Reinforcement Learning Applied to Train Autonomous Maritime Search and Rescue Drones

  • Jorás C. C. de Oliveira Insper
  • Pedro H. B. A. Andrade Insper
  • Renato L. Falcão Insper
  • Ricardo R. Rodrigues Insper
  • José Fernando Brancalion Embraer
  • Fabrício J. Barth Insper

Resumo


This paper presents a Search and Rescue (SAR) environment tailored for locating shipwrecked individuals and evaluation of Reinforcement Learning (RL) algorithms under different scenarios, considering a variety of different hypotheses, and an extensive number of experiments. Our findings indicate that RL techniques, particularly Proximal Policy Optimization (PPO), significantly outperform traditional greedy algorithms regarding success rates. Centralized network architectures demonstrate superior convergence compared to decentralized ones. Historical search data does not notably enhance algorithm performance, suggesting that real-time observations are sufficient. Agents are able to naturally parallelize the search efforts within a given probability zone while prioritizing higher probability areas first. Finally, while managing multiple persons-in-water (PIWs) increases complexity, agents show effective coordination and improvement over time, underscoring the potential of RL in complex SAR missions. This study highlights the promising role of RL in optimizing SAR operations.
Palavras-chave: maritime search and rescue, deep reinforcement learning

Referências

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Publicado
17/11/2024
OLIVEIRA, Jorás C. C. de; ANDRADE, Pedro H. B. A.; FALCÃO, Renato L.; RODRIGUES, Ricardo R.; BRANCALION, José Fernando; BARTH, Fabrício J.. Reinforcement Learning Applied to Train Autonomous Maritime Search and Rescue Drones. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 328-339. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2024.245030.