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Journals / Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi / 2025 / Cilt: 12 Sayı: 2

Autonomous UAV Navigation in Simulated Environments: A Comparative Study of Dijkstra and A* Algorithms

Benzetim Yapılan Ortamlarda Otonom İHA Navigasyonu: Dijkstra ve A* Algoritmalarının Karşılaştırmalı Çalışması

Selman Kayalı(BİLECİK ŞEYH EDEBALİ ÜNİVERSİTESİ)0009-0001-2766-980X
Uğur Yüzgeç(Bilecik Şeyh Edebali Üniversitesi)0000-0002-5364-6265
Murat Özalp(BİLECİK ŞEYH EDEBALİ ÜNİVERSİTESİ)0000-0002-6186-2435
Pages
488–503
Publication Date
2025-11-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results indicate that while Dijkstra guarantees the shortest path, A* achieves faster convergence with minimal deviation in path length, making it more suitable for real-time UAV navigation.

📌 3 Key Takeaways

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Objective & Hypothesis:This study presents a comparative analysis of the Dijkstra and A* algorithms for the autonomous path planning of Unmanned Aerial Vehicles (UAVs) in simulated 2D environments.
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Key Findings & Data:The simulations were conducted in CoppeliaSim (V-REP), a versatile robotics simulation platform, where a quadcopter model navigated through obstacle-rich scenarios by following the shortest path generated by each algorithm.
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Conclusion & Impact:The results indicate that while Dijkstra guarantees the shortest path, A* achieves faster convergence with minimal deviation in path length, making it more suitable for real-time UAV navigation.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:İHA yol planlama · Dijkstra · A* algoritması

Abstract

This study presents a comparative analysis of the Dijkstra and A* algorithms for the autonomous path planning of Unmanned Aerial Vehicles (UAVs) in simulated 2D environments. The simulations were conducted in CoppeliaSim (V-REP), a versatile robotics simulation platform, where a quadcopter model navigated through obstacle-rich scenarios by following the shortest path generated by each algorithm. Both algorithms were implemented using a grid-based graph representation, with the path costs calculated using the Manhattan and Euclidean distances. The UAV visually traced the computed path in real time, avoided obstacles, and returned to the starting point after reaching the target. Performance metrics such as path optimality, computational efficiency, and execution time were evaluated to compare the two approaches. The results indicate that while Dijkstra guarantees the shortest path, A* achieves faster convergence with minimal deviation in path length, making it more suitable for real-time UAV navigation. The visualized simulation framework demonstrates the effectiveness of integrating classical pathfinding algorithms with UAV models in a physics-enabled environment, offering a reproducible testbed for autonomous navigation research.

Özet

Bu çalışma, simüle edilmiş 2D ortamlarda İnsansız Hava Araçlarının (İHA) otonom yol planlaması için Dijkstra ve A* algoritmalarının karşılaştırmalı bir analizini sunmaktadır. Simülasyonlar, çok yönlü bir robotik simülasyon platformu olan CoppeliaSim (V-REP) üzerinde gerçekleştirilmiştir. Bu platformda, bir quadcopter modeli, her algoritma tarafından oluşturulan en kısa yolu takip ederek engellerle dolu senaryolarda navigasyon yapmaktadır. Her iki algoritma da ızgara tabanlı bir grafik temsil kullanılarak uygulanmış ve yol maliyetleri Manhattan ve Öklid mesafeleri ile hesaplanmıştır. İHA, hesaplanan yolu gerçek zamanlı olarak görsel olarak izler, engelleri önler ve hedefe ulaştığında başlangıç noktasına geri döner. Yol optimalitesi, hesaplama verimliliği ve yürütme süresi gibi performans ölçütleri, iki yaklaşımı karşılaştırmak için değerlendirilir. Sonuçlar, Dijkstra'nın en kısa yolu garanti ederken, A*'nın yol uzunluğunda minimum sapma ile daha hızlı yakınsama sağladığını ve bu nedenle gerçek zamanlı İHA navigasyonu için daha uygun olduğunu göstermektedir. Görselleştirilmiş simülasyon çerçevesi, klasik yol bulma algoritmalarının fizik özellikli bir ortamda İHA modelleriyle entegre edilmesinin etkinliğini gösterir ve otonom navigasyon araştırmaları için tekrarlanabilir bir test ortamı sunar.

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Cite This Article

Kayalı, S., Yüzgeç, U., Özalp, M. (2025). Autonomous UAV Navigation in Simulated Environments: A Comparative Study of Dijkstra and A* Algorithms. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, Cilt: 12 Sayı: 2, 488–503. https://doi.org/10.35193/bseufbd.1782323

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