mlsanityData to 14 Oct 2026

Strain-Parameterized Coupled Dynamics and Dual-Camera Visual Servoing for Aerial Continuum Manipulators

arXiv 2603.23333 ↗First posted 24 Mar 2026cs.RO cs.CVcited by 1

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Abstract (arXiv)

Tendon-driven aerial continuum manipulators (TD-ACMs) combine the maneuverability of uncrewed aerial vehicles (UAVs) with the compliance of lightweight continuum robots (CRs). Existing coupled dynamic modeling approaches for TD-ACMs incur high computational costs and do not explicitly account for aerial platform underactuation. To address these limitations, this paper presents a generalized dynamic formulation of a coupled TD-ACM with an underactuated base. The proposed approach integrates a strain-parameterized Cosserat rod model with a rigid-body model of the UAV into a unified Lagrangian ordinary differential equation (ODE) framework on SE(3), thereby eliminating computationally intensive symbolic derivations. Building upon the developed model, a robust dual-camera image-based visual servoing (IBVS) scheme is introduced. The proposed controller mitigates the field-of-view (FoV) limitations of conventional IBVS, compensates for attitude-induced image motion caused by UAV lateral dynamics, and incorporates a low-level adaptive controller to address modeling uncertainties with formal stability guarantees. Extensive simulations and experimental validation on a compact custom-built prototype demonstrate the effectiveness and robustness of the proposed framework in real-world scenarios.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 33 references that are in the map. First posted on arXiv 24 Mar 2026.

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4 of 33 references.

References · 4

  1. 2021Modeling and Control of an Omnidirectional Micro Aerial Vehicle Equipped with a Soft Robotic Armcited by 3
  2. 2024Modular Adaptive Aerial Manipulation under Unknown Dynamic Coupling Forcescited by 3
  3. 2018Hybrid Multi-camera Visual Servoing to Moving Targetcited by 2
  4. 2025Deep Visual Servoing of an Aerial Robot Using Keypoint Feature Extractioncited by 2

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