Image-based Visual Servo Control for Aerial Manipulation Using a Fully-Actuated UAV
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Abstract (arXiv)
Using Unmanned Aerial Vehicles (UAVs) to perform high-altitude manipulation tasks beyond just passive visual application can reduce the time, cost, and risk of human workers. Prior research on aerial manipulation has relied on either ground truth state estimate or GPS/total station with some Simultaneous Localization and Mapping (SLAM) algorithms, which may not be practical for many applications close to infrastructure with degraded GPS signal or featureless environments. Visual servo can avoid the need to estimate robot pose. Existing works on visual servo for aerial manipulation either address solely end-effector position control or rely on precise velocity measurement and pre-defined visual visual marker with known pattern. Furthermore, most of previous work used under-actuated UAVs, resulting in complicated mechanical and hence control design for the end-effector. This paper develops an image-based visual servo control strategy for bridge maintenance using a fully-actuated UAV. The main components are (1) a visual line detection and tracking system, (2) a hybrid impedance force and motion control system. Our approach does not rely on either robot pose/velocity estimation from an external localization system or pre-defined visual markers. The complexity of the mechanical system and controller architecture is also minimized due to the fully-actuated nature. Experiments show that the system can effectively execute motion tracking and force holding using only the visual guidance for the bridge painting. To the best of our knowledge, this is one of the first studies on aerial manipulation using visual servo that is capable of achieving both motion and force control without the need of external pose/velocity information or pre-defined visual guidance.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Jun 2023.
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Citing papers per year: 2024: 2; 2025: 0; 2026: 2.
4 papers.
- 2024Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomycited by 16
- 2026π, But Make It Fly: Physics-Guided Transfer of VLA Models to Aerial Manipulationcited by 7
- 2024Towards Real-World Aerial Vision Guidance with Categorical 6D Pose Trackercited by 3
- 2026AI-Enabled Image-Based Hybrid Vision/Force Control of Tendon-Driven Aerial Continuum Manipulatorscited by 0
- 2026AI-Enabled Image-Based Hybrid Vision/Force Control of Tendon-Driven Aerial Continuum Manipulators21 Apr 2026
- 2026π, But Make It Fly: Physics-Guided Transfer of VLA Models to Aerial Manipulation26 Mar 2026
- 2024Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy23 Jun 2024
- 2024Towards Real-World Aerial Vision Guidance with Categorical 6D Pose Tracker9 Jan 2024