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

• URL: http://arxiv.org/abs/2603.23333v1
• Date: Tue, 24 Mar 2026 15:33:22 GMT
• Title: Strain-Parameterized Coupled Dynamics and Dual-Camera Visual Servoing for Aerial Continuum Manipulators
• Abstract summary: Tendon-driven continuum aerial manipulators (TD-ACMs) combine the maneuverability of uncrewed aerial vehicles (UAVs) with the compliance of lightweight robots (CRs)<n>Existing continuum coupled dynamic modeling approaches for TD-ACMs incur high computational costs.<n>This paper presents a generalized dynamic formulation of a coupled TD-ACM with an underactuated base.
• Score: 0.8594140167290097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 $\mathrm{SE}(3)$, 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.

Related papers

• A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
  Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
  arXiv  Detail & Related papers  (2026-08-11T01:51:08Z)
• EmbodiedVAE: Disentangled Video VAE for Efficient and Controllable Embodied Manipulation [35.20482569112933]
  We present EmbodiedVAE, a novel video VAE that provides compact yet controllable latent representations tailored for robotic manipulation world models.<n>Our proposed EmbodiedVAE achieves superior reconstruction quality with high compression rate, while enabling more precise action control in robotic manipulation scenarios with an average of 2dB PSNR improvement over state-of-the-art video VAEs.
  arXiv  Detail & Related papers  (2026-08-04T01:01:18Z)
• Sensorless State Estimation and Control for Agile Cable-Suspended Payload Transport by Quadrotors [0.0]
  This work proposes a novel control and estimation approach for aerial manipulation of a cable-suspended load using Unmanned Aerial Vehicles (UAVs)<n>Results from real-robot experiments demonstrated that the explicit inclusion of load dynamics in the optimization problem significantly reduces trajectory-tracking errors and yields better overall performance compared to strategies based on incomplete models.
  arXiv  Detail & Related papers  (2026-05-05T11:57:31Z)
• Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots [23.655392951446995]
  Tendon-Driven Robots Continuum (TDCRs) pose significant modeling and control challenges due to complex nonlinearities.<n>This paper proposes a differentiable learning framework that integrates high-fidelity dynamics modeling with robust neural control.
  arXiv  Detail & Related papers  (2026-04-28T14:20:44Z)
• AI-Enabled Image-Based Hybrid Vision/Force Control of Tendon-Driven Aerial Continuum Manipulators [0.6999740786886535]
  This paper presents an AI-enabled cascaded hybrid vision/force control framework for tendon-driven aerial manipulators.<n>The proposed controller is designed to enable autonomous, physical interaction with a static environment while stabilizing the image feature error.
  arXiv  Detail & Related papers  (2026-04-21T01:19:11Z)
• Systematic Analysis of Coupling Effects on Closed-Loop and Open-Loop Performance in Aerial Continuum Manipulators [0.6524460254566903]
  This paper investigates two distinct approaches to the dynamic modeling of aerial continuum manipulators (ACMs)<n>The primary objective is to determine the conditions under which the decoupled model attains accuracy comparable to the coupled model.<n>To extend the analysis to closed-loop performance, a novel dynamics-based proportional-derivative sliding mode image-based visual servoing controller is developed.
  arXiv  Detail & Related papers  (2026-02-21T01:18:34Z)
• Nonlinear Predictive Control of the Continuum and Hybrid Dynamics of a Suspended Deformable Cable for Aerial Pick and Place [4.356242302111724]
  This paper presents a framework for aerial manipulation of an cable that combines a high-fidelity model based on partial differential equations (PDEs) with a reduced-order representation suitable for real-time control.<n>The framework enables real-time, dynamics-aware control of unmanned aerial vehicles (UAVs) carrying suspended flexible cables.
  arXiv  Detail & Related papers  (2026-02-19T09:38:32Z)
• Unifying Model-Free Efficiency and Model-Based Representations via Latent Dynamics [6.208369829942616]
  We present Unified Latent Dynamics (ULD), a novel reinforcement learning algorithm.<n>ULD unifies the efficiency of model-free methods with the representational strengths of model-based approaches.<n> evaluated on 80 environments spanning Gym locomotion, DeepMind Control (proprioceptive and visual), and Atari.
  arXiv  Detail & Related papers  (2026-02-13T06:06:56Z)
• Model-Less Feedback Control of Space-based Continuum Manipulators using Backbone Tension Optimization [0.0]
  Continuum manipulators offer dexterity and safe compliance for navigation within confined and obstacle-rich environments.<n>Their infinite-dimensional backbone deformation, unmodeled internal friction, and configuration-dependent stiffness limit the reliability of model-based kinematic formulations.<n>This work presents a complete model-less control framework that bypasses kinematic modeling by using an empirically intrinsic Jacobian refined online through differential convex updates.
  arXiv  Detail & Related papers  (2025-12-07T09:34:28Z)
• MambaX: Image Super-Resolution with State Predictive Control [48.76194230142064]
  Mamba has emerged as a promising approach that can represent the entire reconstruction process as a state sequence with multiple nodes, allowing for intermediate intervention.<n>We created a nonlinear state predictive control model textbfMambaX that maps consecutive spectral bands into a latent state space and generalizes the SR task by dynamically learning the nonlinear state parameters of control equations.<n>Our evaluation demonstrates the superior performance of the dynamic spectrum-state representation model in both single-image SR and multimodal fusion-based SR tasks.
  arXiv  Detail & Related papers  (2025-11-22T11:44:09Z)
• ScaleWeaver: Weaving Efficient Controllable T2I Generation with Multi-Scale Reference Attention [86.93601565563954]
  ScaleWeaver is a framework designed to achieve high-fidelity, controllable generation upon advanced visual autoregressive( VAR) models.<n>The proposed Reference Attention module discards the unnecessary attention from image$rightarrow$condition, reducing computational cost.<n>Experiments show that ScaleWeaver delivers high-quality generation and precise control while attaining superior efficiency over diffusion-based methods.
  arXiv  Detail & Related papers  (2025-10-16T17:00:59Z)
• Multi-Modality Driven LoRA for Adverse Condition Depth Estimation [61.525312117638116]
  We propose Multi-Modality Driven LoRA (MMD-LoRA) for Adverse Condition Depth Estimation.<n>It consists of two core components: Prompt Driven Domain Alignment (PDDA) and Visual-Text Consistent Contrastive Learning (VTCCL)<n>It achieves state-of-the-art performance on the nuScenes and Oxford RobotCar datasets.
  arXiv  Detail & Related papers  (2024-12-28T14:23:58Z)
• Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments [0.0]
  This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100. The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers.
  arXiv  Detail & Related papers  (2024-10-03T17:50:19Z)
• ControlVAR: Exploring Controllable Visual Autoregressive Modeling [48.66209303617063]
  Conditional visual generation has witnessed remarkable progress with the advent of diffusion models (DMs) Challenges such as expensive computational cost, high inference latency, and difficulties of integration with large language models (LLMs) have necessitated exploring alternatives to DMs. This paper introduces Controlmore, a novel framework that explores pixel-level controls in visual autoregressive modeling for flexible and efficient conditional generation.
  arXiv  Detail & Related papers  (2024-06-14T06:35:33Z)
• FullLoRA: Efficiently Boosting the Robustness of Pretrained Vision Transformers [72.83770102062141]
  Vision Transformer (ViT) model has gradually become mainstream in various computer vision tasks.<n>Existing large models tend to prioritize performance during training, potentially neglecting the robustness.<n>We develop novel LNLoRA module, incorporating a learnable layer normalization before the conventional LoRA module.<n>We propose the FullLoRA framework by integrating the learnable LNLoRA modules into all key components of ViT-based models.
  arXiv  Detail & Related papers  (2024-01-03T14:08:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.