Blended shared control utilizing online identification
Surgical robots are increasingly common, yet routine tasks such as tissue grasping remain potentially harmful with high occurrences of tissue crush injury due to the lack of force feedback from the grasper. This work aims to investigate whether a blended shared control framework which utilizes real-time identification of the object being grasped as part of the feedback may help address the prevalence of tissue crush injury in robotic surgeries.
This work tests the proposed shared control framework and tissue identification algorithm on a custom surrogate surgical robotic grasping setup. This scheme utilizes identification of the object being grasped as part of the feedback to regulate to a desired force. The blended shared control is arbitrated between human and an implicit force controller based on a computed confidence in the identification of the grasped object. The online identification is performed using least squares based on a nonlinear tissue model. Testing was performed on five silicone tissue surrogates. Twenty grasps were conducted, with half of the grasps performed under manual control and half of the grasps performed with the proposed blended shared control, to test the efficacy of the control scheme.
The identification method resulted in an average of 95% accuracy across all time samples of all tissue grasps using a full leave-grasp-out cross-validation. There was an average convergence time of \(8.1 \pm 6.3\) ms across all training grasps for all tissue surrogates. Additionally, there was a reduction in peak forces induced during grasping for all tissue surrogates when applying blended shared control online.
The blended shared control using online identification more successfully regulated grasping forces to the desired target force when compared with manual control. The preliminary work on this surrogate setup for surgical grasping merits further investigation on real surgical tools and with real human tissues.
KeywordsShared control Robotic surgery Tissue identification Tissue crush injury
This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. 00039202. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Compliance with ethical standards
Conflict of interest
Robert Sweet is a consultant for Olympus-Advisory for endourologic applications. Robert Sweet is chief executive officer for Simagine Health-Distributing simulation training solutions.
This article does not contain any studies with human participants or animals performed by any of the authors.
This articles does not contain patient data.
- 2.Dockter R, O’Neill J, Stephens T, Kowalewski T (2016) Feasibility of tissue classification via da vinci endowrist surgical tool. In: Hamlyn symposium on medical robotics, pp 64–65Google Scholar
- 4.Enes A, Book W (2010) Blended shared control of zermelo’s navigation problem. In: American control conference (ACC), 2010, IEEE, pp 4307–4312Google Scholar
- 13.Sie A, Winek M, Kowalewski TM (2014) Online identification of abdominal tissues in vivo for tissue-aware and injury-avoiding surgical robots. In: 2014 IEEE/RSJ international conference on intelligent robots and systems (IROS 2014), IEEE, pp 2036–2042Google Scholar
- 17.Winkler A, Suchỳ J (2015) Implicit force control of a position controlled robot—a comparison with explicit algorithms. World Acad Sci Eng Technol Int J Comput Electr Autom Control Inf Eng 9(6):1454–1460Google Scholar
- 18.Yu X, Chizeck HJ, Hannaford B (2007) Comparison of transient performance in the control of soft tissue grasping. In: IEEE/RSJ international conference on intelligent robots and systems, 2007. IROS 2007, IEEE, pp 1809–1814Google Scholar