Adaptive Machine Learning Framework for Navigation Control of Autonomous Drones

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]23/09/2025 at 3:18 PM[/if 2224] | [if 1553 equals=””] Volume : 12 [else] Volume : 12[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 1 7

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    Vikramsingh R. Parihar, Soni A. Chaturvedi,

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  1. Research Scholar, Associate Professor, Department of Electronics and Telecommunication Engineering, Priyadarshini College of Engineering, Nagpur, Department of Electronics and Telecommunication Engineering, Priyadarshini College of Engineering, Nagpur, Maharashtra, Maharashtra, India, India

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Abstract

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nThe rise of autonomous drones has expanded UAV applications across sectors like surveillance, delivery, agriculture, and rescue operations. However, traditional navigation systems face limitations in adapting to dynamic environments. This study proposes an AI-driven adaptive navigation framework that leverages real-time sensor data, reinforcement learning, and adaptive control strategies to enhance drone autonomy, scalability, and security. The system processes mission inputs, environmental data (from LiDAR, cameras, GPS, and weather sensors), and applies data preprocessing techniques like Kalman filtering and sensor fusion. A real-time feedback loop monitors obstacles, weather, and battery status. The decision-making module, using reinforcement learning models like DQN and PPO, dynamically adapts navigation strategies, supported by global and local path planning algorithms. Continuous learning through experience replay and anomaly detection ensures performance improvement over time. This work tackles key challenges related to scalability, adaptability, and system integrity, contributing significantly to the advancement of autonomous drone navigation. By addressing these aspects, the study enhances the reliability and intelligence of drones operating in dynamic and complex environments. The proposed solutions lay a strong foundation for future developments, ensuring that autonomous systems can efficiently adapt to varying conditions while maintaining high performance and safety standards across diverse applications and mission scenarios.nn

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Keywords: Adaptive navigation, autonomous drones, path planning, real-time decision making, sensor fusion

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Microwave Engineering and Technologies ]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Microwave Engineering and Technologies (jomet)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article:
nVikramsingh R. Parihar, Soni A. Chaturvedi. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Adaptive Machine Learning Framework for Navigation Control of Autonomous Drones[/if 2584]. Journal of Microwave Engineering and Technologies. 10/09/2025; 12(03):1-7.

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How to cite this URL:
nVikramsingh R. Parihar, Soni A. Chaturvedi. [if 2584 equals=”][226 striphtml=1][else]Adaptive Machine Learning Framework for Navigation Control of Autonomous Drones[/if 2584]. Journal of Microwave Engineering and Technologies. 10/09/2025; 12(03):1-7. Available from: https://journals.stmjournals.com/jomet/article=10/09/2025/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

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Volume 12
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 21/05/2025
Accepted 12/06/2025
Published 10/09/2025
Retracted
Publication Time 112 Days

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