Real-Time 3D Routing Optimization for Unmanned Aerial Vehicle using Machine Learning




UAV, Unmanned Aerial Vehicle, Artificial Intelligence, AI, Sensor arrays, Heuristic A* algorithm, Simultaneous Localization and Mapping, SLAM, Software-In-The-Loop simulations, SITL


In the realm of Unmanned Aerial Vehicles (UAVs) for civilian applications, the surge in demand has underscored the need for sophisticated technologies. The integration of Unmanned Aerial Systems (UAS) with Artificial Intelligence (AI) has become paramount to address challenges in urban environments, particularly those involving obstacle collision risks. These UAVs are equipped with advanced sensor arrays, incorporating LiDAR and computer vision technologies. The AI algorithm undergoes comprehensive training on an embedded machine, fostering the development of a robust spatial perception model. This model enables the UAV to interpret and navigate through the intricate urban landscape with a human-like understanding of its surroundings. During mission execution, the AI-driven perception system detects and localizes objects, ensuring real-time awareness. This study proposes an innovative real-time three-dimensional (3D) path planner designed to optimize UAV trajectories through obstacle-laden environments. The path planner leverages a heuristic A* algorithm, a widely recognized search algorithm in artificial intelligence. A distinguishing feature of this proposed path planner is its ability to operate without the need to store frontier nodes in memory, diverging from conventional A* implementations. Instead, it relies on relative object positions obtained from the perception system, employing advanced techniques in simultaneous localization and mapping (SLAM). This approach ensures the generation of collision-free paths, enhancing the UAV's navigational efficiency. Moreover, the proposed path planner undergoes rigorous validation through Software-In-The-Loop (SITL) simulations in constrained environments, leveraging high-fidelity UAV dynamics models. Preliminary real flight tests are conducted to assess the real-world applicability of the system, considering factors such as wind disturbances and dynamic obstacles. The results showcase the path planner's effectiveness in providing swift and accurate guidance, thereby establishing its viability for real-time UAV missions in complex urban scenarios.


Konovalenko, I.; Kuznetsova, E.; Miller, A.; Miller, B.; Popov, A.; Shepelev, D.; Stepanyan, K. New approaches to the integration of navigation systems for autonomous unmanned vehicles (UAV). Sensors 2018, 18, 3010.

Wang, J.; Garrat, M.; Lambert, A.; Wang, J.J.; Han, S.; Sinclair, D. Integration of GPS/INS/vision sensors to navigate unmanned aerial vehicles. ISPRS Int. Arch. Photogramm. Remote Sens. Spatial Inform. Sci. 2008, 37, 963–970.

García, J.; Molina, J.M.; Trincado, J.; Sánchez, J. Analysis of sensor data and estimation output with configurable UAV platforms. In Proceedings of the 2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF), Bonn, Germany, 10–12 October 2017;pp. 1–6.

Abdulla, A.-K.; David, M.; Fernando, G.; Arturodela, E.; José, M.A. Survey of computer vision algorithms and applications for unmanned aerial vehicles. Expert Syst. Appl. 2018, 92, 447–463.

Wagoner, A.R.; Schrader, D.K.; Matson, E.T. Survey on Detection and Tracking of UAVs Using Computer Vision. In Proceedings of the First IEEE International Conference on Robotic Computing (IRC), Taichung, Taiwan, 10–12 April 2017; pp. 320–325.

Belmonte, L.M.; Morales, R.; Fernández-Caballero A. Computer Vision in Autonomous Unmanned Aerial Vehicles- A Systematic Mapping Study. Appl. Sci. 2019, 9, 3196.

Choi, S.Y.; Dowan, C. Unmanned aerial vehicles using machine learning for autonomous flight; state-of-the-art. Adv. Robot. 2019, 33, 265–277.

Kyrkou, C.; Theocharides, T. Deep-Learning-Based Aerial Image Classification for Emergency Response Applications using Unmanned Aerial Vehicles. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, 16–17 June 2019.

Gonzalez, L.F.; Montes, G.A.; Puig, E.; Johnson, S.; Mengersen, K.; Gaston, K.J. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation. Sensors 2016, 16, 97.

Valenti, F.; Giaquinto, D.; Musto, L.; Zinelli, A.; Bertozzi, M.; Broggi, A. Enabling computer vision-based autonomous navigation for unmanned aerial vehicles in cluttered gps-denied environments. In Proceedings of the IEEE International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 4–7 November 2018.

Lu, Y.; Xue, Z.; Xia, G.-S.; Zhang, L. A Survey on vision-based UAV navigation. Geo-Spat. Inf. Sci. 2018, 21, 21–32.

Yan, C.; Xiang, X.; Wang, C. Towards Real-Time Path Planning through Deep Reinforcement Learning for a UAV in Dynamic Environments. J. Intell. Robot. Syst. 2019.

Ankit Garg, Priya Mishra, and Naveen Mishra, "Optimized Route Planning and Precise Circle Detection in Unmanned Aerial Vehicle with Machine Learning ", International Conference on Cognitive Computing and Cyber-Physical Systems, Springer Nature Switzerland, pp. 95-105, 1/12/2023.

Sandino, J.; Vanegas, F.; Maire, F.; Caccetta, P.; Sanderson, C.; Gonzalez, F. UAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environment. Remote Sens. 2020, 12, 3386.

Dai, X.; Mao, Y.; Huang, T.; Qin, N.; Huang, D.; Li, Y. Automatic obstacle avoidance of quadcopter UAV via CNN-based learning. Neurocomputing 2020, 402, 346–358.

Yang, L.; Qi, J.; Song, D.; Xiao, J.; Han, J.; Xia, Y. Survey of Robot 3D Path Planning Algorithms. J. Control Sci. Eng. 2016, 1–22.

Loquercio, A.; Maqueda, A.I.; Del-Blanco, C.R.; Scaramuzza, D. DroNet: Learning to Fly by Driving. IEEE Robot. Autom. Lett. 2018, 3, 1088–1095.

Nathan, K.; Andre, H. Design and Use Pradigms for Gazebo, An Open-Source Multi-Robot Simulator. In Proceedings of the 2004 IEEE/RSJ International Conference Intelligent Robot System, Sendai, Japan, 28 September–2 October 2004; pp. 2149–2154.

Chahal, K.; Dey, K. A Survey of Modern Object Detection Literature using Deep Learning. arXiv 2018, arXiv:1808.07256v1.

Girshick, R.; Donahue, J.; Darrell, T.; Malik, J. Region-Based Convolutional Networks for Accurate Object Detection and Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2016, 38, 142–158.

Nayagam, M.G.; Ramar, D.K. A Survey on Real time Object Detection and Tracking algorithms. Int. J. Appl. Eng. Res. 2015, 10, 8290–8297.

Najibi, M.; Rastegari, M.; Davis, L.S. G-CNN: An Iterative Grid Based Object Detector. In Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 2369–2377.

Zhao, Z.-Q.; Zheng, P.; Xu, S.-T.; Wu, X. Object Detection with Deep Learning: A review. IEEE Trans. Neural Netw. Learn. Syst. 2019, 30, 3212–3232.

Liu, Q.; Xiang, X.; Wang, Y.; Luo, Z.; Fang, F. Aircraft detection in remote sensing image based on corner clustering and deep learning. Eng. Appl. Artif. Intell. 2020, 87, 103333.

Tan, J. Complex object detection using deep proposal mechanism. Eng. Appl. Artif. Intell. 2020, 87, 103234.




How to Cite

Mishra P, Boopal B, Mishra N. Real-Time 3D Routing Optimization for Unmanned Aerial Vehicle using Machine Learning. EAI Endorsed Scal Inf Syst [Internet]. 2024 Apr. 9 [cited 2024 May 20];. Available from:



Research articles