Wildfire detection in large-scale environments using force-based control for swarms of UAVs
Document Type: Journal Article
Author(s): Georgios Tzoumas; Lenka Pitonakova; Lucio Salinas; Charles Scales; Thomas Richardson
Publication Year: 2023

Cataloging Information

  • drone swarms
  • dynamic space partition
  • fire detection
  • physicomimetics
  • UAV - unmanned aerial vehicles
  • wildfires
Record Maintained By:
Record Last Modified: February 24, 2023
FRAMES Record Number: 66846


Wildfires affect countries worldwide as global warming increases the probability of their appearance. Monitoring vast areas of forests can be challenging due to the lack of resources and information. Additionally, early detection of wildfires can be beneficial for their mitigation. To this end, we explore in simulation the use of swarms of uncrewed aerial vehicles (UAVs) with long autonomy that can cover large areas the size of California to detect early stage wildfires. Four decentralised control algorithms are tested: (1) random walking, (2) dispersion, (3) pheromone avoidance and (4) dynamic space partition. The first three adaptations are known from literature, whereas the last one is newly developed. The algorithms are tested with swarms of different sizes to test the spatial coverage of the system in 24 h of simulation time. Best results are achieved using a version of the dynamic space partition algorithm (DSP) which can detect 82% of the fires using only 20 UAVs. When the swarm consists of 40 or more aircraft 100% coverage can also be achieved. Further tests of DSP show robustness when agents fail and when new fires are generated in the area.

Online Link(s):
Tzoumas, Georgios; Pitonakova, Lenka; Salinas, Lucio; Scales, Charles; Richardson, Thomas; Hauert, Sabine. 2023. Wildfire detection in large-scale environments using force-based control for swarms of UAVs. Swarm Intelligence 17(1):89-115.