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Journal of Healthcare Engineering
Volume 2017, Article ID 6702919, 13 pages
https://doi.org/10.1155/2017/6702919
Research Article

Surgeon Training in Telerobotic Surgery via a Hardware-in-the-Loop Simulator

1Department of Mechanical Engineering, University of Illinois, Urbana, IL 61801, USA
2Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22903, USA
3Department of Electrical and Computer Engineering, University of Illinois, Urbana, IL 61801, USA
4Department of Industrial and Enterprise Systems Engineering, University of Illinois, Urbana, IL 61801, USA

Correspondence should be addressed to Xiao Li; ude.sionilli@61iloaix

Received 6 January 2017; Revised 4 April 2017; Accepted 14 May 2017; Published 3 August 2017

Academic Editor: Qing Shi

Copyright © 2017 Xiao Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This work presents a software and hardware framework for a telerobotic surgery safety and motor skill training simulator. The aims are at providing trainees a comprehensive simulator for acquiring essential skills to perform telerobotic surgery. Existing commercial robotic surgery simulators lack features for safety training and optimal motion planning, which are critical factors in ensuring patient safety and efficiency in operation. In this work, we propose a hardware-in-the-loop simulator directly introducing these two features. The proposed simulator is built upon the Raven-II™ open source surgical robot, integrated with a physics engine and a safety hazard injection engine. Also, a Fast Marching Tree-based motion planning algorithm is used to help trainee learn the optimal instrument motion patterns. The main contributions of this work are (1) reproducing safety hazards events, related to da Vinci™ system, reported to the FDA MAUDE database, with a novel haptic feedback strategy to provide feedback to the operator when the underlying dynamics differ from the real robot’s states so that the operator will be aware and can mitigate the negative impact of the safety-critical events, and (2) using motion planner to generate semioptimal path in an interactive robotic surgery training environment.