Friction-Based Robotic Driver for Programmable Bevel-Tip Needles

EasyChair Preprint 15078

Abstract

This paper presents a new concept that has the potential to miniaturize the driving mechanism of Programmable Bevel-Tip Needles. The study utilized a custom-designed test rig and advanced video analysis techniques to assess potential slippage between segments and gears. The results suggest that gears made from softer materials had poorer engagement with the needle, while gears that were too hard had a smooth and slippery surface that resulted in large amounts of slip. After iterative testing, an optimal material composition of 60% hard and 40% soft was identified, along with an optimal normal force. However, slipping issues must be addressed to realize the potential of this new concept fully, and future experimental studies are planned to consider different teeth profiles and the size of teeth on the needle and wheels. Overall, this study provides useful insights into the potential of this new concept and highlights areas for future research, including a methodology for testing the new mechanism and measuring slippage.

Keyphrases: Neurosurgery, Surgical Catheters, mechanical design

@booklet{EasyChair:15078,
  author    = {Ayhan Aktas and Akilesh Perumal and Rongwan Chen and Seong Young Ko and Daniele Dini and Ferdinando Rodriguez Y Baena},
  title     = {Friction-Based Robotic Driver for Programmable Bevel-Tip Needles},
  howpublished = {EasyChair Preprint 15078},
  year      = {EasyChair, 2024}}