An Efficient Approach with Dynamic Multi-Swarm of UAVs for Forest Firefighting

TL;DR

This paper introduces MSCIDC, a multi-swarm UAV approach for rapid forest fire detection and mitigation, significantly reducing fire damage and mission time through dynamic swarm cooperation and divide-and-conquer strategies.

Contribution

The paper presents a novel multi-swarm cooperative approach with dynamic regulation and divide-and-conquer control for faster forest fire mitigation using UAVs.

Findings

Reduces forest area burnt by 65%

Decreases mission time by 60%

Outperforms existing multi-UAV methods in simulations

Abstract

In this paper, the Multi-Swarm Cooperative Information-driven search and Divide and Conquer mitigation control (MSCIDC) approach is proposed for faster detection and mitigation of forest fire by reducing the loss of biodiversity, nutrients, soil moisture, and other intangible benefits. A swarm is a cooperative group of Unmanned Aerial Vehicles (UAVs) that fly together to search and quench the fire effectively. The multi-swarm cooperative information-driven search uses a multi-level search comprising cooperative information-driven exploration and exploitation for quick/accurate detection of fire location. The search level is selected based on the thermal sensor information about the potential fire area. The dynamicity of swarms, aided by global regulative repulsion and merging between swarms, reduces the detection and mitigation time compared to the existing methods. The local attraction among the members of the swarm helps the non-detector members to reach the fire location faster, and divide-and-conquer mitigation control ensures a non-overlapping fire sector allocation for all members quenching the fire. The performance of MSCIDC has been compared with different multi-UAV methods using a simulated environment of pine forest. The performance clearly shows that MSCIDC mitigates fire much faster than the multi-UAV methods. The Monte-Carlo simulation results indicate that the proposed method reduces the average forest area burnt by $65\%$ and mission time by $60\%$ compared to the best result case of the multi-UAV approaches, guaranteeing a faster and successful mission.