Automatic adjustment of laparoscopic pose using deep reinforcement learning
Abstract Q Q -value estimation consists of convolutional neural networks for feature extraction and fully connected layers for policy learning. The proposed method is validated in simulation. In different test scenarios, the laparoscopic arm can be well automatically adjusted so that surgical instruments with different postures are in the proper position of the field of view. Simulation results demonstrate the effectiveness of the method in learning the highly non-linear mapping between laparoscopic images and the optimal action policy of a laparoscopic arm.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Automatic adjustment of laparoscopic pose using deep reinforcement learning ; volume:13 ; number:1 ; year:2022 ; pages:593-602 ; extent:10
Mechanical sciences ; 13, Heft 1 (2022), 593-602 (gesamt 10)
- Creator
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Yu, Lingtao
Xia, Yongqiang
Wang, Pengcheng
Sun, Lining
- DOI
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10.5194/ms-13-593-2022
- URN
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urn:nbn:de:101:1-2022063005173089919646
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:32 AM CEST
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Associated
- Yu, Lingtao
- Xia, Yongqiang
- Wang, Pengcheng
- Sun, Lining
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