Monitoring and Cordoning Wildfires with an Autonomous Swarm of Unmanned Aerial Vehicles

Fabrice Saffre (Corresponding Author), Hanno Hildmann, Hannu Karvonen, Timo Lind

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
7 Downloads (Pure)

Abstract

Unmanned aerial vehicles, or drones, are already an integral part of the equipment used by firefighters to monitor wildfires. They are, however, still typically used only as remotely operated, mobile sensing platforms under direct real-time control of a human pilot. Meanwhile, a substantial body of literature exists that emphasises the potential of autonomous drone swarms in various situational awareness missions, including in the context of environmental protection. In this paper, we present the results of a systematic investigation by means of numerical methods i.e., Monte Carlo simulation. We report our insights into the influence of key parameters such as fire propagation dynamics, surface area under observation and swarm size over the performance of an autonomous drone force operating without human supervision. We limit the use of drones to perform passive sensing operations with the goal to provide real-time situational awareness to the fire fighters on the ground. Therefore, the objective is defined as being able to locate, and then establish a continuous perimeter (cordon) around, a simulated fire event to provide live data feeds such as e.g., video or infra-red. Special emphasis was put on exclusively using simple, robust and realistically implementable distributed decision functions capable of supporting the self-organisation of the swarm in the pursuit of the collective goal. Our results confirm the presence of strong nonlinear effects in the interaction between the aforementioned parameters, which can be closely approximated using an empirical law. These findings could inform the mobilisation of adequate resources on a case-by-case basis, depending on known mission characteristics and acceptable odds (chances of success).
Original languageEnglish
Article number301
JournalDrones
Volume6
Issue number10
DOIs
Publication statusPublished - Oct 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • autonomous decision-making
  • collective intelligence
  • decentralised control
  • drone swarms
  • drones
  • forest fire
  • numerical experiment
  • situational awareness
  • UAV
  • wildfire

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  • FUAVE: Finnish UAV Ecosystem

    Karvonen, H.

    1/07/2031/12/22

    Project: Academy of Finland project

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