Browsing by Author Lucio, Salinas
Showing results [1 - 1] / 1
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. |
