Shape Representation and Modeling of Tendon-Driven Continuum Robots Using Euler Arc Splines

Due to the compliance of tendon-driven continuum robots, carrying a load or experiencing a tip force result in variations in backbone curvature. While the spatial robot configuration theoretically needs an infinite number of parameters for exact description, it can be well approximated using Euler A...

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Vydáno v:IEEE robotics and automation letters Ročník 7; číslo 3; s. 1 - 8
Hlavní autoři: Rao, Priyanka, Peyron, Quentin, Burgner-Kahrs, Jessica
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Analytical models
and Learning for Soft Robots
Automatic
Computational modeling
Computing time
Control
Data models
Disks
Engineering Sciences
Flexible Robotics
Kinematics
Modeling
Parameterization
Representations
Robots
Shape
Splines (mathematics)
Static models
Three dimensional models
Three-dimensional displays
Control
and Learning for Soft Robots
Flexible Robotics
Modeling Control and Learning for Soft Robots Flexible Robotics Kinematics
Modeling
Kinematics
ISSN:2377-3766, 2377-3766
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Shrnutí:Due to the compliance of tendon-driven continuum robots, carrying a load or experiencing a tip force result in variations in backbone curvature. While the spatial robot configuration theoretically needs an infinite number of parameters for exact description, it can be well approximated using Euler Arc Splines which use only six of them. In this letter, we first show the accuracy of this representation by fitting the Euler Arc splines directly to experimentally measured robot shapes. Additionally, we propose a 3D static model that can account for gravity, friction and tip forces. We demonstrate the utility of using efficient parameterization by analyzing the computation time of the proposed model and then, using it to propose a hybrid model that combines physics-based model with observed data. The average tip error for the Euler arc spline representation is <inline-formula><tex-math notation="LaTeX">0.43</tex-math></inline-formula>% and the proposed static model is <inline-formula><tex-math notation="LaTeX">3.25</tex-math></inline-formula>% w.r.t. robot length. The average computation time is <inline-formula><tex-math notation="LaTeX">0.56 \,\mathrm{ms}</tex-math></inline-formula> for nonplanar deformations for a robot with ten disks. The hybrid model reduces the maximum error predicted by the static model from <inline-formula><tex-math notation="LaTeX">8.6</tex-math></inline-formula>% to <inline-formula><tex-math notation="LaTeX">5.1</tex-math></inline-formula>% w.r.t. robot length, while using 30 observations for training.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3185377