Remote Minimally Invasive Surgery – Haptic Feedback and Selective Automation in Medical Robotics
The automation of recurrent tasks and force feedback are complex problems in medical robotics. We present a novel approach that extends human-machine skill-transfer by a scaffolding framework. It assumes a consolidated working environment for both, the trainee and the trainer. The trainer provides h...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2011-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.3233/ABB-2011-0022 |
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| _version_ | 1832555080277032960 |
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| author | Christoph Staub Keita Ono Hermann Mayer Alois Knoll Heinz Ulbrich Robert Bauernschmitt |
| author_facet | Christoph Staub Keita Ono Hermann Mayer Alois Knoll Heinz Ulbrich Robert Bauernschmitt |
| author_sort | Christoph Staub |
| collection | DOAJ |
| description | The automation of recurrent tasks and force feedback are complex problems in medical robotics. We present a novel approach that extends human-machine skill-transfer by a scaffolding framework. It assumes a consolidated working environment for both, the trainee and the trainer. The trainer provides hints and cues in a basic structure which is already understood by the learner. In this work, the scaffolding is constituted by abstract patterns, which facilitate the structuring and segmentation of information during “Learning by Demonstration” (LbD). With this concept, the concrete example of knot-tying for suturing is exemplified and evaluated. During the evaluation, most problems and failures arose due to intrinsic system imprecisions of the medical robot system. These inaccuracies were then improved by the visual guidance of the surgical instruments. While the benefits of force feedback in telesurgery has already been demonstrated and measured forces are also used during task learning, the transmission of signals between the operator console and the robot system over long-distances or across-network remote connections is still a challenge due to time-delay. Especially during incision processes with a scalpel into tissue, a delayed force feedback yields to an unpredictable force perception at the operator-side and can harm the tissue which the robot is interacting with. We propose a XFEM-based incision force prediction algorithm that simulates the incision contact-forces in real-time and compensates the delayed force sensor readings. A realistic 4-arm system for minimally invasive robotic heart surgery is used as a platform for the research. |
| format | Article |
| id | doaj-art-dcd87278eb4f4d8b98edb2eba60c5cb5 |
| institution | Kabale University |
| issn | 1176-2322 1754-2103 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-dcd87278eb4f4d8b98edb2eba60c5cb52025-02-03T05:49:45ZengWileyApplied Bionics and Biomechanics1176-23221754-21032011-01-018222123610.3233/ABB-2011-0022Remote Minimally Invasive Surgery – Haptic Feedback and Selective Automation in Medical RoboticsChristoph Staub0Keita Ono1Hermann Mayer2Alois Knoll3Heinz Ulbrich4Robert Bauernschmitt5Robotics and Embedded Systems, Technische Universität München, Munich, GermanyInstitute of Applied Mechanics, Technische Universität München, Munich, GermanyRobotics and Embedded Systems, Technische Universität München, Munich, GermanyRobotics and Embedded Systems, Technische Universität München, Munich, GermanyInstitute of Applied Mechanics, Technische Universität München, Munich, GermanyGerman Heart Center Munich, Clinic for Cardiovascular Surgery, Munich, GermanyThe automation of recurrent tasks and force feedback are complex problems in medical robotics. We present a novel approach that extends human-machine skill-transfer by a scaffolding framework. It assumes a consolidated working environment for both, the trainee and the trainer. The trainer provides hints and cues in a basic structure which is already understood by the learner. In this work, the scaffolding is constituted by abstract patterns, which facilitate the structuring and segmentation of information during “Learning by Demonstration” (LbD). With this concept, the concrete example of knot-tying for suturing is exemplified and evaluated. During the evaluation, most problems and failures arose due to intrinsic system imprecisions of the medical robot system. These inaccuracies were then improved by the visual guidance of the surgical instruments. While the benefits of force feedback in telesurgery has already been demonstrated and measured forces are also used during task learning, the transmission of signals between the operator console and the robot system over long-distances or across-network remote connections is still a challenge due to time-delay. Especially during incision processes with a scalpel into tissue, a delayed force feedback yields to an unpredictable force perception at the operator-side and can harm the tissue which the robot is interacting with. We propose a XFEM-based incision force prediction algorithm that simulates the incision contact-forces in real-time and compensates the delayed force sensor readings. A realistic 4-arm system for minimally invasive robotic heart surgery is used as a platform for the research.http://dx.doi.org/10.3233/ABB-2011-0022 |
| spellingShingle | Christoph Staub Keita Ono Hermann Mayer Alois Knoll Heinz Ulbrich Robert Bauernschmitt Remote Minimally Invasive Surgery – Haptic Feedback and Selective Automation in Medical Robotics Applied Bionics and Biomechanics |
| title | Remote Minimally Invasive Surgery – Haptic Feedback and Selective Automation in Medical Robotics |
| title_full | Remote Minimally Invasive Surgery – Haptic Feedback and Selective Automation in Medical Robotics |
| title_fullStr | Remote Minimally Invasive Surgery – Haptic Feedback and Selective Automation in Medical Robotics |
| title_full_unstemmed | Remote Minimally Invasive Surgery – Haptic Feedback and Selective Automation in Medical Robotics |
| title_short | Remote Minimally Invasive Surgery – Haptic Feedback and Selective Automation in Medical Robotics |
| title_sort | remote minimally invasive surgery haptic feedback and selective automation in medical robotics |
| url | http://dx.doi.org/10.3233/ABB-2011-0022 |
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