An Efficient Approach With Dynamic Multiswarm of UAVs for Forest Firefighting

This is the authors' abstract. We don't add key points for this paper.

This article proposes the multiswarm cooperative information-driven search and divide and conquer mitigation control (MSCIDC) approach for faster detection and mitigation of forest fires by reducing the loss of biodiversity, nutrients, soil moisture, and other intangible benefits. A swarm is a cooperative group of unmanned aerial vehicles (UAVs) flying together to search and quench the fire areas effectively. The multiswarm cooperative information-driven search uses a two-stage search comprising cooperative information-driven exploration and exploitation for quick/accurate detection of fire locations. The search level is selected based on the thermal sensor information about the potential fire area. The dynamic nature of swarms acquired from global regulative repulsion and merging between swarms reduces the detection and mitigation time compared to the existing methods. The local attraction among the swarm members helps the nondetector members reach the fire location faster, and divide-and-conquer mitigation control ensures a nonoverlapping fire sector allocation for all members quenching the fire. The performance of the MSCIDC has been compared with different multi-UAV methods using a simulated pine forest environment. The Monte-Carlo simulation results indicate that the MSCIDC reduces the average forest area burnt by$65%$and mission time by$60%$compared to the best case of the multi-UAV approaches, guaranteeing a faster and more successful mission.