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Research Article

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

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  • @ARTICLE{10.4108/eetsis.5693,
        author={Priya Mishra and Balaji Boopal and Naveen Mishra},
        title={Real-Time 3D Routing Optimization for Unmanned Aerial Vehicle using Machine Learning},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={6},
        publisher={EAI},
        journal_a={SIS},
        year={2024},
        month={4},
        keywords={UAV, Unmanned Aerial Vehicle, Artificial Intelligence, AI, Sensor arrays, Heuristic A* algorithm, Simultaneous Localization and Mapping, SLAM, Software-In-The-Loop simulations, SITL},
        doi={10.4108/eetsis.5693}
    }
    
  • Priya Mishra
    Balaji Boopal
    Naveen Mishra
    Year: 2024
    Real-Time 3D Routing Optimization for Unmanned Aerial Vehicle using Machine Learning
    SIS
    EAI
    DOI: 10.4108/eetsis.5693
Priya Mishra1, Balaji Boopal1, Naveen Mishra1,*
  • 1: Vellore Institute of Technology University
*Contact email: naveenmishra.ece@gmail.com

Abstract

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.

Keywords
UAV, Unmanned Aerial Vehicle, Artificial Intelligence, AI, Sensor arrays, Heuristic A* algorithm, Simultaneous Localization and Mapping, SLAM, Software-In-The-Loop simulations, SITL
Received
2023-12-29
Accepted
2024-04-02
Published
2024-04-09
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.5693

Copyright © 2024 P. Mishra et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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