Reliable Tissue Grasping in Robotic Minimally Invasive Surgery Enabled by Optical‐Based Slip Sensing
Source abstract
Abstract
Grasping delicate, deformable, and slippery biological tissues in robotic minimally invasive surgery (RMIS) is challenging: excessive force can cause tissue damage and bleeding, while insufficient force may result in slippage, increasing the risk of surgical accidents and delays. To apply appropriate force and enhance surgical outcomes, detecting slippage between the tissue and the grasper is critical. An optical slip‐sensing method that precisely detects various slip conditions and estimates their displacement, direction, and speed across a broad range of slip speeds (0.3–104 mm s−1). Experimental results confirm high accuracy in slip displacement estimation below 0.01 mm with a mean error of 0.20 mm over 8.5 mm and a short response time (<30 ms), as well as independence from intermediate medium and slip speed. The slip sensor was integrated into a da Vinci surgical grasper and used to grasp ex‐vivo porcine tissue. The results confirm that distinct translational and rotational slip patterns and tissue stretch during tissue handling can be identified using data from paired sensors. These findings verify the feasibility of optical slip sensing in a surgical environment and suggest its potential to enable reliable tissue handling in RMIS.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Soft Robotics and Applications · Surgical Simulation and Training · Anatomy and Medical Technology
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering Seoul National University Seoul 08826 Republic of Korea
Institute of Advanced Machines and Design (SNU‐IAMD) Seoul National University Seoul 08826 Republic of Korea
Funding
Seoul National University
References (36)
Show source-provided references
1. 10.1007/s00464-013-2973-910.1007/s00464-013-2973-9 ↗
2. 10.1001/jamanetworkopen.2019.1891110.1001/jamanetworkopen.2019.18911 ↗
3. 10.1007/s11695-020-05055-510.1007/s11695-020-05055-5 ↗
4. 10.1007/s00464-022-09643-710.1007/s00464-022-09643-7 ↗
5. Strategic Market Research. Top robotic surgery statistics to follow in 2023. Online 2023. URLhttps://www.strategicmarketresearch.com/blogs/robotic‐surgery‐statistics. Accessed: 2025‐02‐24.
6. 10.1136/adc.2004.06276010.1136/adc.2004.062760 ↗
7. 10.1055/s-2004-81411310.1055/s-2004-814113 ↗
8. Rivero‐Moreno Y. · Cureus · 2023 · 15
9. 10.1097/01.sla.0000103020.19595.7d10.1097/01.sla.0000103020.19595.7d ↗
10. 10.1111/j.1471-0528.2008.02038.x10.1111/j.1471-0528.2008.02038.x ↗
11. 10.1109/TRO.2015.247351510.1109/TRO.2015.2473515 ↗
12. 10.1109/JSEN.2008.91748110.1109/JSEN.2008.917481 ↗
13. 10.1109/LRA.2018.286336010.1109/LRA.2018.2863360 ↗
14. 10.1109/TASE.2023.326140310.1109/TASE.2023.3261403 ↗
15. 10.1109/ACCESS.2020.301773810.1109/ACCESS.2020.3017738 ↗
16. 10.1016/1350-4533(94)00003-R10.1016/1350-4533(94)00003-R ↗
17. 10.1007/s00464-008-0298-x10.1007/s00464-008-0298-x ↗
18. J. M.Walker A. A.Blank P. A.Shewokis M. K.O’Malley in2014 IEEE Haptics Symp. (HAPTICS) IEEE2014 pp.461–466.10.1109/HAPTICS.2014.6775499 ↗
19. 10.1109/TOH.2015.242009610.1109/TOH.2015.2420096 ↗
20. 10.1007/s42235-019-0005-810.1007/s42235-019-0005-8 ↗
21. Jones D. · IEEE Sensors · 2019 · 1
22. K.Ando T.Yamamoto Y.Maeda K.Terao F.Shimokawa M.Fujiwara H.Takao in2019 IEEE Inter. Electron Devices Meeting (IEDM) IEEE2019 pp.18.2.1–18.2.4.10.1109/IEDM19573.2019.8993645 ↗
23. 10.1007/s00542-018-3975-210.1007/s00542-018-3975-2 ↗
24. 10.1109/JSEN.2022.318786010.1109/JSEN.2022.3187860 ↗
25. PixArt Imaging Inc PAW3008J1 Datasheet 2022. Accessed: 2025‐02‐24.
26. 10.1016/j.triboint.2014.08.01310.1016/j.triboint.2014.08.013 ↗
27. KEYENCE IL‐065 Datasheet2025. Accessed: 2025‐02‐24.10.1088/1475-7516/2025/02/065 ↗
28. 10.1002/admt.20210028510.1002/admt.202100285 ↗
29. 10.1002/admt.20220026010.1002/admt.202200260 ↗
30. 10.1002/adma.20231385710.1002/adma.202313857 ↗
31. 10.1016/j.amsu.2021.10226810.1016/j.amsu.2021.102268 ↗
32. 10.1089/end.2010.030610.1089/end.2010.0306 ↗
33. 10.1109/JTEHM.2022.318093710.1109/JTEHM.2022.3180937 ↗
34. 10.1002/jbio.20210033110.1002/jbio.202100331 ↗
35. 10.1109/ACCESS.2025.355094810.1109/ACCESS.2025.3550948 ↗
36. 10.1109/LRA.2022.315844010.1109/LRA.2022.3158440 ↗