SWiMM DEEPeR: A Simulated Underwater Environment for Tracking Marine Mammals Using Deep Reinforcement Learning and BlueROV2

Appleby, S; Crane, K; Bergami, G; McGough, AS

doi:10.1109/CoG57401.2023.10333168

SWiMM DEEPeR: A Simulated Underwater Environment for Tracking Marine Mammals Using Deep Reinforcement Learning and BlueROV2

Lookup NU author(s): Sam Appleby, Dr Kirsten Crane, Dr Giacomo Bergami ORCiD, Dr Stephen McGough

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

This paper offers a feasibility study on using simulated environments for training autonomous underwater vehicles (AUVs). With the goal of monitoring marine megafauna, we propose a Unity-hosted simulation of a realistic open ocean environment, with a focus on simulating Blue Robotics’ BlueROV2. The result is SWiMM DEEPeR ¹ , coupling the former simulation with a reinforcement learning (RL) pipeline. Animated marine mammal models emulate the target objects of the real-world deployment scenario, offering a solution in a new application space (conservation) as well as a new problem space (visual active tracking). We provide experiments with respect to each stage of the proposed pipeline: i) image similarity experiments provide evidence for decisions around image rendering and data transfer, ii) autoencoder training demonstrates the feasibility of mapping raw images to low-dimensional feature representations, iii) agent training demonstrates successful self-learnt vehicle control.