Disaster Area Coverage Optimisation Using Reinforcement Learning

Gruffeille, Ciaran; Perrusquía, Adolfo; Tsourdos, Antonios; Guo, Weisi

doi:10.1109/icuas60882.2024.10557095

2024 International Conference on Unmanned Aircraft Systems (ICUAS) 2024

DOI: 10.1109/icuas60882.2024.10557095

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Disaster Area Coverage Optimisation Using Reinforcement Learning

Ciaran Gruffeille,

Adolfo Perrusquía,

Antonios Tsourdos

et al.

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Cited by 5 publications

References 34 publications

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Wildfire and smoke early detection for drone applications: A light-weight deep learning approach

Kumar,

Perrusquía,

Al-Rubaye

et al. 2024

Engineering Applications of Artificial Intelligence

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Wildfire and smoke early detection for drone applications: A light-weight deep learning approach

Kumar,

Perrusquía,

Al-Rubaye

et al. 2024

Engineering Applications of Artificial Intelligence

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Uncovering Reward Goals in Distributed Drone Swarms Using Physics-Informed Multiagent Inverse Reinforcement Learning

Perrusquía,

Guo

2025

IEEE Trans. Cybern.

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Flight Plan Optimisation of Unmanned Aerial Vehicles with Minimised Radar Observability Using Action Shaping Proximal Policy Optimisation

Ali,

Perrusquía,

Guo

et al. 2024

Drones

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The increasing use of unmanned aerial vehicles (UAVs) is overwhelming air traffic controllers for the safe management of flights. There is a growing need for sophisticated path-planning techniques that can balance mission objectives with the imperative to minimise radar exposure and reduce the cognitive burden of air traffic controllers. This paper addresses this challenge by developing an innovative path-planning methodology based on an action-shaping Proximal Policy Optimisation (PPO) algorithm to enhance UAV navigation in radar-dense environments. The key idea is to equip UAVs, including future stealth variants, with the capability to navigate safely and effectively, ensuring their operational viability in congested radar environments. An action-shaping mechanism is proposed to optimise the path of the UAV and accelerate the convergence of the overall algorithm. Simulation studies are conducted in environments with different numbers of radars and detection capabilities. The results showcase the advantages of the proposed approach and key research directions in this field.

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Disaster Area Coverage Optimisation Using Reinforcement Learning

Cited by 5 publications

References 34 publications

Wildfire and smoke early detection for drone applications: A light-weight deep learning approach

Wildfire and smoke early detection for drone applications: A light-weight deep learning approach

Uncovering Reward Goals in Distributed Drone Swarms Using Physics-Informed Multiagent Inverse Reinforcement Learning

Flight Plan Optimisation of Unmanned Aerial Vehicles with Minimised Radar Observability Using Action Shaping Proximal Policy Optimisation

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