Stonefish: Supporting Machine Learning Research in Marine Robotics

TL;DR

Stonefish is an enhanced open-source marine robotics simulator that supports machine learning research by providing diverse sensors, communication options, and improved modeling to generate reliable training data in underwater environments.

Contribution

The paper introduces significant updates to the Stonefish simulator, including new sensors, communication methods, and modeling improvements, facilitating advanced marine robotics and machine learning research.

Findings

Enhanced sensor suite enables diverse data collection.

Improved hydrodynamics and thruster models increase simulation realism.

Automated annotation tool aids in training data generation.

Abstract

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.