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Sensors 2017, 17(4), 844; doi:10.3390/s17040844

Variable Admittance Control Based on Fuzzy Reinforcement Learning for Minimally Invasive Surgery Manipulator

State Key Laboratory of Robotics and System, Harbin Institute of Technology, 2 Yikuang Street, Harbin 150080, China
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Academic Editor: Vittorio M. N. Passaro
Received: 12 February 2017 / Revised: 4 April 2017 / Accepted: 7 April 2017 / Published: 12 April 2017
(This article belongs to the Section Physical Sensors)
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Abstract

In order to get natural and intuitive physical interaction in the pose adjustment of the minimally invasive surgery manipulator, a hybrid variable admittance model based on Fuzzy Sarsa(λ)-learning is proposed in this paper. The proposed model provides continuous variable virtual damping to the admittance controller to respond to human intentions, and it effectively enhances the comfort level during the task execution by modifying the generated virtual damping dynamically. A fuzzy partition defined over the state space is used to capture the characteristics of the operator in physical human-robot interaction. For the purpose of maximizing the performance index in the long run, according to the identification of the current state input, the virtual damping compensations are determined by a trained strategy which can be learned through the experience generated from interaction with humans, and the influence caused by humans and the changing dynamics in the robot are also considered in the learning process. To evaluate the performance of the proposed model, some comparative experiments in joint space are conducted on our experimental minimally invasive surgical manipulator. View Full-Text
Keywords: minimally invasive surgical robot; physical human-robot interaction; reinforcement learning; variable admittance control minimally invasive surgical robot; physical human-robot interaction; reinforcement learning; variable admittance control
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Du, Z.; Wang, W.; Yan, Z.; Dong, W.; Wang, W. Variable Admittance Control Based on Fuzzy Reinforcement Learning for Minimally Invasive Surgery Manipulator. Sensors 2017, 17, 844.

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