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Abstract

Wildfires, also known as forest or wildland fires, are uncontrolled vegetation fires occurring in rural areas that cause tremendous damage to the society, harming environment, property and people. The firefighting endeavor is a dull, dirty and dangerous job and as such, can greatly benefit from automation to reduce human exposure to hazards. Aerial remote sensing is a common technique to obtain precise information about a wildfire state so fire response teams can prepare countermeasures. This task, when performed with manned aerial vehicles, expose operators to high risks that can be eliminated by the use of autonomous vehicles. This thesis introduces a wildfire monitoring system based on fleets of unmanned aerial vehicles (UAVs) to provide firefighters with timely updated information about a wildland fire. We present an approach to plan trajectories for a fleet of fixed-wing UAVs to observe a wildfire evolving over time. Realistic models of the terrain, of the fire propagation process, and of the UAVs are exploited, together with a model of the wind, to predict wildfire spread and plan UAV motion. The approach tailors a generic Variable Neighborhood Search method to these models and the associated constraints. The execution of the planned monitoring mission provides wildfire maps that are transmitted to the fire response team and exploited by the planning algorithm to plan new observation trajectories. Algorithms and models are integrated within a software architecture allowing for execution under scenarios with different levels of realism, with real and simulated UAVs flying over a real or synthetic wildfire. Mixed-reality simulation results show the ability to plan observation trajectories for a small fleet of UAVs, and to update the plans when new information on the fire are incorporated in the fire model.

Bibtex

@phdthesis{bailonruiz:tel-02995471,
  title = {{Design of a wildfire monitoring system using fleets of Unmanned Aerial Vehicles}},
  author = {Bailon-Ruiz, Rafael},
  url = {https://hal.laas.fr/tel-02995471},
  number = {2020ISAT0011},
  hal_local_reference = {Rapport LAAS N{\textdegree} 20261},
  school = {{INSA de Toulouse}},
  year = {2020},
  month = sep,
  keywords = {Fleets of UAVs ; Wildfire monitoring ; Multi-Robot planning ; Mixed-reality simulation ; Remote sensing ; Flotte de drones ; Surveillance de feux de for{\^e}t ; Planification multi-Robot ; Simulation {\`a} r{\'e}alit{\'e} mixte ; T{\'e}l{\'e}d{\'e}tection},
  hal_id = {tel-02995471},
  hal_version = {v3},
  language = {en}
}