Physical Human–Robot Interaction of a Robotic Exoskeleton By Admittance Control

Li, Zhijun; Huang, Bo; Ye, Zhifeng; Deng, Mingdi; Yang, Chenguang

doi:10.1109/tie.2018.2821649

Cited by 248 publications

(114 citation statements)

References 32 publications

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“…This feature is the major contribution of ARMin-V and it increased the performance of the robot to the level of adjusting patient's anthropometry automatically [28]. Another work in [33] presents a framework for adaptive admittance control by incorporating and considering human motion intention in order to perform more accurately in the actual physical interaction. The framework consists of double control loops with the inner loop overseeing the unknown masses and inertia of the robot dynamics while the outer loop harmonies the interaction based on the observed intention of the subject.…”

Section: A Collaborative Robotic Armsmentioning

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: A Collaborative Robotic Armsmentioning

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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“…Robotic implementation of such minimally invasive surgery (MIS) techniques is expected to improve the control and precision of the surgical tools used in interventions while reducing trauma to patients. (1)(2)(3) However, specialized surgical robots are expensive, which limits their acquisition and use in hospitals.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Bimanual Control of Dual-arm Serial Manipulator for Robot-assisted Minimally Invasive Surgeries

Su¹,

Schmirander²,

Valderrama-Hincapie³

et al. 2020

Sensors and Materials

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Keywords: bimanual control, minimally invasive surgery, radial basis function neural network, redundant manipulator, remote center of motion Robotic assistance is promising for improving minimally invasive surgery (MIS). This work presents asymmetric bimanual control of a dual-arm serial robot with two remote centers of motion (RCMs) constraints for MIS. In our previous works, general null space controllers to guarantee the fixed RCM constraint have been proposed. However, an incision on a patient's abdominal wall is not fixed owing to the respiration of the patient, which generates an uncertain disturbance at the joints of robotic manipulators. To improve accuracy, a radial basis function neural network is implemented to adapt to these disturbances and control the end-effector position. Finally, the adaptive bimanual control strategy is validated through simulations based on clinical data. The proposed control shows improved accuracy in the end effector position for all the designed surgical tasks. In future works, the algorithm will be validated on an actual dual-arm serial robot making use of a body phantom. China University of Technology, Guangzhou, China, in 2017. She is currently pursuing a Ph.D. degree at the Department of Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden, working on robotic exoskeletons for patients with motor disorders. Her main research interests include human movement simulation, strategies, consequences, assistance for patients with motor disorders, and adaptive control. (longbin@kth.se) Yingbai Hu received his M.S. degree in control engineering from South

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“…In complex electromagnetic environments, noise uncertainty greatly impairs the performance of spectrum sensing [7,8]. Therefore, it is necessary to reduce the noise and redundant information in the signals collected by the secondary users (SUs).…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Spectrum Sensing Method Based on Empirical Mode Decomposition and Information Geometry in Complex Electromagnetic Environment

Wang

Zhang

et al. 2019

Complexity

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In a complex electromagnetic environment, there are cases where the noise is uncertain and difficult to estimate, which poses a great challenge to spectrum sensing systems. This paper proposes a cooperative spectrum sensing method based on empirical mode decomposition and information geometry. The method mainly includes two modules, a signal feature extraction module and a spectrum sensing module based on K-medoids. In the signal feature extraction module, firstly, the empirical modal decomposition algorithm is used to denoise the signals collected by the secondary users, so as to reduce the influence of the noise on the subsequent spectrum sensing process. Further, the spectrum sensing problem is considered as a signal detection problem. To analyze the problem more intuitively and simply, the signal after empirical mode decomposition is mapped into the statistical manifold by using the information geometry theory, so that the signal detection problem is transformed into geometric problems. Then, the corresponding geometric tools are used to extract signal features as statistical features. In the spectrum sensing module, the K-medoids clustering algorithm is used for training. A classifier can be obtained after a successful training, thereby avoiding the complex threshold derivation in traditional spectrum sensing methods. In the experimental part, we verified the proposed method and analyzed the experimental results, which show that the proposed method can improve the spectrum sensing performance.

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Physical Human–Robot Interaction of a Robotic Exoskeleton By Admittance Control

Cited by 248 publications

References 32 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

Asymmetric Bimanual Control of Dual-arm Serial Manipulator for Robot-assisted Minimally Invasive Surgeries

A Cooperative Spectrum Sensing Method Based on Empirical Mode Decomposition and Information Geometry in Complex Electromagnetic Environment

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