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This research introduces a discrete Cosserat rod formulation that jointly estimates shape, external loads, and actuation inputs for continuum robots, addressing critical uncertainties in confined surgical environments. By leveraging mechanically principled priors and a sparse factor graph structure, the method achieves high numerical accuracy and efficient real-time optimization across tendon-driven and parallel architectures. The experimental validation on a concentric tube robot demonstrates the framework's ability to provide direct access to manipulator Jacobians, enhancing state estimation reliability under model uncertainty.
Continuum Robot State Estimation with Actuation Uncertainty
arXiv:2601.04493v3 Announce Type: replace Abstract: Continuum robots are flexible, slender manipulators well suited for confined surgical environments. In these settings, unknown interaction forces and model uncertainty significantly affect robot shape, motivating state estimation from external observations. Existing estimation methods either neglect actuation modeling or rely on simplified deterministic actuation models. In contrast, we jointly estimate robot shape, external loads, and actuation inputs using mechanically principled actuation priors. To achieve this, we present a discrete Cosserat rod formulation with piecewise-linear strain integration that provides high numerical accuracy while inducing a sparse factor graph structure for efficient nonlinear optimization. We extend the framework to tendon-driven and parallel robots in simulation and validate it experimentally on a surgical concentric tube robot. Overall, our approach enables principled real-time estimation across multiple robot architectures while providing direct access to manipulator Jacobians through the linearized factor graph.
Abstract:Continuum robots are flexible, slender manipulators well suited for confined surgical environments. In these settings, unknown interaction forces and model uncertainty significantly affect robot shape, motivating state estimation from external observations. Existing estimation methods either neglect actuation modeling or rely on simplified deterministic actuation models. In contrast, we jointly estimate robot shape, external loads, and actuation inputs using mechanically principled actuation priors. To achieve this, we present a discrete Cosserat rod formulation with piecewise-linear strain integration that provides high numerical accuracy while inducing a sparse factor graph structure for efficient nonlinear optimization. We extend the framework to tendon-driven and parallel robots in simulation and validate it experimentally on a surgical concentric tube robot. Overall, our approach enables principled real-time estimation across multiple robot architectures while providing direct access to manipulator Jacobians through the linearized factor graph.
Public preprint for IEEE RAL. Accepted May 2026
Robotics (cs.RO)
arXiv:2601.04493 [cs.RO]
(or arXiv:2601.04493v3 [cs.RO] for this version)
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