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Vision driven trailer loading for autonomous surface vehicles in dynamic environments.

Jianwen Li1, Jalil Chavez-Galaviz1, Nina Mahmoudian1

  • 1School of Mechanical Engineering, Purdue University, West Lafayette, IN, United States.

Frontiers in Robotics and AI
|October 8, 2025
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Summary

This study introduces a vision-based system for autonomous surface vehicle (ASV) trailer loading without GPS. The novel framework achieves high success rates in various wave conditions, enabling robust navigation in GPS-denied environments.

Keywords:
autonomous surface vehicle (ASV)autonomous trailer loadingfinite state machine (FSM)object detectionvision-based navigation

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Area of Science:

  • Marine Robotics
  • Computer Vision
  • Autonomous Systems

Background:

  • Automated docking for marine vessels is advancing, but trailer loading for autonomous surface vehicles (ASVs) is underexplored.
  • Existing methods often rely on GPS, limiting adaptability in dynamic or GPS-denied environments.

Purpose of the Study:

  • To present a novel, vision-based framework for autonomous trailer loading.
  • To enable ASV trailer loading without relying on GPS, enhancing environmental adaptability.

Main Methods:

  • Integration of real-time computer vision with a finite state machine (FSM) control strategy.
  • Utilizing visual cues like LED panels and bunk boards for ASV detection, approach, and alignment.
  • Development and use of a realistic simulation environment with wave disturbances for validation.

Main Results:

  • Demonstrated 100% success rate in calm to medium wave disturbances.
  • Achieved a 90% success rate under high wave conditions.
  • Validated the system's robustness and adaptability in a realistic simulation and experimental setup.

Conclusions:

  • The vision-driven system offers a promising solution for fully autonomous trailer loading.
  • The framework's GPS-independent nature makes it suitable for dynamic and unstructured marine environments.
  • The developed simulation environment is available for further research and validation.