研究課題/領域番号 |
20K14691
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研究種目 |
若手研究
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配分区分 | 基金 |
審査区分 |
小区分20020:ロボティクスおよび知能機械システム関連
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研究機関 | 東京大学 |
研究代表者 |
ファラガッソ アンジェラ 東京大学, 大学院工学系研究科(工学部), 特任助教 (80847070)
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研究期間 (年度) |
2020-04-01 – 2024-03-31
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研究課題ステータス |
交付 (2022年度)
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配分額 *注記 |
4,160千円 (直接経費: 3,200千円、間接経費: 960千円)
2021年度: 2,080千円 (直接経費: 1,600千円、間接経費: 480千円)
2020年度: 2,080千円 (直接経費: 1,600千円、間接経費: 480千円)
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キーワード | Visual-based sensing / Optimal design / MIS / Medical Robotics / Remote Palpation / Stiffness Sensing / Topological Optimization / Real-time Stiffness Map / Stiffness map / Remote palpation / Stiffness sensing / Topological optimization / Stiffness Sensor / Laparoscopic Surgery / Optimal 3D Design |
研究開始時の研究の概要 |
The aim of this research is to create a real-time stiffness map for minimally invasive procedures by developing an optimal task-specific visual-based stiffness sensor, to be embedded at the tip of the endoscopic camera, and used to model the nonlinear behaviour characterizing anatomical surfaces.
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研究実績の概要 |
The implementation of this research is related to the creation of two systems, one placed in the remote site composed by a robot manipulator and a visual based sensing mechanism, and another placed on the clinical room in which an operator can control and replan the motion of the robot while visualizing real-time stiffness information of the explored anatomical area. A light weight six degree of freedom manipulator, the UFactory Lite6 collaborative robot, has been purchased and interfaced with the Robot Operating System (ROS), a powerful framework which allow implementation of algorithms-simulations for robotics and easy interface with real hardware. A semi-autonomous navigation algorithm, in which the operator can control the motion of the robot remotely and replan the its path, has been implemented using Moveit, a motion planning navigation framework, in ROS. Although the operator can replan the motion of the robot and control it remotely, virtual fixture will be implemented using the visual-stiffness information to control the force and integrate safety futures during the contact. The control and planning algorithm have been initially tested in simulations and are now interfaced with the real robot. A novel 3D machine learning algorithm, that shatters the constraints of conventional methodologies, is implemented to find the optimal design of the sensing mechanism. The method is still in a development phase.
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現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
The research is progressing rather smoothly as the PI is working on the optimization algorithm and in parallel working on the control an planning system which will be integrated with the final design of the sensor.
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今後の研究の推進方策 |
In this year the PI will work on the finalization of the optimization algorithm, manufacturing of the sensor and realization of the interface that will be implemented in the clinical room. Once the optimal design is completed, the sensor will be integrated with the control and planning algorithm of the robot. A real-time stiffness map will be feedback to the remote operator which will be also able to control and replan the motion of the remote-manipulator. The stiffness information will be used to create and store a colored-code stiffness map. The integrated system will be tested with phantoms which mimicking the mechanical properties of the humans' anatomical areas.
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