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arxiv logo>cs> arXiv:2303.06130
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Computer Science > Robotics

arXiv:2303.06130 (cs)
[Submitted on 10 Mar 2023 (v1), last revised 31 Jul 2024 (this version, v4)]

Title:Full State Estimation of Continuum Robots from Tip Velocities: A Cosserat-Theoretic Boundary Observer

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Abstract:State estimation of robotic systems is essential to implementing feedback controllers, which usually provide better robustness to modeling uncertainties than open-loop controllers. However, state estimation of soft robots is very challenging because soft robots have theoretically infinite degrees of freedom while existing sensors only provide a limited number of discrete measurements. This work focuses on soft robotic manipulators, also known as continuum robots. We design an observer algorithm based on the well-known Cosserat rod theory, which models continuum robots by nonlinear partial differential equations (PDEs) evolving in geometric Lie groups. The observer can estimate all infinite-dimensional continuum robot states, including poses, strains, and velocities, by only sensing the tip velocity of the continuum robot, and hence it is called a ``boundary'' observer. More importantly, the estimation error dynamics is formally proven to be locally input-to-state stable. The key idea is to inject sequential tip velocity measurements into the observer in a way that dissipates the energy of the estimation errors through the boundary. The distinct advantage of this PDE-based design is that it can be implemented using any existing numerical implementation for Cosserat rod models. All theoretical convergence guarantees will be preserved, regardless of the discretization method. We call this property ``one design for any discretization''. Extensive numerical studies are included and suggest that the domain of attraction is large and the observer is robust to uncertainties of tip velocity measurements and model parameters.
Subjects:Robotics (cs.RO); Systems and Control (eess.SY)
Cite as:arXiv:2303.06130 [cs.RO]
 (orarXiv:2303.06130v4 [cs.RO] for this version)
 https://doi.org/10.48550/arXiv.2303.06130
arXiv-issued DOI via DataCite

Submission history

From: Tongjia Zheng [view email]
[v1] Fri, 10 Mar 2023 18:48:48 UTC (796 KB)
[v2] Sun, 16 Apr 2023 16:11:28 UTC (2,594 KB)
[v3] Tue, 27 Jun 2023 01:37:49 UTC (2,598 KB)
[v4] Wed, 31 Jul 2024 16:09:03 UTC (4,722 KB)
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