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Updated: Dec 13, 2025

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A Novel Visual Sensor Stabilization Platform for Robotic Sharks Based on Improved LADRC and Digital Image Algorithm.

Jie Pan1, Pengfei Zhang2,3, Jincun Liu1

  • 1State Key Laboratory for Turbulence and Complex Systems, Department of Mechanics and Engineering Science, BIC-ESAT, College of Engineering, Peking University, Beijing 100871, China.

Sensors (Basel, Switzerland)
|July 26, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel stabilization strategy for robotic fish, combining mechanical and digital methods to achieve steady underwater visual sensing. This enhances robotic fish navigation and target recognition in dynamic aquatic environments.

Keywords:
digital techniqueimproved LADRCrobotic fishstabilization platformvisual sensor

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Area of Science:

  • Robotics
  • Computer Vision
  • Control Systems

Background:

  • Autonomous underwater missions demand stable visual sensing, which is difficult for bionic robotic fish due to space constraints and tail-beat disturbances.
  • Existing methods struggle to provide continuous, steady image sequences essential for navigation and data acquisition.

Purpose of the Study:

  • To develop and validate a hybrid stabilization strategy for robotic fish visual systems.
  • To enhance the stability of visual sensors against periodic disturbances in underwater environments.

Main Methods:

  • Implemented an improved window function-based linear active disturbance rejection control (LADRC) for mechanical stabilization.
  • Developed a rapid algorithm utilizing inertial measurement units (IMUs) for digital image stabilization.
  • Utilized an experimental platform simulating fishlike oscillations for testing.

Main Results:

  • Demonstrated the effectiveness of the mechanical stabilization strategy in reducing visual sensor instability.
  • Validated the digital stabilization algorithm's ability to counteract periodic disturbances.
  • Confirmed successful target recognition capabilities with the stabilized visual system.

Conclusions:

  • The proposed hybrid approach significantly improves visual sensor stability for robotic fish.
  • This research provides a foundation for advanced visual applications of robotic fish in complex aquatic settings.