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Robust image-based control for aerial interaction under external disturbances.

Junhao Zeng1, Hang Zhong2, Jiacheng Liang1

  • 1College of Electrical and Information Engineering, Hunan University, No. 2 Lushan Road, Changsha, 410082, China; National Engineering Research Center for Robot Visual Perception and Control Technology, Changsha, 410082, China.

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|April 23, 2026
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Summary
This summary is machine-generated.

This study introduces a robust aerial manipulator control framework for infrastructure inspection, improving force-tracking accuracy and stability during physical interaction tasks. The new system significantly reduces contact force errors, enhancing inspection reliability.

Keywords:
Aerial physical interactionExternal disturbance estimationForce controlNonlinear model predictive controlVisual servoing

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Area of Science:

  • Robotics
  • Control Systems
  • Computer Vision

Background:

  • Aerial manipulators (AMs) face challenges in infrastructure inspection due to coupling, uncertainties, and disturbances.
  • Robust control is essential for precise contact inspection and aerial physical interaction (APhI).

Purpose of the Study:

  • To develop a robust image-based control framework for AMs performing contact inspection.
  • To enhance force-tracking accuracy and robustness against disturbances and constraints.

Main Methods:

  • Derived a decoupled second-order image dynamics model using virtual-camera image moments.
  • Developed a disturbance observer-based nonlinear model predictive controller (NMPC) with online disturbance estimation and constraint enforcement.
  • Designed an image-based variable-stiffness impedance controller for force-tracking and a geometric attitude controller to prevent Euler angle singularities.

Main Results:

  • Achieved significant reduction in contact force Root Mean Square Error (RMSE): 31.6% in simulation and 58.3% in experiments.
  • Demonstrated improved tracking accuracy and robustness in aerial physical interaction (APhI) tasks.
  • Verified force-tracking accuracy and reproducibility across multiple trials.

Conclusions:

  • The proposed image-based control framework enhances aerial manipulator performance in contact inspection.
  • The system offers superior robustness and force-tracking capabilities compared to existing methods.
  • Validated the framework's effectiveness for reliable aerial physical interaction (APhI) applications.