Beyond the Manual Touch: Situational-aware Force Control for Increased Safety in Robot-assisted Skullbase Surgery
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916101163581440 |
|---|---|
| author | Ishida, Hisashi Galaiya, Deepa Nagururu, Nimesh Creighton, Francis Kazanzides, Peter Taylor, Russell Sahu, Manish |
| author_facet | Ishida, Hisashi Galaiya, Deepa Nagururu, Nimesh Creighton, Francis Kazanzides, Peter Taylor, Russell Sahu, Manish |
| contents | Purpose - Skullbase surgery demands exceptional precision when removing bone in the lateral skull base. Robotic assistance can alleviate the effect of human sensory-motor limitations. However, the stiffness and inertia of the robot can significantly impact the surgeon's perception and control of the tool-to-tissue interaction forces. Methods - We present a situational-aware, force control technique aimed at regulating interaction forces during robot-assisted skullbase drilling. The contextual interaction information derived from the digital twin environment is used to enhance sensory perception and suppress undesired high forces. Results - To validate our approach, we conducted initial feasibility experiments involving a medical and two engineering students. The experiment focused on further drilling around critical structures following cortical mastoidectomy. The experiment results demonstrate that robotic assistance coupled with our proposed control scheme effectively limited undesired interaction forces when compared to robotic assistance without the proposed force control. Conclusions - The proposed force control techniques show promise in significantly reducing undesired interaction forces during robot-assisted skullbase surgery. These findings contribute to the ongoing efforts to enhance surgical precision and safety in complex procedures involving the lateral skull base. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11721 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Beyond the Manual Touch: Situational-aware Force Control for Increased Safety in Robot-assisted Skullbase Surgery Ishida, Hisashi Galaiya, Deepa Nagururu, Nimesh Creighton, Francis Kazanzides, Peter Taylor, Russell Sahu, Manish Robotics Systems and Control Purpose - Skullbase surgery demands exceptional precision when removing bone in the lateral skull base. Robotic assistance can alleviate the effect of human sensory-motor limitations. However, the stiffness and inertia of the robot can significantly impact the surgeon's perception and control of the tool-to-tissue interaction forces. Methods - We present a situational-aware, force control technique aimed at regulating interaction forces during robot-assisted skullbase drilling. The contextual interaction information derived from the digital twin environment is used to enhance sensory perception and suppress undesired high forces. Results - To validate our approach, we conducted initial feasibility experiments involving a medical and two engineering students. The experiment focused on further drilling around critical structures following cortical mastoidectomy. The experiment results demonstrate that robotic assistance coupled with our proposed control scheme effectively limited undesired interaction forces when compared to robotic assistance without the proposed force control. Conclusions - The proposed force control techniques show promise in significantly reducing undesired interaction forces during robot-assisted skullbase surgery. These findings contribute to the ongoing efforts to enhance surgical precision and safety in complex procedures involving the lateral skull base. |
| title | Beyond the Manual Touch: Situational-aware Force Control for Increased Safety in Robot-assisted Skullbase Surgery |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2401.11721 |