Application Strategy of Unmanned Aerial Vehicle Swarms in Forest Fire Detection Based on the Fusion of Particle Swarm Optimization and Artificial Bee Colony Algorithm
Source abstract
Abstract
Unmanned aerial vehicle (UAV) swarm intelligence technology has shown unique advantages in agricultural and forestry disaster detection, early warning, and prevention with its efficient and precise cooperative operation capability. In this paper, a systematic application strategy of UAV swarms in forest fire detection is proposed, including fire point detection, fire assessment, and control measures, based on the fusion of particle swarm optimization (PSO) and the artificial bee colony (ABC) algorithm. The UAV swarm application strategy provides optimized paths to quickly locate multiple mountain forest fire points in 3D forest modeling environments and control measures based on the analysis of the fire situation. This work lays a research foundation for studying the precise application of UAV swarm technology in real-world forest fire detection and prevention.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing and Land Use
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
College of Artificial Intelligence, Xinjiang Vocational and Technical College of Communication, Urumqi 831401, China
References (36)
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