AI Article Synopsis
- Surgical robots are prone to causing tissue crush injuries during grasping due to the lack of force feedback, prompting the exploration of a blended shared control framework that incorporates real-time object identification to mitigate these risks.
- The study tests this framework on a custom robotic setup, assessing both the effectiveness of a tissue identification algorithm and the control strategy by comparing manual and blended shared control grasps on silicone tissue surrogates.
- Results show a 95% accuracy in tissue identification and a reduction in peak grasping forces with the blended control, suggesting it more effectively regulates forces during surgeries compared to traditional manual methods, and indicating a need for further research in this area.
Article Abstract
Purpose: 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.
Methods: 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.
Results: 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 [Formula: see text] 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.
Conclusion: 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.
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| http://dx.doi.org/10.1007/s11548-018-1745-3 | DOI Listing |
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