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

Abstract

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.

Keywords

• İHA yol planlama
• Dijkstra
• A* algoritması
• CoppeliaSim
• otonom navigasyon
• en kısa yol

Supporting Institution

Bu araştırmayı desteklemek için dış fon kullanılmamıştır.

Ethical Statement

Bu çalışmanın hazırlanma sürecinde bilimsel ve etik ilkelere uyulduğu ve yararlanılan tüm çalışmaların kaynakçada belirtildiği beyan olunur.

Thanks

Bu çalışma, Bilecik Şeyh Edebali Üniversitesi Bilgisayar Mühendisliği Bölümü'nde bir bitirme projesi kapsamında gerçekleştirilmiştir. Yazarlar, bu çalışma boyunca akademik destek ve altyapı sağladığı için Bilgisayar Mühendisliği Bölümü'ne içten teşekkürlerini sunar. Simülasyonun kaynak kodu şu adreste mevcuttur: https://github.com/retnap/Dijkstra-and-A-Search-Algorithm-with-CoppeliaSim. CoppeliaSim ortamı ve İHA yol izleme ile ilgili ek tanıtım videolarına YouTube kanalından ulaşılabilir: https://www.youtube.com/@selmankayal2006.

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

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.

Keywords

• UAV path planning
• Dijkstra
• A* algorithm
• CoppeliaSim
• autonomous navigation
• shortest path

Supporting Institution

The author(s) acknowledge that they received no external funding to support this research.

Ethical Statement

It is declared that scientific and ethical principles were followed during the preparation of this study and that all studies used are stated in the bibliography.

Thanks

This study was conducted within the scope of a senior project in the Department of Computer Engineering at Bilecik Şeyh Edebali University. The authors would like to express their sincere gratitude to the Department of Computer Engineering for its academic support and infrastructure throughout this work. The source code for the simulation is available at: https://github.com/retnap/Dijkstra-and-A-Search-Algorithm-with-CoppeliaSim. Additional demonstration videos of the CoppeliaSim environment and UAV path tracking can be accessed via the YouTube channel: https://www.youtube.com/@selmankayal2006.

References

1. Singh, R., & Kumar, S. (2025). A comprehensive insights into drones: History, classification, architecture, navigation, applications, challenges, and future trends. arXiv preprint, arXiv:2501.10066.
2. Tsouros, D. C., Bibi, S., & Sarigiannidis, P. G. (2019). A review on UAV-based applications for precision agriculture. Information, 10(11), 349.
3. Velusamy, P., Rajendran, S., Mahendran, R. K., Naseer, S., Shafiq, M., & Choi, J. G. (2021). Unmanned aerial vehicles (UAV) in precision agriculture: Applications and challenges. Energies, 15(1), 217.
4. Lekidis, A., Anastasiadis, A. G., & Vokas, G. A. (2022). Electricity infrastructure inspection using AI and edge platform-based UAVs. Energy Reports, 8, 1394–1411.
5. Khan, A., Gupta, S., & Gupta, S. K. (2022). Emerging UAV technology for disaster detection, mitigation, response, and preparedness. Journal of Field Robotics, 39(6), 905–955.
6. Aljohani, M., Mukkamala, R., & Olariu, S. (2025). Delivery of medical supplies to remote locations via unmanned aerial vehicles: Approaches, challenges, and solutions. Transportation Research Procedia, 84, 73–80.
7. Huang, H., Savkin, A. V., & Huang, C. (2021). Decentralized autonomous navigation of a UAV network for road traffic monitoring. IEEE Transactions on Aerospace and Electronic Systems, 57(4), 2558–2564.
8. Green, D. R., Hagon, J. J., Gómez, C., & Gregory, B. J. (2019). Using low-cost UAVs for environmental monitoring, mapping, and modelling: Examples from the coastal zone. In Coastal management (pp. 465–501). Academic Press.

9. Jung, W., Park, C., Lee, S., & Kim, H. (2024). Enhancing UAV swarm tactics with edge AI: Adaptive decision making in changing environments. Drones, 8(10), 582.
10. Mohsan, S. A. H., Othman, N. Q. H., Li, Y., Alsharif, M. H., & Khan, M. A. (2023). Unmanned aerial vehicles (UAVs): Practical aspects, applications, open challenges, security issues, and future trends. Intelligent Service Robotics, 16(1), 109–137.
11. Lu, J., Jin, Q., Yuan, J., Ma, J., Qi, J., & Shao, Y. (2025). Path planning methods for four-way shuttles in dynamic environments based on A* and CBS algorithms. Mathematics, 13(10), 1588. Khan, M. A. (2020). A comprehensive study of Dijkstra's algorithm. SSRN.
12. Wang, R., Lu, Z., Jin, Y., & Liang, C. (2022). Application of A* algorithm in intelligent vehicle path planning. Mathematical Models in Engineering, 8(3), 82–90.
13. Tursynbek, I., & Shintemirov, A. (2020). Modeling and simulation of spherical parallel manipulators in CoppeliaSim (V-REP) robot simulator software. In 2020 International Conference on Nonlinearity, Information and Robotics (NIR) (pp. 1–6). IEEE.
14. Meng, W., Zhang, X., Zhou, L., Guo, H., & Hu, X. (2025). Advances in UAV path planning: A comprehensive review of methods, challenges, and future directions. Drones, 9(5), 376.
15. Maneev, V. V., & Syryamkin, M. V. (2019). Optimizing the A* search algorithm for mobile robotic devices. In IOP Conference Series: Materials Science and Engineering, 516(1), 012054. IOP Publishing.
16. Schichler, L., Festl, K., Solmaz, S., & Watzenig, D. (2024). A cost-effective approach to smooth A* path planning for autonomous vehicles. In 2024 IEEE International Automated Vehicle Validation Conference (IAVVC) (pp. 1–6). IEEE.
17. Open Robotics. (n.d.). Gazebo Sim. https://gazebosim.org/home , (August 2025).
18. Cyberbotics. (n.d.). Webots: Robot simulator. https://cyberbotics.com/ , (August 2025).
19. Hellenic Robotics Center of Excellence. (n.d.). HERON: The Hellenic Robotics Center of Excellence. https://heron-robotics-coe.eu/ , (August 2025).
20. Coppelia Robotics. (n.d.). CoppeliaSim: Robot simulation software. https://www.coppeliarobotics.com/, (August 2025).
21. Collins, J., Chand, S., Vanderkop, A., & Howard, D. (2021). A review of physics simulators for robotic applications. IEEE Access, 9, 51416–51431.
22. Yu, C. H., Tsai, J., & Chang, Y. T. (2024). Intelligent path planning for UAV patrolling in dynamic environments based on the transformer architecture. Electronics, 13(23), 4716.
23. Theile, M., Bayerlein, H., Caccamo, M., & Sangiovanni-Vincentelli, A. L. (2023). Learning to recharge: UAV coverage path planning through deep reinforcement learning. arXiv preprint arXiv:2309.03157.
24. Sun, T., Sun, W., Sun, C., & He, R. (2024). Path planning of UAV formations based on semantic maps. Remote Sensing, 16(16), 3096.
25. Alyammahi, A., Xu, Z., Petrunin, I., Peng, B., & Grech, R. (2025). Reinforcement Learning for UAV Path Planning Under Complicated Constraints with GNSS Quality Awareness. Engineering Proceedings, 88(1), 66.
26. Meng, W., Zhang, X., Zhou, L., Guo, H., & Hu, X. (2025). Advances in UAV path planning: A comprehensive review of methods, challenges, and future directions. Drones (2504-446X), 9(5), 376.
27. Humphery, O. U. (n.d.). Path planning simulation robot [Source code]. GitHub. Retrieved October 2024, from https://github.com/Humphery7/path_planning_simulation_robot.
28. Harting, H. (n.d.). A-star-search [Source code]. GitHub. Retrieved October 2024, from https://github.com/hharting14/a-star-search.
29. Shitsukane, A., Otieno, C., Obuhuma, J., Mukhongo, L., & Kariuki, S. (2025). A co-simulation framework using MATLAB and CoppeliaSim for path planning of nonholonomic mobile robots. Global Journal of Engineering and Technology Advances, 23(1), 6–19.
30. Kwee, L. H., & Sobran, N. M. M. (2023). Comparison of RRT and TRRT object arrangement path planning robot for retail warehouse using CoppeliaSim. International Journal of Electrical Engineering and Applied Sciences (IJEEAS), 6(2), 1–7.