Probabilistic Motion Model Learning for Tendon Actuated Continuum Robots with Backlash
Mahdi Chaari, Philippe Zanne, Florent Nageotte
Abstract
In this paper, we propose a probabilistic motion model for tendon actuated continuum robots that experience actuation transmission non-linearities due to cable slack and cable-sheath friction. The model is based on a Lie group formulation of the robot’s end-effector pose that incorporates a new simple backlash model. Bayesian parameter estimation is then employed to learn a probability distribution over the model’s parameters. This allows the uncertainty over the parameters to be propagated in the prediction of the end-effector’s trajectory. The model’s predictive capabilities are compared against the static Cosserat-rod-based model and the Kirchhoff model in simulation and are validated with experiments on a robotized medical endoscope.
BibTeX
@inproceedings{iros2025_probabilisticmot,
title = {Probabilistic Motion Model Learning for Tendon Actuated Continuum Robots with Backlash},
author = {Mahdi Chaari and Philippe Zanne and Florent Nageotte},
booktitle = {IROS 2025},
year = {2025}
}