Probabilistic planning for AUV data harvesting from smart underwater sensor networks

Budd, Matthew; Salavasidis, Georgios; Karnarudzaman, Izzat; Harris, Catherine A.; Phillips, Alexander B.; Duckworth, Paul; Hawes, Nick; Lacerda, Bruno. 2022 Probabilistic planning for AUV data harvesting from smart underwater sensor networks. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 23-27 October 2022. 12051-12057.

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Abstract/Summary

Harvesting valuable ocean data, ranging from climate and marine life analysis to industrial equipment monitoring, is an extremely challenging real-world problem. Sparse underwater sensor networks are a promising approach to scale to larger and deeper environments, but these have difficulty offloading their data without external assistance. Traditionally, offloading data has been achieved by costly, fixed communication infrastructure. In this paper, we propose a planning under uncertainty method that enables an autonomous underwater vehicle (AUV) to adaptively collect data from smart sensor networks in underwater environments. Our novel solution exploits the ability of sensor nodes to provide the AUV with time-of-flight acoustic localisation, and is able to prioritise nodes with the most valuable data. In both simulated experiments and a real-world field trial, we demonstrate that our method outperforms the type of hand-designed behaviours that has previously been used in the context of underwater data harvesting.

Item Type:	Publication - Conference Item (Paper)
Digital Object Identifier (DOI):	10.1109/IROS47612.2022.9981460
Date made live:	01 Feb 2023 13:25 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/533947

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