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Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part I

Research Article

Optimized Route Planning and Precise Circle Detection in Unmanned Aerial Vehicle with Machine Learning

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-48888-7_8,
        author={Ankit Garg and Priya Mishra and Naveen Mishra},
        title={Optimized Route Planning and Precise Circle Detection in Unmanned Aerial Vehicle with Machine Learning},
        proceedings={Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part I},
        proceedings_a={IC4S},
        year={2024},
        month={1},
        keywords={Autonomous unmanned aerial vehicles distributed architecture motion planning control machine learning web applications experimentation},
        doi={10.1007/978-3-031-48888-7_8}
    }
    
  • Ankit Garg
    Priya Mishra
    Naveen Mishra
    Year: 2024
    Optimized Route Planning and Precise Circle Detection in Unmanned Aerial Vehicle with Machine Learning
    IC4S
    Springer
    DOI: 10.1007/978-3-031-48888-7_8
Ankit Garg1, Priya Mishra1, Naveen Mishra1,*
  • 1: Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology
*Contact email: naveenmishra.ece@gmail.com

Abstract

This research paper presents a distributed architecture for optimized route planning and precise circle detection in unmanned aerial vehicle with machine learning. The architecture focuses on three key areas: motion planning, control, and the integration of a web application with machine learning (ML) for autonomous drones. By leveraging advanced planning and control algorithms, the architecture enables UAVs to navigate dynamic environments, execute complex maneuvers, and maintain stability. The ML-integrated web application enhances decision-making for detection, optimizing route planning. Extensive simulations and real-world experiments validate the effectiveness and scalability of the proposed architecture, making it a valuable tool for advancing research in autonomous UAV systems.

Keywords
Autonomous unmanned aerial vehicles distributed architecture motion planning control machine learning web applications experimentation
Published
2024-01-05
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-48888-7_8
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