Stonefish: Supporting Machine Learning Research in Marine Robotics
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arXiv
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| Format: | Preprint |
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2025
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| author | Grimaldi, Michele Cieslak, Patryk Ochoa, Eduardo Bharti, Vibhav Rajani, Hayat Carlucho, Ignacio Koskinopoulou, Maria Petillot, Yvan R. Gracias, Nuno |
| author_facet | Grimaldi, Michele Cieslak, Patryk Ochoa, Eduardo Bharti, Vibhav Rajani, Hayat Carlucho, Ignacio Koskinopoulou, Maria Petillot, Yvan R. Gracias, Nuno |
| contents | Simulations are highly valuable in marine robotics, offering a cost-effective and controlled environment for testing in the challenging conditions of underwater and surface operations. Given the high costs and logistical difficulties of real-world trials, simulators capable of capturing the operational conditions of subsea environments have become key in developing and refining algorithms for remotely-operated and autonomous underwater vehicles. This paper highlights recent enhancements to the Stonefish simulator, an advanced open-source platform supporting development and testing of marine robotics solutions. Key updates include a suite of additional sensors, such as an event-based camera, a thermal camera, and an optical flow camera, as well as, visual light communication, support for tethered operations, improved thruster modelling, more flexible hydrodynamics, and enhanced sonar accuracy. These developments and an automated annotation tool significantly bolster Stonefish's role in marine robotics research, especially in the field of machine learning, where training data with a known ground truth is hard or impossible to collect. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11887 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stonefish: Supporting Machine Learning Research in Marine Robotics Grimaldi, Michele Cieslak, Patryk Ochoa, Eduardo Bharti, Vibhav Rajani, Hayat Carlucho, Ignacio Koskinopoulou, Maria Petillot, Yvan R. Gracias, Nuno Robotics Artificial Intelligence Systems and Control Simulations are highly valuable in marine robotics, offering a cost-effective and controlled environment for testing in the challenging conditions of underwater and surface operations. Given the high costs and logistical difficulties of real-world trials, simulators capable of capturing the operational conditions of subsea environments have become key in developing and refining algorithms for remotely-operated and autonomous underwater vehicles. This paper highlights recent enhancements to the Stonefish simulator, an advanced open-source platform supporting development and testing of marine robotics solutions. Key updates include a suite of additional sensors, such as an event-based camera, a thermal camera, and an optical flow camera, as well as, visual light communication, support for tethered operations, improved thruster modelling, more flexible hydrodynamics, and enhanced sonar accuracy. These developments and an automated annotation tool significantly bolster Stonefish's role in marine robotics research, especially in the field of machine learning, where training data with a known ground truth is hard or impossible to collect. |
| title | Stonefish: Supporting Machine Learning Research in Marine Robotics |
| topic | Robotics Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2502.11887 |