Assessment of Area Coverage with Swarm Coordination Rules in Agricultural Monitoring Drones
Keywords:
Precision Agriculture, Swarm Robotics, Area Coverage, Unmanned Aerial Vehicles, Decentralized CoordinationAbstract
The integration of multiple unmanned aerial vehicles into cohesive swarms offers transformative potential for precision agriculture, particularly concerning large scale area coverage and real time environmental monitoring. This paper investigates the efficacy of decentralized swarm coordination rules in optimizing area coverage for agricultural monitoring missions. Utilizing a comprehensive systems experiment approach, the research evaluates how fundamental coordination parameters such as repulsion, alignment, and attraction influence the spatial distribution and coverage efficiency of drone swarms operating over heterogeneous crop fields. The methodology encompasses both high fidelity computational simulations and empirical field experiments involving a customized fleet of rotary wing unmanned aerial vehicles equipped with multispectral imaging sensors. The analysis focuses on quantifying the relationship between specific algorithmic coordination thresholds and the resulting area coverage metrics, including total spatial footprint, revisit frequency, and sensor overlap redundancy. Findings indicate that dynamically adjusting coordination rules based on local drone density significantly enhances overall coverage efficiency while mitigating the risks of inter drone collisions and spatial clustering. The study provides concrete systems experiment evidence that decentralized swarm intelligence can outperform traditional pre programmed flight paths in dynamic agricultural environments, offering a scalable solution for modern farm management.References
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