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A novel search and survey technique for unmanned aerial systems in detecting and estimating the area for wildfires

Author(s): Mrinmoy Sarkar, Xuyang Yan, Berat A. Erol, Ioannis Raptis, Abdollah Homaifar
Year Published: 2021
Description:

In recent years Unmanned Aerial Vehicles (UAVs) have progressively been utilized for wildfire management, and are especially in prevalent in forest fire monitoring missions. To ensure the fast detection and accurate area estimation of forest fires, a two-step search and survey algorithm for multi-UAV system is proposed to address these fire scenarios. Initially, a grid-based partition method is applied to divide the area-of-interest into several search areas. Then, an archetype search pattern is used to provide timely UAV exploration within those sub-areas. Once the fire zones are detected, a novel survey strategy is employed for UAVs to discover the boundary points of the fire zones, so that the area of the fire zones can be estimated using the sampled boundary points. In addition, the effect of wind is accounted for improving fire zone boundary estimates. The proposed search-and-survey procedure is validated on multiple simulated scenarios using the U.S. Air Force’s mission-realistic Aerospace Multi-Agent Simulation Environment (AMASE) software. Simulation results showcase that the proposed search pattern can effectively discover the seeded fire zones within 40 min of the mission. This is relatively faster than the other two well-known search patterns. Moreover, the proposed survey technique provides a coverage estimate with at least 85% accuracy for the area of interest within 90 min of the mission.

Citation: Sarkar, Mrinmoy; Yan, Xuyang; Erol, Berat A.; Raptis, Ioannis; Homaifar, Abdollah. 2021. A novel search and survey technique for unmanned aerial systems in detecting and estimating the area for wildfires. Robotics and Autonomous Systems 145:103848. https://doi.org/10.1016/j.robot.2021.103848
Topic(s): Fire Effects, Ecological - Second Order, Effects Monitoring, Mapping
Ecosystem(s): None
Document Type: Book or Chapter or Journal Article
NRFSN number: 23888
FRAMES RCS number: 64133
Record updated: Nov 19, 2021