2023 Fiscal Year Annual Research Report
Automatic Optimal Design of a Visual-based Stiffness Sensor and real-time Colour-coded Stiffness Map for Minimally Invasive Procedures.
| Project/Area Number |
20K14691
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| Research Institution | The University of Tokyo |
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Principal Investigator |
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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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Keywords | Visual based sensing / MIS / Optimal design / Medical Robotics / Remote Palpation / Topological Optimization |
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| Outline of Annual Research Achievements |
This research has made substantial progress in the development of a visual-based stiffness sensor for minimally invasive procedures. By employing generative design in Fusion 360, the sensor was optimized for integration with a lightweight UFactory Lite6 robot. This setup was interfaced with the Robot Operating System (ROS), enabling remote manipulation and path replanning through a semi-autonomous navigation algorithm. The project established a remote system with the robot on one side and the Meta Quest on the other, enhancing the operator's control and visualization capabilities. The information gathered could be used for generating a real-time colour-coded stiffness map, promising to enhance the precision and safety of minimally invasive surgeries.
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