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Daily Arxiv

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Stonefish: Supporting Machine Learning Research in Marine Robotics

Created by
  • Haebom

Author

Michele Grimaldi, Patryk Cieslak, Eduardo Ochoa, Vibhav Bharti, Hayat Rajani, Ignacio Carlucho, Maria Koskinopoulou, Yvan R. Petillot, Nuno Gracias

Outline

This paper presents recent improvements to Stonefish, an open source simulator used in the field of marine robotics. Since testing in real marine environments is expensive and logistically difficult, simulators are essential for the development and improvement of marine robotics algorithms. The Stonefish simulator has been enhanced with updates such as the addition of various sensors such as event-based cameras, thermal cameras, and optical flow cameras, support for optical optical communication, support for mooring operations, improved thruster modeling, improved hydrodynamic models, and improved sonar accuracy. In addition, it has been improved to contribute to research in the field of machine learning, where collecting accurate data is particularly difficult, by adding an automatic annotation tool.

Takeaways, Limitations

Takeaways:
Enhanced capabilities of the Stonefish simulator can significantly increase the efficiency of developing and testing marine robotics algorithms.
The addition of various sensors and capabilities enables the provision of critical data for machine learning-based marine robotics research.
Being open source ensures accessibility to a wide range of researchers.
Limitations:
Perfect matching with real marine environments can be difficult. Additional verification is needed to ensure that simulation results are equally applicable to real environments.
There is a possibility that simulation speed may decrease due to the addition of new features.
Further evaluation of the accuracy and reliability of the simulator is needed.
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