Multi Objective UAV Network Deployment for Dynamic Fire Coverage

Recent large wildfires and subsequent damage have increased the importance of wildfire monitoring and tracking. However, human monitoring on the ground or in the air may be too dangerous and we thus investigate deploying Unmanned Aerial Vehicles (UAVs) to track wildfires. Specifically, we attack the problem of distributed autonomous control of UAVs using a set of potential fields to track wildfire boundaries. A multi-objective evolutionary algorithm searches through the space of potential field parameters to maximize fire coverage while minimizing energy consumption. Fire spread is modelled by the well known FARSITE fire model. Preliminary simulation results show that our potential fields approach to UAV control leads to 100% coverage of the boundary by UAVs and 78.1% energy remaining on three testing scenarios.