Abstract
This work presents a 'smart' robotic surgical grasper capable of identifying tissue during the early stages of a grasp, allowing automated prevention of grasper-induced tissue crush injuries. It employs no additional sensors beyond signals already present in surgical robots. An estimation algorithm using an extended Kalman filter (EKF) is employed for a nonlinear tissue dynamic model, which is investigated in silico as well as in vivo and in situ on porcine models. Results show that while the approach is sensitive to initial conditions, tissue can be identified during the early stage of a typical grasp.
Original language | English (US) |
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Title of host publication | IROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2036-2042 |
Number of pages | 7 |
ISBN (Electronic) | 9781479969340 |
DOIs | |
State | Published - Oct 31 2014 |
Event | 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 - Chicago, United States Duration: Sep 14 2014 → Sep 18 2014 |
Publication series
Name | IEEE International Conference on Intelligent Robots and Systems |
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ISSN (Print) | 2153-0858 |
ISSN (Electronic) | 2153-0866 |
Other
Other | 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 |
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Country/Territory | United States |
City | Chicago |
Period | 9/14/14 → 9/18/14 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.