Tarımda Optimal Drone Uçuş Rotası Planlaması İçin Genetik ve Açgözlü Algoritmanın Karşılaştırmalı Analizi

Yıl 2024, , 129 - 142, 29.02.2024

Eray Önler

https://doi.org/10.7161/omuanajas.1394616

Öz

Bu çalışmada Genetik Algoritmanın (GA) tarımsal dronların uçuş rotasını optimize etmedeki performansı Açgözlü Algoritma ile karşılaştırılmıştır. GA'nın ortalama %17,44 daha kısa rotalar ürettiği görülmüştür. Statik olarak simüle edilen bir saha modelinde, genetik algoritmada 500 nesil üzerinden ölçülen bu verimlilik, tarımsal faaliyetlerde kaynak ve zaman tasarrufu açısından önemli bir potansiyele işaret etmektedir. GA'nın etkinliğine rağmen hesaplama yoğunluğu, gerçek zamanlı saha uygulamalarını sınırlandırmaktadır, ancak önceden uygulama haritası hazırlanmış alanlar için çevrimdışı rota planlamada avantajlar sunmaktadır. Algoritmaların rastgele olarak üretilen uçuş simülasyonlarında üretmiş oldukları rota uzunlukları arasında t-testi kullanılarak yapılan karşılaştırmada GA tarafından üretilen rotaların istatistiksel olarak anlamlı seviyede kısa olduğu görülmüştür (p=7.18×10−9). Gelecekteki araştırmalarda, GA'ların dron rota optimizasyonunda pratik kullanımını geliştirmek için simülasyonda kullanılan basitleştirilmiş model ile gerçek dünya uygulamalarındaki karmaşıklık arasında bulunan farkları gidermek amaçlanacaktır.

Anahtar Kelimeler

Dron rota optimizasyonu, tarımsal dron, çevrimdışı rota planlama

Comparative Analysis of Genetic and Greedy Algorithm for Optimal Drone Flight Route Planning in Agriculture

Öz

In this study, the performance of the Genetic Algorithm (GA) in optimizing the agricultural drone flight route was compared with the Greedy Algorithm, revealing that GA produce routes that are, on average, 17.44 % more efficient. This efficiency, measured over 500 generations in a static field model, suggests substantial potential for saving resources and time in agricultural operations. Despite the effectiveness of the GA, its computational intensity limits real-time field applications, but offers advantages in offline route planning for pre-mapped areas. A t-test between flight lengths created by the algorithms highlighted a significant difference, with a p-value of approximately 7.18×10−9, indicating the GA's superior performance. Future research should aim to bridge the gap between the simplified binary field model used in simulations and the complexities of real-world agricultural landscapes to improve the practical deployment of GAs in drone route optimization.

Anahtar Kelimeler

Agricultural drone, drone route optimization, offline route planning

Kaynakça

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