Interface Design of a Physical Human–Robot Interaction System for Human Impedance Adaptive Skill Transfer

Yang, Chenguang; Zeng, Chao; Liang, Peidong; Li, Zhijun; Li, Ruifeng; Su, Chun‐Yi

doi:10.1109/tase.2017.2743000

Cited by 178 publications

(129 citation statements)

References 35 publications

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“…3) Electromyography (EMG) and Electroencephalography (EEG): Electromyography (EMG) sensor is used to record the activities of electrical signals that are generated when a muscle contracts. Information from the systems could be used to estimate human limb motion [70], [71] which is essential for the reconstruction of robotic prosthetic for amputees and also important for limb rehabilitation [72], [73]. This system has been applied to detect hand grasping actions in [74] and in [75] to transfer writing skill from a human to a robot.…”

Section: Sensors and Actuatorsmentioning

confidence: 99%

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Ogenyi

Liu

Yang

et al. 2021

IEEE Trans. Cybern.

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This paper presents a state-of-the-art survey on robotic systems, sensors, actuators and collaborative strategies for Physical Human-Robot Collaboration (pHRC). The paper starts with an overview of some robotic systems with cuttingedge technologies (sensors and actuators) suitable for pHRC operations and the intelligent assist devices employed in pHRC. Sensors being among the essential components to establish communication between a human and a robotic system are surveyed. The sensor supplies the signal needed to drive the robotic actuators. The survey reveals that the design of new generation collaborative robots and other intelligent robotic systems has paved the way for sophisticated learning techniques and control algorithms to be deployed in pHRC. Furthermore, it revealed relevant components needed to be considered for effective pHRC to be accomplished. Finally, a discussion of the major advances made, some research directions, and future challenges are presented.

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Section: Sensors and Actuatorsmentioning

confidence: 99%

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Ogenyi

Liu

Yang

et al. 2021

IEEE Trans. Cybern.

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“…These technologies have been widely employed in complex dynamic scenarios such as robot-assisted surgical operation (Taylor et al [18]) and disability assistance and rehabilitation (Kaufmann et al [19]). For tasks requiring physical movements, for example, in manufacturing, sEMG-based neural interface is widely adopted in HRI scenarios (Zucker et al [20] and Yang et al [21]). Knowledge and implementation skills transfer by sEMG to telerobot makes it controllable in a remote place and manageable when accomplishing hazardous jobs (Artemiadis and Kyriakopoulos [22], Fukuda et al [23], and Yang et al [24]).…”

Section: Complexitymentioning

confidence: 99%

Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals

Wang

et al. 2018

Complexity

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To investigate the effects of muscle fatigue on bioinspired robot learning quality in teaching by demonstration (TbD) tasks, in this work, we propose to first identify the emerging muscle fatigue phenomenon of the human demonstrator by analyzing his/her surface Electromyography (sEMG) recordings and then guide the robot learning curve with this knowledge in mind. The time-varying amplitude and frequency sequences determining the subband sEMG signals have been estimated and their dominant values over short time intervals have been explored as fatigue-indicating features. These features are found carrying muscle fatigue cues of the human demonstrator in the course of robot manipulation. In robot learning tasks requiring multiple demonstrations, the fatiguing status of human demonstrator can be acquired by tracking the changes of the proposed features over time. In order to model data from multiple demonstrations, Gaussian mixture models (GMMs) have been employed. According to the identified muscle fatigue factor, a weight has been assigned to each of the demonstration trials in training stage, which is therefore termed as weighted GMMs (W-GMMs) algorithm. Six groups of data with various fatiguing status, as well as their corresponding weights, are taken as input data to get the adapted W-GMMs parameters. After that, Gaussian mixture regression (GMR) algorithm has been applied to regenerate the movement trajectory for the robot. TbD experiments on Baxter robot with 30 human demonstration trials show that the robot can successfully accomplish the taught task with a generated trajectory much closer to that of the desirable condition where little fatigue exists.

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“…In recent years, many researchers and scholars are incline to the design of intelligent control to cope with different regions or fields with sophisticated and system identification in engineering environments, and hence, artificial intelligence control has become an indispensable significant technique. [1][2][3] For example, in the work of Yang et al, 4 an intelligence shared control system with brain computer interface was developed to achieve accurate object for robot manipulator system, in which the user's mind is regarded as a commander. Parameters identifications are also be populated for frequently usage in various engineering such as hovercraft system, 5 robot system, 6,7 vehicle engines, 8 and Wiener system.…”

Section: Introductionmentioning

confidence: 99%

Neural adaptive global stability control for robot manipulators with time‐varying output constraints

Fan

Kang

Wang

et al. 2019

Intl J Robust & Nonlinear

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Summary In this paper, a novel adaptive control scheme is proposed based on radial basis function neural network (RBFNN). The considered system is deduced by the structure of RBFNN with nonzero time‐varying parameter that installed in the fore‐end and terminal of RBFNN. With this structure and the Taylor expansion of any smooth continuous nonlinear function, a universal approximation of RBFNN is addressed according to the analysis of the character of continuous homogenous function and the Euler's theorem. The approximation accuracies can be adjusted online by the nonzero time‐varying parameter in the device with the degree of continuous homogenous function, which expand the semiglobally stability to global stability over conventional neural controller design approaches. Based on the theory analysis of barrier Lyapunov function, the violation of time‐varying constraints can be subjugated without wrecked. Finally, simulation results are carried out to verify the effectiveness by the design methods.

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Interface Design of a Physical Human–Robot Interaction System for Human Impedance Adaptive Skill Transfer

Cited by 178 publications

References 35 publications

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals

Neural adaptive global stability control for robot manipulators with time‐varying output constraints

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