Disaster Area Coverage Optimisation Using Reinforcement Learning
Ciaran Gruffeille, Adolfo Perrusquía, Antonios Tsourdos, Weisi Guo
Abstract
Search and rescue applications using unmanned aerial vehicles (UAVs) also known as drones are becoming a research topic of interest to industry and academia due to its high impact in the ecosystem and people. Exploration of the disaster area is a crucial element in search and rescue to identify the zones that require immediate assistance or with high hazard probability. This paper aims to contribute in the coverage optimisation of a disaster area using drones. We focus on a flood disaster scenario as case of study. The proposed approach consists in two main parts: i) a Siamese-net is used to identify flooded buildings in satellite images, and ii) the points of interest are converted into a suitable maze environment that subsequently is used by any reinforcement learning (RL) architecture for area coverage optimisation. Here, the goal of the RL architecture is to ensure that the complete environment is covered by the drone by optimizing time and previously visited zones. Experiments are conducted to show the benefits and challenges of the current approach.