This paper presents an innovative investigation on prototyping a digital twin(DT)as the platform for human-robot interactive welding and welder behavior analysis.This humanrobot interaction(HRI)working style helps to ...This paper presents an innovative investigation on prototyping a digital twin(DT)as the platform for human-robot interactive welding and welder behavior analysis.This humanrobot interaction(HRI)working style helps to enhance human users'operational productivity and comfort;while data-driven welder behavior analysis benefits to further novice welder training.This HRI system includes three modules:1)a human user who demonstrates the welding operations offsite with her/his operations recorded by the motion-tracked handles;2)a robot that executes the demonstrated welding operations to complete the physical welding tasks onsite;3)a DT system that is developed based on virtual reality(VR)as a digital replica of the physical human-robot interactive welding environment.The DT system bridges a human user and robot through a bi-directional information flow:a)transmitting demonstrated welding operations in VR to the robot in the physical environment;b)displaying the physical welding scenes to human users in VR.Compared to existing DT systems reported in the literatures,the developed one provides better capability in engaging human users in interacting with welding scenes,through an augmented VR.To verify the effectiveness,six welders,skilled with certain manual welding training and unskilled without any training,tested the system by completing the same welding job;three skilled welders produce satisfied welded workpieces,while the other three unskilled do not.A data-driven approach as a combination of fast Fourier transform(FFT),principal component analysis(PCA),and support vector machine(SVM)is developed to analyze their behaviors.Given an operation sequence,i.e.,motion speed sequence of the welding torch,frequency features are firstly extracted by FFT and then reduced in dimension through PCA,which are finally routed into SVM for classification.The trained model demonstrates a 94.44%classification accuracy in the testing dataset.The successful pattern recognition in skilled welder operations should benefit to accelerate n展开更多
Intuitive and efficient interfaces for human- robot interaction （HRI） have been a challenging issue in robotics as it is essential for the prevalence of robots supporting humans in key areas of activities. This pape...Intuitive and efficient interfaces for human- robot interaction （HRI） have been a challenging issue in robotics as it is essential for the prevalence of robots supporting humans in key areas of activities. This paper presents a novel augmented reality （AR） based interface to facilitate human-virtual robot interaction. A number of human-virtual robot interaction methods have been for- mulated and implemented with respect to the various types of operations needed in different robotic applications. A Euclidean distance-based method is developed to assist the users in the interaction with the virtual robot and the spatial entities in an AR environment. A monitor-based visualization mode is adopted as it enables the users to perceive the virtual contents associated with different interaction methods, and the virtual content augmented in the real environment is informative and useful to the users during their interaction with the virtual robot. Case researches are presented to demonstrate the successful implementation of the AR-based HRI interface in planning robot pick-and-place operations and path following operations.展开更多
A facial expression emotion recognition based human-robot interaction(FEER-HRI) system is proposed, for which a four-layer system framework is designed. The FEERHRI system enables the robots not only to recognize huma...A facial expression emotion recognition based human-robot interaction(FEER-HRI) system is proposed, for which a four-layer system framework is designed. The FEERHRI system enables the robots not only to recognize human emotions, but also to generate facial expression for adapting to human emotions. A facial emotion recognition method based on2D-Gabor, uniform local binary pattern(LBP) operator, and multiclass extreme learning machine(ELM) classifier is presented,which is applied to real-time facial expression recognition for robots. Facial expressions of robots are represented by simple cartoon symbols and displayed by a LED screen equipped in the robots, which can be easily understood by human. Four scenarios,i.e., guiding, entertainment, home service and scene simulation are performed in the human-robot interaction experiment, in which smooth communication is realized by facial expression recognition of humans and facial expression generation of robots within 2 seconds. As a few prospective applications, the FEERHRI system can be applied in home service, smart home, safe driving, and so on.展开更多
In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the robot.The presented HRI controller design i...In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the robot.The presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller design.The task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the inner-loop.Data-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance parameters.In the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity measurement.On this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task space.The simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.展开更多
In order to enable personalized natural interaction in service robots, artificial emotion is needed which helps robots to appear as individuals. In the emotion modeling theory of emotional Markov chain model （eMCM） ...In order to enable personalized natural interaction in service robots, artificial emotion is needed which helps robots to appear as individuals. In the emotion modeling theory of emotional Markov chain model （eMCM） for spontaneous transfer and emotional hidden Markov model （eHMM） for stimulated transfer, there are three problems： 1） Emotion distinguishing problem： whether adjusting parameters of the model have any effects on individual emotions; 2） How much effect the change makes; 3） The problem of different initial emotional states leading to different resultant emotions from a given stimuli. To solve these problems, a research method of individual emotional difference is proposed based on metric multidimensional scaling theory. Using a dissimilarity matrix, a scalar product matrix is calculated. Subsequently, an individual attribute reconstructing matrix can be obtained by principal component factor analysis. This can display individual emotion difference with low dimension. In addition, some mathematical proofs are carried out to explain experimental results. Synthesizing the results and proofs, corresponding conclusions are obtained. This new method provides guidance for the adjustment of parameters of emotion models in artificial emotion theory.展开更多
基于语音识别设计了针对脑瘫患儿的数字语音训练系统。应用人机交互(HRI)技术与仿生机械手动作控制相结合达到提升脑瘫(CP)康复训练效果的目的。该系统中控制器采用Arduino MEGA 2560为主控制器,显示屏LCD1602作为人机交互数据显示界面...基于语音识别设计了针对脑瘫患儿的数字语音训练系统。应用人机交互(HRI)技术与仿生机械手动作控制相结合达到提升脑瘫(CP)康复训练效果的目的。该系统中控制器采用Arduino MEGA 2560为主控制器,显示屏LCD1602作为人机交互数据显示界面,通过LD3320语音芯片实现人机语音交互功能。人机交互功能是通过Labview环境展开,可实现人、机器人的手势与动作的实时交互训练与评价。该系统可训练脑瘫患者反应能力、语言表述能力以及认识手势动作数字动作能力,为提升脑瘫康复训练系统提供关键技术。展开更多
This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior ...This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction(p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration(LfD) techniques to adapt to the users' performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user,is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method,and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.展开更多
文摘This paper presents an innovative investigation on prototyping a digital twin(DT)as the platform for human-robot interactive welding and welder behavior analysis.This humanrobot interaction(HRI)working style helps to enhance human users'operational productivity and comfort;while data-driven welder behavior analysis benefits to further novice welder training.This HRI system includes three modules:1)a human user who demonstrates the welding operations offsite with her/his operations recorded by the motion-tracked handles;2)a robot that executes the demonstrated welding operations to complete the physical welding tasks onsite;3)a DT system that is developed based on virtual reality(VR)as a digital replica of the physical human-robot interactive welding environment.The DT system bridges a human user and robot through a bi-directional information flow:a)transmitting demonstrated welding operations in VR to the robot in the physical environment;b)displaying the physical welding scenes to human users in VR.Compared to existing DT systems reported in the literatures,the developed one provides better capability in engaging human users in interacting with welding scenes,through an augmented VR.To verify the effectiveness,six welders,skilled with certain manual welding training and unskilled without any training,tested the system by completing the same welding job;three skilled welders produce satisfied welded workpieces,while the other three unskilled do not.A data-driven approach as a combination of fast Fourier transform(FFT),principal component analysis(PCA),and support vector machine(SVM)is developed to analyze their behaviors.Given an operation sequence,i.e.,motion speed sequence of the welding torch,frequency features are firstly extracted by FFT and then reduced in dimension through PCA,which are finally routed into SVM for classification.The trained model demonstrates a 94.44%classification accuracy in the testing dataset.The successful pattern recognition in skilled welder operations should benefit to accelerate n
文摘Intuitive and efficient interfaces for human- robot interaction （HRI） have been a challenging issue in robotics as it is essential for the prevalence of robots supporting humans in key areas of activities. This paper presents a novel augmented reality （AR） based interface to facilitate human-virtual robot interaction. A number of human-virtual robot interaction methods have been for- mulated and implemented with respect to the various types of operations needed in different robotic applications. A Euclidean distance-based method is developed to assist the users in the interaction with the virtual robot and the spatial entities in an AR environment. A monitor-based visualization mode is adopted as it enables the users to perceive the virtual contents associated with different interaction methods, and the virtual content augmented in the real environment is informative and useful to the users during their interaction with the virtual robot. Case researches are presented to demonstrate the successful implementation of the AR-based HRI interface in planning robot pick-and-place operations and path following operations.
基金supported by the National Natural Science Foundation of China(61403422,61273102)the Hubei Provincial Natural Science Foundation of China(2015CFA010)+1 种基金the Ⅲ Project(B17040)the Fundamental Research Funds for National University,China University of Geosciences(Wuhan)
文摘A facial expression emotion recognition based human-robot interaction(FEER-HRI) system is proposed, for which a four-layer system framework is designed. The FEERHRI system enables the robots not only to recognize human emotions, but also to generate facial expression for adapting to human emotions. A facial emotion recognition method based on2D-Gabor, uniform local binary pattern(LBP) operator, and multiclass extreme learning machine(ELM) classifier is presented,which is applied to real-time facial expression recognition for robots. Facial expressions of robots are represented by simple cartoon symbols and displayed by a LED screen equipped in the robots, which can be easily understood by human. Four scenarios,i.e., guiding, entertainment, home service and scene simulation are performed in the human-robot interaction experiment, in which smooth communication is realized by facial expression recognition of humans and facial expression generation of robots within 2 seconds. As a few prospective applications, the FEERHRI system can be applied in home service, smart home, safe driving, and so on.
基金This work was supported in part by the National Natural Science Foundation of China(61903028)the Youth Innovation Promotion Association,Chinese Academy of Sciences(2020137)+1 种基金the Lifelong Learning Machines Program from DARPA/Microsystems Technology Officethe Army Research Laboratory(W911NF-18-2-0260).
文摘In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the robot.The presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller design.The task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the inner-loop.Data-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance parameters.In the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity measurement.On this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task space.The simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.
基金Acknowledgements This work was supported by the National High Technology Research and Development Program of China （2007AA04Z218）, the National Natural Science Foundation of China （Grant No. 60903067）, and the Beijing Key Discipline Development Program （XK100080537）.
文摘In order to enable personalized natural interaction in service robots, artificial emotion is needed which helps robots to appear as individuals. In the emotion modeling theory of emotional Markov chain model （eMCM） for spontaneous transfer and emotional hidden Markov model （eHMM） for stimulated transfer, there are three problems： 1） Emotion distinguishing problem： whether adjusting parameters of the model have any effects on individual emotions; 2） How much effect the change makes; 3） The problem of different initial emotional states leading to different resultant emotions from a given stimuli. To solve these problems, a research method of individual emotional difference is proposed based on metric multidimensional scaling theory. Using a dissimilarity matrix, a scalar product matrix is calculated. Subsequently, an individual attribute reconstructing matrix can be obtained by principal component factor analysis. This can display individual emotion difference with low dimension. In addition, some mathematical proofs are carried out to explain experimental results. Synthesizing the results and proofs, corresponding conclusions are obtained. This new method provides guidance for the adjustment of parameters of emotion models in artificial emotion theory.
文摘基于语音识别设计了针对脑瘫患儿的数字语音训练系统。应用人机交互(HRI)技术与仿生机械手动作控制相结合达到提升脑瘫(CP)康复训练效果的目的。该系统中控制器采用Arduino MEGA 2560为主控制器,显示屏LCD1602作为人机交互数据显示界面,通过LD3320语音芯片实现人机语音交互功能。人机交互功能是通过Labview环境展开,可实现人、机器人的手势与动作的实时交互训练与评价。该系统可训练脑瘫患者反应能力、语言表述能力以及认识手势动作数字动作能力,为提升脑瘫康复训练系统提供关键技术。
文摘This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction(p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration(LfD) techniques to adapt to the users' performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user,is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method,and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.
