Abstract
This paper proposes an expertise-oriented Training platform for robotics-Assisted minimally invasive surgery. The framework builds on previous work of The authors and makes use of dual-user Teleoperation scenario, allowing The presence of an expert in The Training loop. A Fuzzy-Logic (FL) methodology is proposed, which specifies The level/mode of The Training required for The Trainee according To his/her level of proficiency over The Task. A major advantage of The proposed FL approach is That, having The expert in The loop, it can specify The Trainee's proficiency level relative To That of The expert in real-time. Moreover, based on The relative skills assessment, The proposed FL approach decides if or To what extent The Trainee should receive a haptic guidance force based on Virtual Fixtures or The environment force from The interaction between The surgical instrument and Tissue at The slave side. In addition To The level/mode of The haptics-enabled Training required for The Trainee, The proposed FL framework specifies The authority level of The Trainees over The operation in real-time, according To Their proficiency levels over The Task. Stability of The overall closed-loop Teleoperated system is also investigated using The small-gain Theorem, resulting in a sufficient condition To guarantee stability in The presence of constant communication delays. Finally, experimental results are given To evaluate The design and feasibility of The proposed framework.
Original language | English (US) |
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Article number | 6907728 |
Pages (from-to) | 5902-5907 |
Number of pages | 6 |
Journal | Proceedings - IEEE International Conference on Robotics and Automation |
DOIs | |
State | Published - Sep 22 2014 |
Event | 2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China Duration: May 31 2014 → Jun 7 2014 |
Keywords
- Dual-User System
- Fuzzy Logic
- Relative Skills Assessment
- Surgical Training
- Teleoperation
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering