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. 2017 Apr 12;17(4):844.
doi: 10.3390/s17040844.

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

Zhijiang Du  1 , Wei Wang  2 , Zhiyuan Yan  3 , Wei Dong  4 , Weidong Wang  5
Affiliations

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

Zhijiang Du et al. Sensors (Basel). .

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.

Keywords: minimally invasive surgical robot; physical human-robot interaction; reinforcement learning; variable admittance control.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Variable admittance controller.
Figure 2
Figure 2
Flow chart of FSL.
Figure 3
Figure 3
(a) Minimally invasive surgery manipulator; (b) The structure diagram of RCM.
Figure 4
Figure 4
Target positons in the experiments.
Figure 5
Figure 5
The trend of the evaluation criteria in the experiments.
Figure 6
Figure 6
(a) Compared with low fixed admittance models; (b) Compared with high fixed admittance models.
Figure 7
Figure 7
Compared with variable admittance model without damping adjustment (c=ce).
Figure 8
Figure 8
(a) Accuracy; (b) Energy transferred from the operator to the robot; (c) Accumulated jerk in the episode.
Figure 9
Figure 9
(a) The score of naturalness; (b) The score of sense of control.
See this image and copyright information in PMC

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