ArticleIEEE transactions on medical robotics and bionics2020
Learning the Complete Shape of Concentric Tube Robots.
Article in IEEE transactions on medical robotics and bionics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 40 citations in OpenAlex.
- Friction Modeling of Tendon-driven Continuum Robots through Linear Complementarity Problem.IEEE robotics and automation letters · 2026Article
- Few-Shot Physics-Informed Neural Network for Shape Reconstruction of Concentric-Tube Robots.IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation · 2026Article
- Tip estimation approach for concentric tube robots using 2D ultrasound images and kinematic model.Medical & biological engineering & computing · 2021Article
- Learning the Complete Shape of Concentric Tube Robots.IEEE transactions on medical robotics and bionics · 2020Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Concentric tube robots, composed of nested pre-curved tubes, have the potential to perform minimally invasive surgery at difficult-to-reach sites in the human body. In order to plan motions that safely perform surgeries in constrained spaces that require avoiding sensitive structures, the ability to accurately estimate the entire shape of the robot is needed. Many state-of-the-art physics-based shape models are unable to account for complex physical phenomena and subsequently are less accurate than is required for safe surgery. In this work, we present a learned model that can estimate the entire shape of a concentric tube robot. The learned model is based on a deep neural network that is trained using a mixture of simulated and physical data. We evaluate multiple network architectures and demonstrate the model's ability to compute the full shape of a concentric tube robot with high accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.