LOTUS: learning from operational teaming with unmanned systems
Date
2024
Authors
Lechene, H.
Clement, B.
Sammut, K.
Santos, P.
Cunningham, A.
Coppin, G.
Buche, C.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Oceans -Conference-, 2024, pp.1-5
Statement of Responsibility
Conference Name
OCEANS 2024 - Singapore (15 Apr 2024 - 18 Apr 2024 : Singapore)
Abstract
The LOTUS project aims at improving maritime surveillance. In this context, this position paper presents ongoing contributions, including novel machine learning algorithms for multi-agent systems to be applied to groups of underwater drones involved in surveillance missions. It emphasises incorporating human-machine teaming to bolster decision-making in maritime scenarios. The expected outcomes of this project comprise the robust control of groups of autonomous vehicles, adaptable to environmental changes, as well as an effective reporting method. Mission summaries will be delivered to human operators by way of narratives about the relevant events detected thanks to drones. The integration of this narrative construction powered by machine learning will enhance the overall effectiveness of the team, constituting a significant breakthrough.
School/Discipline
Dissertation Note
Provenance
Description
Link to a related website: https://unpaywall.org/10.1109/OCEANS51537.2024.10682309, Open Access via Unpaywall
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Rights
Copyright 2024 The IEEE Oceanic Engineering Society, Inc. All rights reserved.