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Conference Papers Year : 2024

LOTUS: Learning from Operational Teaming with Unmanned Systems

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 poweredby machine learning will enhance the overall effectiveness of the team, constituting a significant breakthrough.
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Dates and versions

hal-04481318 , version 1 (18-04-2024)

Identifiers

  • HAL Id : hal-04481318 , version 1

Cite

Helene Lechene, Benoit Clement, Karl Sammut, Paulo Santos, Andrew Cunningham, et al.. LOTUS: Learning from Operational Teaming with Unmanned Systems. 2024 IEEE Oceans Conference, Apr 2024, Singapour, Singapore. ⟨hal-04481318⟩
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