Machine Learning for Autonomous Navigation and Collision Avoidance in UAVs

Machine learning techniques are revolutionizing the field of autonomous navigation and collision avoidance in Unmanned Aerial Vehicles (UAVs). The advancement of UAVs toward autonomous navigation methods is aided by the sensors they carry, which can gather vast amounts of data, including images. Thi...

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Bibliographic Details
Published inProceedings (International Confernce on Computational Intelligence and Communication Networks) pp. 381 - 388
Main Authors Mon, Bisni Fahad, Hayajneh, Mohammad, Ali, Najah Abu, Ullah, Farman, Al Warafy, Abdulmalik, Saeed, Nasir
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.12.2024
Subjects
Adaptation models
Autonomous aerial vehicles
Autonomous robots
Collision avoidance
communication
machine learning
Navigation
Path planning
Real-time systems
Sensors
Testing
UAV
Vehicle dynamics
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ISSN2472-7555
DOI10.1109/CICN63059.2024.10847476

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Summary:Machine learning techniques are revolutionizing the field of autonomous navigation and collision avoidance in Unmanned Aerial Vehicles (UAVs). The advancement of UAVs toward autonomous navigation methods is aided by the sensors they carry, which can gather vast amounts of data, including images. This data can be used to train using vision-based deep learning autonomous navigation techniques. This study explores machine learning techniques to tackle complex navigation tasks such as path planning, localization, mapping, and obstacle detection. It also highlights the challenges of implementing machine learning in real-time environments, focusing on data management, computational efficiency, and the adaptability of models to dynamic conditions. By addressing these factors, the paper offers a detailed overview of how machine learning can improve UAV performance and suggests future research directions in this rapidly evolving field.
ISSN:2472-7555
DOI:10.1109/CICN63059.2024.10847476