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Vision-Based Learning for Drones: A Survey.

Jiaping Xiao, Rangya Zhang, Yuhang Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |May 6, 2025
    PubMed
    Summary
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    Vision-based learning is revolutionizing drone autonomy and functionality by enhancing perception and decision-making. This survey provides a comprehensive overview of methods, applications, and future directions for intelligent drones.

    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Drones are advanced cyber-physical systems (CPSs).
    • Vision-based learning is increasingly vital for drone autonomy and functionality.
    • Existing surveys are task-specific, lacking a comprehensive overview.

    Purpose of the Study:

    • To provide a comprehensive overview of vision-based learning for drones.
    • To emphasize the role of vision-based learning in enhancing drone operational capabilities.
    • To discuss challenges and future research directions.

    Main Methods:

    • Elucidation of fundamental principles of vision-based learning for drones.
    • Categorization of vision-based control methods (indirect, semidirect, end-to-end).

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  • Exploration of applications in single-agent and multi-agent systems.

    • Main Results:

    • Vision-based learning significantly improves drone visual perception and decision-making.
    • Diverse applications span from individual drones to complex multi-drone systems.
    • Identified challenges and innovations in various application domains.

    Conclusions:

    • Vision-based learning is crucial for advancing drone autonomy and intelligence.
    • Future research should address open questions for developing more capable drones.
    • This field is a key pathway toward artificial general intelligence (AGI) in physical environments.