As human–robot interaction(HRI)technology advances,dexterous robotic hands are playing a dual role—serving both as tools for manipulation and as channels for non-verbal communication.While much of the existing resea...As human–robot interaction(HRI)technology advances,dexterous robotic hands are playing a dual role—serving both as tools for manipulation and as channels for non-verbal communication.While much of the existing research emphasizes improving grasping and structural dexterity,the semantic dimension of gestures and its impact on user experience has been relatively overlooked.Studies from HRI and cognitive psychology consistently show that the naturalness and cognitive empathy of gestures significantly influence user trust,satisfaction,and engagement.This shift reflects a broader transition from mechanically driven designs toward cognitively empathic interactions—robots’ability to infer human affect,intent,and social context to generate appropriate nonverbal responses.In this paper,we argue that large language models(LLMs)enable a paradigm shift in gesture control—from rule-based execution to semantic-driven,context-aware generation.By leveraging LLMs and visual-language models,robots can interpret environmental and social cues,dynamically map emotions,and generate gestures aligned with human communication norms.We conducted a comprehensive review of research in dexterous hand mechanics,gesture semantics,and user experience evaluation,integrating insights from linguistics and cognitive science.Furthermore,we propose a closed-loop framework—"perception–cognition–generation–assessment"—to guide gesture design through iterative,multimodal feedback.This framework lays the conceptual foundation for building universal,adaptive,and emotionally intelligent gesture systems in future human–robot interaction.展开更多
The research on legged robots attracted much attention both from the academia and industry. Legged robots are multi-input multi-output with multiple end-e ector systems. Therefore,the mechanical design and control fra...The research on legged robots attracted much attention both from the academia and industry. Legged robots are multi-input multi-output with multiple end-e ector systems. Therefore,the mechanical design and control framework are challenging issues. This paper reviews the development of type synthesis and behavior control on legged robots; introduces the hexapod robots developed in our research group based on the proposed type synthesis method. The control framework for legged robots includes data driven layer,robot behavior layer and robot execution layer. Each layer consists several components which are explained in details. Finally,various experiments were conducted on several hexapod robots. The summarization of the type synthesis and behavior control design constructed in this paper would provide a unified platform for communications and references for future advancement for legged robots.展开更多
The existing fixed gait lower limb rehabilitation robots perform a predetermined walking trajectory for patients,ignoring their residual muscle strength.To enhance patient participation and safety in training,this pap...The existing fixed gait lower limb rehabilitation robots perform a predetermined walking trajectory for patients,ignoring their residual muscle strength.To enhance patient participation and safety in training,this paper aims to develop a lower limb rehabilitation robot with adaptive gait training capability relying on human–robot interaction force measurement.Firstly,a novel lower limb rehabilitation robot system with several active and passive driven joints is developed,and 2 face-to-face mounted cantilever beam force sensors are employed to measure the human–robot interaction forces.Secondly,a dynamic model of the rehabilitation training robot is constructed to estimate the driven forces of the human lower leg in a completely passive state.Thereafter,based on the theoretical moment from the dynamics and the actual joint interaction force collected by the sensors,an adaptive gait adjustment method is proposed to achieve the goal of adapting to the wearer’s movement intention.Finally,interactive experiments are carried out to validate the effectiveness of the developed rehabilitation training robot system.The proposed rehabilitation training robot system with adaptive gaits offers great potential for future highquality rehabilitation training,e.g.,improving participation and safety.展开更多
In recent years,humanoid robots have gained significant attention due to their potential to revolutionize various industries,from healthcare to manufacturing.A key factor driving this transformation is the advancement...In recent years,humanoid robots have gained significant attention due to their potential to revolutionize various industries,from healthcare to manufacturing.A key factor driving this transformation is the advancement of visual perception systems,which are crucial for making humanoid robots more intelligent and autonomous.Despite the progress,the full potential of vision-based technologies in humanoid robots has yet to be fully realized.This review aims to provide a comprehensive overview of recent advancements in visual perception applied to humanoid robots,specifically focusing on applications in state estimation and environmental interaction.By summarizing key developments and analyzing the challenges and opportunities in these areas,this paper seeks to inspire future research that can unlock new capabilities for humanoid robots,enabling them to better navigate complex environments,perform intricate tasks,and interact seamlessly with humans.展开更多
Telekinesis,as commonly portrayed in science fiction literature and cinema,is a super power wherein users control and manipulate objects absent in physical interaction.In real world,enhancing human–robot interaction ...Telekinesis,as commonly portrayed in science fiction literature and cinema,is a super power wherein users control and manipulate objects absent in physical interaction.In real world,enhancing human–robot interaction needs the synthesis of human intuitive processes with robotic arm.This paper introduces a robotic teleoperation system achieving the essence of telekinetic operations,combining the profound capabilities of augmented reality(AR)with the robotic arm operations.Utilizing AR,the proposed methodology offers operators with a visual feedback,facilitating a level of control surpassing the capacities of natural interfaces.By using AR-driven visual recognition,this system achieves operations in a virtual environment,subsequently actualized in the real world through the robotic arm.Through multiple experiments,we found that the system has a small margin of error in telekinesis operations,meeting the needs of remote operation.Furthermore,our system can operate on objects in the real world.These experiments underscore the capability of the remote control system to assist humans in accomplishing a wider range of tasks through the integration of AR and robotic arms,providing a natural human–robot interaction approach.展开更多
In human interactions,social touch communication is widely used to convey emotions,emphasizing its critical role in advancing human–robot interactions by enabling robots to understand and respond to human emotions,th...In human interactions,social touch communication is widely used to convey emotions,emphasizing its critical role in advancing human–robot interactions by enabling robots to understand and respond to human emotions,thereby significantly enhancing their service capabilities.However,the challenge is to dynamically capture social touch with sufficient spatiotemporal and mechanical resolution for deep haptic data analysis.This study presents a robotic system with flexible electronic skin and a high-frequency signal circuit,utilizing deep neural networks to recognize social touch emotions.The electronic skin,made from double cross-linked ionogels and microstructured arrays,has a low force detection threshold(8 Pa)and a wide perception range(0-150 kPa),enhancing the mechanical resolution of touch signals.By incorporating a high-speed readout circuit capable of capturing spatiotemporal features of social touch gesture information at 30 Hz,the system facilitates precise analysis of touch interactions.A 3D convolutional neural network with a Squeeze-and-Excitation Attention module achieves 87.12% accuracy in recognizing social touch gestures,improving the understanding of emotions conveyed through touch.The effectiveness of the system is validated through interactive demonstrations with robotic dogs and humanoid robots,demonstrating its potential to enhance the emotional intelligence of robots.展开更多
Background The uncanny valley hypothesis states that users may experience discomfort when inter-acting with almost human-like artificial characters.Advancements in artificial intelligence,robotics,and computer graphic...Background The uncanny valley hypothesis states that users may experience discomfort when inter-acting with almost human-like artificial characters.Advancements in artificial intelligence,robotics,and computer graphics have led to the development of life-like virtual humans and humanoid robots.Revisiting this hypothesis is necessary to check whether they positively or negatively affect the current population,who are highly accustomed to the latest technologies.Methods In this study,we present a unique evaluation of the uncanny valley hypothesis by allowing participants to interact live with four humanoid robots that have varying levels of human-likeness.Each participant completed a survey questionnaire to evaluate the affinity of each robot.Additionally,we used deep learning methods to quantify the participants’emotional states using multimodal cues,including visual,audio,and text cues,by recording the participant-robot interactions.Results Multi-modal analysis and surveys provided interesting results and insights into the uncanny valley hypothesis.展开更多
Q:Dear Mr Sam,the robot has become a hot issue in our lives.What are your ideas about robots working in restaurants?A:I love robots,and I love people.But you know what I love most?People use robots to make life better.
A novel system for human following using a differential robot,including an accurate 3‐D human position tracking module and a novel planning strategy that ensures safety and dynamic feasibility,is proposed.The authors...A novel system for human following using a differential robot,including an accurate 3‐D human position tracking module and a novel planning strategy that ensures safety and dynamic feasibility,is proposed.The authors utilise a combination of gimbal camera and LiDAR for long‐term accurate human detection.Then the planning module takes the target's future trajectory as a reference to generate a coarse path to ensure the following visibility.After that,the trajectory is optimised considering other constraints and following distance.Experiments demonstrate the robustness and efficiency of our system in complex environments,demonstrating its potential in various applications.展开更多
Nuclear facilities have a regulatory requirement to measure radiation levels within Post Operational Clean Out(POCO)around nuclear facilities each year,resulting in a trend towards robotic deployments to gain an impro...Nuclear facilities have a regulatory requirement to measure radiation levels within Post Operational Clean Out(POCO)around nuclear facilities each year,resulting in a trend towards robotic deployments to gain an improved understanding during nuclear decommissioning phases.The UK Nuclear Decommissioning Authority supports the view that human-in-the-loop(HITL)robotic deployments are a solution to improve procedures and reduce risks within radiation characterisation of nuclear sites.The authors present a novel implementation of a Cyber-Physical System(CPS)deployed in an analogue nuclear environment,comprised of a multi-robot(MR)team coordinated by a HITL operator through a digital twin interface.The development of the CPS created efficient partnerships across systems including robots,digital systems and human.This was presented as a multi-staged mission within an inspection scenario for the hetero-geneous Symbiotic Multi-Robot Fleet(SMuRF).Symbiotic interactions were achieved across the SMuRF where robots utilised automated collaborative governance to work together,where a single robot would face challenges in full characterisation of radiation.Key contributions include the demonstration of symbiotic autonomy and query-based learning of an autonomous mission supporting scalable autonomy and autonomy as a service.The coordination of the CPS was a success and displayed further challenges and improvements related to future MR fleets.展开更多
Shared control of mobile robots integrates manual input with auxiliary autonomous controllers to improve the overall system performance.However,prior work that seeks to find the optimal shared control ratio needs an a...Shared control of mobile robots integrates manual input with auxiliary autonomous controllers to improve the overall system performance.However,prior work that seeks to find the optimal shared control ratio needs an accurate human model,which is usually challenging to obtain.In this study,the authors develop an extended Twin Delayed Deep Deterministic Policy Gradient(DDPG)(TD3X)-based shared control framework that learns to assist a human operator in teleoperating mobile robots optimally.The robot's states,shared control ratio in the previous time step,and human's control input is used as inputs to the reinforcement learning(RL)agent,which then outputs the optimal shared control ratio between human input and autonomous controllers without knowing the human model.Noisy softmax policies are developed to make the TD3X algorithm feasible under the constraint of a shared control ratio.Furthermore,to accelerate the training process and protect the robot,a navigation demonstration policy and a safety guard are developed.A neural network(NN)structure is developed to maintain the correlation of sensor readings among heterogeneous input data and improve the learning speed.In addition,an extended DAGGER(DAGGERX)human agent is developed for training the RL agent to reduce human workload.Robot simulations and experiments with humans in the loop are conducted.The results show that the DAGGERX human agent can simulate real human inputs in the worst-case scenarios with a mean square error of 0.0039.Compared to the original TD3 agent,the TD3X-based shared control system decreased the average collision number from 387.3 to 44.4 in a simplistic environment and 394.2 to 171.2 in a more complex environment.The maximum average return increased from 1043 to 1187 with a faster converge speed in the simplistic environment,while the performance is equally good in the complex environment because of the use of an advanced human agent.In the human subject tests,participants'average perceived workload was significantly lower in shared control than that in exclusively manual control(26.90 vs.40.07,p=0.013).展开更多
In the context of collaborative robotics,distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective ...In the context of collaborative robotics,distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support.This is particularly important in applications pertaining to emergency rescue and crisis management.During operational missions,data and knowledge are gathered incrementally and in different ways by heterogeneous robots and humans.We describe this as the creation of Hastily Formed Knowledge Networks(HFKNs).The focus of this paper is the specification and prototyping of a general distributed system architecture that supports the creation of HFKNs by teams of robots and humans.The information collected ranges from low-level sensor data to high-level semantic knowledge,the latter represented in part as RDF Graphs.The framework includes a synchronization protocol and associated algorithms that allow for the automatic distribution and sharing of data and knowledge between agents.This is done through the distributed synchronization of RDF Graphs shared between agents.High-level semantic queries specified in SPARQL can be used by robots and humans alike to acquire both knowledge and data content from team members.The system is empirically validated and complexity results of the proposed algorithms are provided.Additionally,a field robotics case study is described,where a 3D mapping mission has been executed using several UAVs in a collaborative emergency rescue scenario while using the full HFKN Framework.展开更多
基金supported by the National Natural Science Foundation of China(62173114)Guangdong Basic and Applied Basic Research Foundation(2024A1515011228)+2 种基金Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics(2023B1212010005)the Shenzhen Science and Technology Program(KJZD20240903100501002 and GXWD20231129174132001)the Program of Shenzhen Pea-cock Innovation Team(KQTD20210811090146075).
文摘As human–robot interaction(HRI)technology advances,dexterous robotic hands are playing a dual role—serving both as tools for manipulation and as channels for non-verbal communication.While much of the existing research emphasizes improving grasping and structural dexterity,the semantic dimension of gestures and its impact on user experience has been relatively overlooked.Studies from HRI and cognitive psychology consistently show that the naturalness and cognitive empathy of gestures significantly influence user trust,satisfaction,and engagement.This shift reflects a broader transition from mechanically driven designs toward cognitively empathic interactions—robots’ability to infer human affect,intent,and social context to generate appropriate nonverbal responses.In this paper,we argue that large language models(LLMs)enable a paradigm shift in gesture control—from rule-based execution to semantic-driven,context-aware generation.By leveraging LLMs and visual-language models,robots can interpret environmental and social cues,dynamically map emotions,and generate gestures aligned with human communication norms.We conducted a comprehensive review of research in dexterous hand mechanics,gesture semantics,and user experience evaluation,integrating insights from linguistics and cognitive science.Furthermore,we propose a closed-loop framework—"perception–cognition–generation–assessment"—to guide gesture design through iterative,multimodal feedback.This framework lays the conceptual foundation for building universal,adaptive,and emotionally intelligent gesture systems in future human–robot interaction.
基金Supported by National Natural Science Foundation of China(Grant No.U1613208)
文摘The research on legged robots attracted much attention both from the academia and industry. Legged robots are multi-input multi-output with multiple end-e ector systems. Therefore,the mechanical design and control framework are challenging issues. This paper reviews the development of type synthesis and behavior control on legged robots; introduces the hexapod robots developed in our research group based on the proposed type synthesis method. The control framework for legged robots includes data driven layer,robot behavior layer and robot execution layer. Each layer consists several components which are explained in details. Finally,various experiments were conducted on several hexapod robots. The summarization of the type synthesis and behavior control design constructed in this paper would provide a unified platform for communications and references for future advancement for legged robots.
基金supported in part by the National Natural Science Foundation of China under Grants 62373353,and 62033013in part by Youth Innovation Promotion Association CAS(2019138).
文摘The existing fixed gait lower limb rehabilitation robots perform a predetermined walking trajectory for patients,ignoring their residual muscle strength.To enhance patient participation and safety in training,this paper aims to develop a lower limb rehabilitation robot with adaptive gait training capability relying on human–robot interaction force measurement.Firstly,a novel lower limb rehabilitation robot system with several active and passive driven joints is developed,and 2 face-to-face mounted cantilever beam force sensors are employed to measure the human–robot interaction forces.Secondly,a dynamic model of the rehabilitation training robot is constructed to estimate the driven forces of the human lower leg in a completely passive state.Thereafter,based on the theoretical moment from the dynamics and the actual joint interaction force collected by the sensors,an adaptive gait adjustment method is proposed to achieve the goal of adapting to the wearer’s movement intention.Finally,interactive experiments are carried out to validate the effectiveness of the developed rehabilitation training robot system.The proposed rehabilitation training robot system with adaptive gaits offers great potential for future highquality rehabilitation training,e.g.,improving participation and safety.
基金supported by the National Natural Science Foundation of China(62306185)the Guangdong Basic and Applied Basic Research Foundation,China(2024A1515012065)the Shenzhen Science and Technology Program,China(JSGGKQTD 20221101115656029 and KJZD20230923113801004).
文摘In recent years,humanoid robots have gained significant attention due to their potential to revolutionize various industries,from healthcare to manufacturing.A key factor driving this transformation is the advancement of visual perception systems,which are crucial for making humanoid robots more intelligent and autonomous.Despite the progress,the full potential of vision-based technologies in humanoid robots has yet to be fully realized.This review aims to provide a comprehensive overview of recent advancements in visual perception applied to humanoid robots,specifically focusing on applications in state estimation and environmental interaction.By summarizing key developments and analyzing the challenges and opportunities in these areas,this paper seeks to inspire future research that can unlock new capabilities for humanoid robots,enabling them to better navigate complex environments,perform intricate tasks,and interact seamlessly with humans.
基金partially supported by the Shenzhen Science and Technology Program(grant no.RCYX20200714114736115)Shenzhen Institute of AI and Robotics for Society(grant no.AC01202101112).
文摘Telekinesis,as commonly portrayed in science fiction literature and cinema,is a super power wherein users control and manipulate objects absent in physical interaction.In real world,enhancing human–robot interaction needs the synthesis of human intuitive processes with robotic arm.This paper introduces a robotic teleoperation system achieving the essence of telekinetic operations,combining the profound capabilities of augmented reality(AR)with the robotic arm operations.Utilizing AR,the proposed methodology offers operators with a visual feedback,facilitating a level of control surpassing the capacities of natural interfaces.By using AR-driven visual recognition,this system achieves operations in a virtual environment,subsequently actualized in the real world through the robotic arm.Through multiple experiments,we found that the system has a small margin of error in telekinesis operations,meeting the needs of remote operation.Furthermore,our system can operate on objects in the real world.These experiments underscore the capability of the remote control system to assist humans in accomplishing a wider range of tasks through the integration of AR and robotic arms,providing a natural human–robot interaction approach.
基金supported by the National Key Research and Development Program of China(2021YFA1401103)the National Natural Science Foundation of China(61825403,61921005,and 82370520).
文摘In human interactions,social touch communication is widely used to convey emotions,emphasizing its critical role in advancing human–robot interactions by enabling robots to understand and respond to human emotions,thereby significantly enhancing their service capabilities.However,the challenge is to dynamically capture social touch with sufficient spatiotemporal and mechanical resolution for deep haptic data analysis.This study presents a robotic system with flexible electronic skin and a high-frequency signal circuit,utilizing deep neural networks to recognize social touch emotions.The electronic skin,made from double cross-linked ionogels and microstructured arrays,has a low force detection threshold(8 Pa)and a wide perception range(0-150 kPa),enhancing the mechanical resolution of touch signals.By incorporating a high-speed readout circuit capable of capturing spatiotemporal features of social touch gesture information at 30 Hz,the system facilitates precise analysis of touch interactions.A 3D convolutional neural network with a Squeeze-and-Excitation Attention module achieves 87.12% accuracy in recognizing social touch gestures,improving the understanding of emotions conveyed through touch.The effectiveness of the system is validated through interactive demonstrations with robotic dogs and humanoid robots,demonstrating its potential to enhance the emotional intelligence of robots.
基金Supported by the National Research Foundation,Singapore under its International Research Centers in Singapore Funding InitiativeInstitute for Media Innovation,Nanyang Technological University(IMI-NTU)。
文摘Background The uncanny valley hypothesis states that users may experience discomfort when inter-acting with almost human-like artificial characters.Advancements in artificial intelligence,robotics,and computer graphics have led to the development of life-like virtual humans and humanoid robots.Revisiting this hypothesis is necessary to check whether they positively or negatively affect the current population,who are highly accustomed to the latest technologies.Methods In this study,we present a unique evaluation of the uncanny valley hypothesis by allowing participants to interact live with four humanoid robots that have varying levels of human-likeness.Each participant completed a survey questionnaire to evaluate the affinity of each robot.Additionally,we used deep learning methods to quantify the participants’emotional states using multimodal cues,including visual,audio,and text cues,by recording the participant-robot interactions.Results Multi-modal analysis and surveys provided interesting results and insights into the uncanny valley hypothesis.
文摘Q:Dear Mr Sam,the robot has become a hot issue in our lives.What are your ideas about robots working in restaurants?A:I love robots,and I love people.But you know what I love most?People use robots to make life better.
文摘A novel system for human following using a differential robot,including an accurate 3‐D human position tracking module and a novel planning strategy that ensures safety and dynamic feasibility,is proposed.The authors utilise a combination of gimbal camera and LiDAR for long‐term accurate human detection.Then the planning module takes the target's future trajectory as a reference to generate a coarse path to ensure the following visibility.After that,the trajectory is optimised considering other constraints and following distance.Experiments demonstrate the robustness and efficiency of our system in complex environments,demonstrating its potential in various applications.
基金Engineering and Physical Sciences Research Council,Grant/Award Numbers:EP/P01366X/1,EP/V026941/1,EP/W001128/1Small Business research initiative,Grant/Award Number:C/2064382。
文摘Nuclear facilities have a regulatory requirement to measure radiation levels within Post Operational Clean Out(POCO)around nuclear facilities each year,resulting in a trend towards robotic deployments to gain an improved understanding during nuclear decommissioning phases.The UK Nuclear Decommissioning Authority supports the view that human-in-the-loop(HITL)robotic deployments are a solution to improve procedures and reduce risks within radiation characterisation of nuclear sites.The authors present a novel implementation of a Cyber-Physical System(CPS)deployed in an analogue nuclear environment,comprised of a multi-robot(MR)team coordinated by a HITL operator through a digital twin interface.The development of the CPS created efficient partnerships across systems including robots,digital systems and human.This was presented as a multi-staged mission within an inspection scenario for the hetero-geneous Symbiotic Multi-Robot Fleet(SMuRF).Symbiotic interactions were achieved across the SMuRF where robots utilised automated collaborative governance to work together,where a single robot would face challenges in full characterisation of radiation.Key contributions include the demonstration of symbiotic autonomy and query-based learning of an autonomous mission supporting scalable autonomy and autonomy as a service.The coordination of the CPS was a success and displayed further challenges and improvements related to future MR fleets.
文摘Shared control of mobile robots integrates manual input with auxiliary autonomous controllers to improve the overall system performance.However,prior work that seeks to find the optimal shared control ratio needs an accurate human model,which is usually challenging to obtain.In this study,the authors develop an extended Twin Delayed Deep Deterministic Policy Gradient(DDPG)(TD3X)-based shared control framework that learns to assist a human operator in teleoperating mobile robots optimally.The robot's states,shared control ratio in the previous time step,and human's control input is used as inputs to the reinforcement learning(RL)agent,which then outputs the optimal shared control ratio between human input and autonomous controllers without knowing the human model.Noisy softmax policies are developed to make the TD3X algorithm feasible under the constraint of a shared control ratio.Furthermore,to accelerate the training process and protect the robot,a navigation demonstration policy and a safety guard are developed.A neural network(NN)structure is developed to maintain the correlation of sensor readings among heterogeneous input data and improve the learning speed.In addition,an extended DAGGER(DAGGERX)human agent is developed for training the RL agent to reduce human workload.Robot simulations and experiments with humans in the loop are conducted.The results show that the DAGGERX human agent can simulate real human inputs in the worst-case scenarios with a mean square error of 0.0039.Compared to the original TD3 agent,the TD3X-based shared control system decreased the average collision number from 387.3 to 44.4 in a simplistic environment and 394.2 to 171.2 in a more complex environment.The maximum average return increased from 1043 to 1187 with a faster converge speed in the simplistic environment,while the performance is equally good in the complex environment because of the use of an advanced human agent.In the human subject tests,participants'average perceived workload was significantly lower in shared control than that in exclusively manual control(26.90 vs.40.07,p=0.013).
基金This work has been supported by the ELLIIT Network Organization for Information and Communication Technology,Sweden(Project B09)and the Swedish Foundation for Strategic Research SSF(Smart Systems Project RIT15-0097)The first author is also supported by an RExperts Program Grant 2020A1313030098 from the Guangdong Department of Science and Technology,China in addition to a Sichuan Province International Science and Technology Innovation Cooperation Project Grant 2020YFH0160.
文摘In the context of collaborative robotics,distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support.This is particularly important in applications pertaining to emergency rescue and crisis management.During operational missions,data and knowledge are gathered incrementally and in different ways by heterogeneous robots and humans.We describe this as the creation of Hastily Formed Knowledge Networks(HFKNs).The focus of this paper is the specification and prototyping of a general distributed system architecture that supports the creation of HFKNs by teams of robots and humans.The information collected ranges from low-level sensor data to high-level semantic knowledge,the latter represented in part as RDF Graphs.The framework includes a synchronization protocol and associated algorithms that allow for the automatic distribution and sharing of data and knowledge between agents.This is done through the distributed synchronization of RDF Graphs shared between agents.High-level semantic queries specified in SPARQL can be used by robots and humans alike to acquire both knowledge and data content from team members.The system is empirically validated and complexity results of the proposed algorithms are provided.Additionally,a field robotics case study is described,where a 3D mapping mission has been executed using several UAVs in a collaborative emergency rescue scenario while using the full HFKN Framework.
