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

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


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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.

Publication metadata

Author(s): Appleby S, Crane K, Bergami G, McGough AS

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IEEE Conference on Games

Year of Conference: 2023

Pages: 1-8

Online publication date: 04/12/2023

Acceptance date: 09/05/2023

ISSN: 9798350322774

Publisher: IEEE


DOI: 10.1109/CoG57401.2023.10333168