Machine Learning Applications in Unmanned Aerial Vehicles and Drones
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 November 2025 | Viewed by 35
Special Issue Editors
Interests: estimation theory; drone navigation; target tracking; signal and image processing; machine; learning; remote sensing applications involving space-based infrared (IR) and electro-optical (EO) sensors
Interests: nonlinear estimation; resilient PNT (pos, nav, timing); secure multi-function waveforms; embedded signal processing; guidance, navigation and control of autonomous and networked UAVs
Special Issue Information
Dear Colleagues,
This Special Issue focuses on the integration of machine learning techniques in unmanned aerial vehicles (UAVs) and drone systems, emphasizing innovative research, practical applications, and emerging trends. As UAVs and drones continue to play a crucial role in sectors such as surveillance, agriculture, delivery, infrastructure inspection, and environmental monitoring, machine learning enables autonomy, real-time decision-making, and intelligent behavior in dynamic environments.
We invite high-quality contributions that explore machine learning applications in object and obstacle detection, flight control, route optimization, and swarm coordination. Special attention will be given to the use of onboard sensors, such as cameras, light detection and ranging (LiDAR), radio detection and ranging (radar), global positioning system (GPS), and inertial measurement units (IMUs), to enhance navigation, environment mapping, and situational awareness. Studies addressing challenges related to limited onboard computational resources, real-time processing, and edge artificial intelligence (AI) deployment are also encouraged.
The Special Issue aims to showcase both theoretical advancements and practical implementations of machine learning in UAV systems, particularly those with real-world validation. By bringing together cutting-edge research, this issue seeks to advance intelligent drone technologies and promote the adoption of machine learning for more efficient and autonomous UAV operations across a range of industries.
Dr. Djedjiga Gigi Belfadel
Dr. David A. Haessig
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- unmanned aerial vehicles (UAVs)
- obstacle detection
- autonomous systems
- drone navigation
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