aiqbench analysis
This paper advances continuum robotics by integrating modular mechanical design with onboard, learning-based pose estimation to eliminate reliance on external tracking infrastructure. The core contribution lies in the machine learning framework that enables a single joint model to generalize across various robot configurations, significantly enhancing adaptability for real-world deployment in confined spaces.
Tendon-Driven Continuum Robot with Modular Stiffness and In-Situ Self Pose Estimation
arXiv:2609.16256v1 Announce Type: new Abstract: Continuum robots enable smooth shape morphing and safe interaction in confined environments. However, most existing systems are task-specific and depend on external sensing infrastructure, limiting their adaptability and real-world deployment. This paper presents a self-contained modular continuum robotic platform that combines mechanical reconfigurability with onboard pose estimation. The robot is constructed from interchangeable continuum joints with analytically precomputed stiffness, allowing rapid assembly and direct programming of the robot shape. Proprioceptive sensing is achieved using magnetic sensors and a modular learning-based framework, where a single model is trained per joint and reused across configurations. The system is experimentally validated in real world, demonstrating self-sensing capabilities and adaptation without external tracking.
Abstract:Continuum robots enable smooth shape morphing and safe interaction in confined environments. However, most existing systems are task-specific and depend on external sensing infrastructure, limiting their adaptability and real-world deployment. This paper presents a self-contained modular continuum robotic platform that combines mechanical reconfigurability with onboard pose estimation. The robot is constructed from interchangeable continuum joints with analytically precomputed stiffness, allowing rapid assembly and direct programming of the robot shape. Proprioceptive sensing is achieved using magnetic sensors and a modular learning-based framework, where a single model is trained per joint and reused across configurations. The system is experimentally validated in real world, demonstrating self-sensing capabilities and adaptation without external tracking.
Robotics (cs.RO); Systems and Control (eess.SY)
arXiv:2609.16256 [cs.RO]
(or arXiv:2609.16256v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.16256 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 206 words
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