An expertise-oriented Training framework for robotics-Assisted surgery

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.