2022 Fiscal Year Annual Research Report
Real-time adaptive control of robots for microsurgery: towards submillimeter accuracy without added sensors
| Project/Area Number |
19K14935
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| Research Institution | The University of Tokyo |
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Principal Investigator |
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| Project Period (FY) |
2019-04-01 – 2023-03-31
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| Keywords | 手術ロボット / 適応制御 / 画像処理 / ロボティクス / センサ融合 |
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| Outline of Annual Research Achievements |
The experiments performed so far have been polished and the results published in world-renowned venues.
In particular,
(1) The proposed adaptive controller can consider partial and complete task-space measurements and workspace constraints, evaluated in a replicable experimental with nonlinear task-space constraints.
The work below is being continuously investigated,
(2) The method for pose tracking of rigid instruments has been developed using deep learning. Validation was done using real robotic rigid instrument images. An extension of this method was studied for instruments with multiple degrees of freedom.
(3) A framework for the generation of realistic CG images using VR simulation was developed. It has been of great use in the training of deep learning algorithms for instrument tracking.
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