Work

Probabilistic Motion Model Learning for Tendon Actuated Continuum Robots with Backlash

Year: 2025

Type: conference-paper

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.

Source: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

DOI: 10.1109/iros60139.2025.11246223

Authors: Mahdi Chaari, Philippe Zanne, Florent Nageotte

Institutions: Centre National de la Recherche Scientifique, Inserm, Université de Strasbourg

Language: English

Topic: Soft Robotics and Applications

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Agence Nationale de la Recherche, HORIZON EUROPE Health

Awards: Par-delà les frontières, l'Université de Strasbourg, Infrastructure technologique pour la recherche d’excellence en robotique