Shape Estimation of Concentric Tube Robots Using Single Point Position Measurement
Emile Mackute, Balint Thamo, Kevin Dhaliwal, Mohsen Khadem
Abstract
Accurate shape estimation of concentric tube robots (CTRs) using mathematical models remains a challenge, reinforcing the need to develop techniques for accurate and real-time shape sensing of CTRs. In this paper, we develop a fusion algorithm that predicts the robot's shape by combining a mathematical model of the CTR with a measurement of the Cartesian coordinates of the robot's tip using an electro-magnetic sensor. We experimentally validated our method in static and dynamic scenarios with and without external loading. Results demonstrated that the fusion algorithm improves the error of model-based shape prediction by an average of 44.3%, corresponding to 2.43% of the robot's arc length. Furthermore, we demonstrate that our method can be used in real-time to simultaneously track the robot's tip position and predict its shape.
BibTeX
@inproceedings{iros2022_shapeestimationo,
title = {Shape Estimation of Concentric Tube Robots Using Single Point Position Measurement},
author = {Emile Mackute and Balint Thamo and Kevin Dhaliwal and Mohsen Khadem},
booktitle = {IROS 2022},
year = {2022}
}