once(YOLO)algorithm,a real-time object detection and localization model based on convolutional neural networks,in identifying and classifying various types of pipeline defects in underwater settings.YOLOv8,the latest evolution in the YOLO family,integrates advanced capabilities,such as anchor-free detection,a cross-stage partial network backbone for efficient feature extraction,and a feature pyramid network+path aggregation network neck for robust multi-scale object detection,which make it particularly well-suited for complex underwater environments.Due to the lack of suitable open-access datasets for underwater pipeline defects,a custom dataset was captured using a remotely operated vehicle in a controlled environment.This application has the following assets available for use.Extensive experimentation demonstrated that YOLOv8 X-Large consistently outperformed other models in terms of pipe defect detection and classification and achieved a strong balance between precision and recall in identifying pipeline cracks,rust,corners,defective welds,flanges,tapes,and holes.This research establishes the baseline performance of YOLOv8 for underwater defect detection and showcases its potential to enhance the reliability and efficiency of pipeline inspection tasks in challenging underwater environments.
基金supported in the Strategic Research and Consulting Project of the Chinese Academy of Engineering(2023-XZ-90 and 2023-JB-09-10)the National Key Research and Development Program of China(2021YFF0500301 and 2023YFB3711300)+1 种基金the National Natural Science Foundation of China(72171092 and 71821001)the Natural Science Fund for Distinguished Young Scholars of Hubei Province(2021CFA091).
文摘Extreme environments are unstructured and change rapidly,making human exploration in unfamiliar areas difficult.Construction robotics can help reduce risks to human safety and property in these environments by integrating digital technology and artificial intelligence.This technology has the potential to significantly improve the quality and efficiency of construction,making it a key area for future research.Extreme environments include hazardous work sites,polluted areas,and harsh natural conditions.Our review of construction robotics in these settings highlights several knowledge gaps.We focused on four main areas:mechanism design,perception,planning,and control.Our analysis reveals challenges in practical applications,such as creating adaptable mechanisms,accurately perceiving changing environments,planning for unstructured sites,and optimizing control models.Future research should explore:biomimetic designs inspired by nature,multimodal data fusion for perception,adaptive planning strategies,and hybrid control models that combine data-driven and mechanism-based approaches.
基金supported by FAST(19FAYORA14)of the Canadian Space Agency,Discovery Grant(RGPIN2024-06290)supported by CREATE grant(555425-2021)&Discovery grant(RGPIN-2024-06290)of the Natural Sciences and Engineering Research Council of Canada.
文摘This review explores the current state and future prospects of tactile sensing technologies in space robotics,addressing the unique challenges posed by harsh space environments such as extreme temperatures,radiation,microgravity,and vacuum conditions,which necessitate specialized sensor designs.We provide a detailed analysis of four primary types of tactile sensors:resistive,capacitive,piezoelectric,and optical,evaluating their operating principles,advantages,limitations,and specific applications in space exploration.Recent advancements in materials science,including the development of radiation-hardened components and flexible sensor materials,are discussed alongside innovations in sensor design and integration techniques that enhance performance and durability under space conditions.Through case studies of various space robotic systems,such as Mars rovers,robotic arms like Canadarm,humanoid robots like Robonaut,and specialized robots like Astrobee and LEMUR 3,this review highlights the crucial role of tactile sensing in enabling precise manipulation,environmental interaction,and autonomous operations in space.Moreover,it synthesizes current research and applications to underscore the transformative impact of tactile sensing technologies on space robotics and highlights their pivotal role in expanding human presence and scientific understanding in space,offering strategic insights and recommendations to guide future research and development in this critical field.
文摘1.Introduction The continuous integration of advanced technologies into medicine has brought profound changes across nearly all specialties.In urology,a field traditionally characterized by its reliance on delicate,precision-driven procedures,the impact of innovations such as robotics,artificial intelligence(AI),telepresence,and telesurgery has been transformative.
基金College Student Innovation andEntrepreneurship Project(Grant No.:S202414435026)ingkou Institute of Technology campus level research project——Development of food additive supercritical extraction equipment and fluid transmission systemresearch(Grant No.HX202427).
文摘With the acceleration of the global aging process and the increase of cardiovascular ancerebrovascular diseases,more and more patients are paralyzed due to accidents,so theexoskeleton robot began to appear in people's sight,and the lower limb exoskeleton robot withrehabilitation training is also favored by more and more people.In this paper,the structural designand analysis of the lower limb exoskeleton robot are carried out in view of the patients'expectation ofnormal walking.First,gait analysis and structural design of lower limb exoskeleton robot.Based onthe analysis of the walking gait of normal people,the freedom of the three key joints of the lower limbexoskeleton robot hip joint,knee joint and ankle joint is determined.at the same time,according tothe structuralcharacteristics of each joint,the three key joints are modeled respectively,and theoverall model assembly of the lower limb exoskeleton robot is completed.Secondly,the kinematicsanalysis of the lower limb exoskeleton robot was carried out to obtain the relationship between thelinear displacement,linear speed and acceleration of each joint,so as to ensure the coordination ofthe model with the human lower limb movement.Thirdly,the static analysis of typical gait of hipjoint,knee joint and ankle joint is carried out to verify the safety of the design model under thepremise of ensuring the structural strength requirements.Finally,the parts of the model were 3Dprinted,and the rationality of the design was further verified in the process of assembling the model.
文摘This review synthesises and assesses the most recent developments in Unmanned Aerial Vehicles(UAVs)and swarm robotics,with a specific emphasis on optimisation strategies,path planning,and formation control.The study identifies key methodologies that are driving progress in the field by conducting a comprehensive analysis of seven critical publications.The following are included:sensor-based platforms that facilitate effective obstacle avoidance,cluster-based hierarchical path planning for efficient navigation,and adaptive hybrid controllers for dynamic environments.The review emphasises the substantial contribution of optimisation techniques,including Max-Min Ant Colony Optimisation(MMACO),to the improvement of convergence rates and the enhancement of path efficiency.The effectiveness of various navigation systems in diverse operational contexts is demonstrated through comparative analysis,which provides valuable insights into the system’s adaptability and performance.The primary findings underscore the strengths and limitations of current methodologies,thereby identifying voids in research and practical applications.This review offers actionable insights for academicians and practitioners who are striving to advance UAV and swarm robotics technology by addressing these challenges.The study concludes with a discussion of future directions,which underscores the potential for innovative solutions to enhance UAV systems in complex,dynamic environments.
