LOTUS: Learning from Operational Teaming with Unmanned Systems

Helene Lechene (1) , Benoit Clement (2, 3, 4, 5) , Karl Sammut (2) , Paulo Santos (2, 3) , Andrew Cunningham (6) , Gilles Coppin (7, 8) , Cédric Buche (9, 1)

1 NGP - Naval Group Pacific
2 Flinders University [Adelaide, Australia]
3 CROSSING - FrenCh austRalian labOratory for humanS/autonomouS agents teamING
4 ENSTA Bretagne - École Nationale Supérieure de Techniques Avancées Bretagne
5 Lab-STICC_MATRIX - Equipe Models and AlgoriThms for pRocessIng and eXtracting information
6 University of South Australia [Adelaide]
7 Lab-STICC_INUIT - Equipe Immersive Natural User Interaction team
8 IMT Atlantique - INFO - Département Informatique
9 ENIB - École Nationale d'Ingénieurs de Brest

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.

Domains: Automatic Control Engineering; Artificial Intelligence [cs.AI]; Multiagent Systems [cs.MA]; Environmental Engineering

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⟩