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Applied Sciences
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Artificial Intelligence in Drone and UAV
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Artificial Intelligence in Drone and UAV

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 February 2026 | Viewed by 2754

Special Issue Editor

Dr. Romain Neuville

E-Mail Website
Guest Editor
Department of Civil, Environmental, and Geomatic Engineering, University College London, Gower Street, London WC1 6BT, UK
Interests: drone

Special Issue Information

Dear Colleagues,

The continuous harmonization of regulations, combined with increasingly cost-effective and compact drones and embedded sensors, is driving the greater adoption of drone-based technologies across various industries. Applications such as surveillance, search and rescue, inspection and predictive maintenance, inventory identification and auditing, as well as design and simulation can now be conducted remotely and at large scales. This removes the need for human deployment in hazardous or hard-to-reach environments, while enabling the more frequent and continuous analysis of the surrounding environment.To fully harness the potential of drones, AI-driven support and automation are essential at all stages of operation—before, during, and after the completion of the flight. Therefore, this Special Issue seeks creative research contributions that explore recent advancements in AI for enhancing the efficiency, effectiveness, and safety of drone applications. Relevant topics include, but are not limited to, flight path optimization, obstacle avoidance systems, simultaneous localization and mapping (SLAM), drone swarm coordination, point cloud registration, and real-time or post-processed semantic information extraction. Submissions are welcome from a wide range of domains and applications.

Dr. Romain Neuville
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • drones
  • AI driven
  • flight

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Published Papers (2 papers)

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Research

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26 pages, 5540 KB  
Article
Enhanced Path Planning by Repositioning the Starting Point
by Gregory Gasteratos and Ioannis Karydis
Appl. Sci. 2025, 15(16), 8786; https://doi.org/10.3390/app15168786 - 8 Aug 2025
Viewed by 336
Abstract
Drone power management poses ongoing challenges that significantly impact operational effectiveness across various applications. This research examines path planning optimization, particularly focusing on distance minimization to enhance efficiency and performance. When drones must visit static ground stations, analyzing the constituent elements of flight [...] Read more.
Drone power management poses ongoing challenges that significantly impact operational effectiveness across various applications. This research examines path planning optimization, particularly focusing on distance minimization to enhance efficiency and performance. When drones must visit static ground stations, analyzing the constituent elements of flight paths reveals that segments connecting the launch pad to initial and final stations emerge as a distinct area for further path optimization. Given scenarios where launch pad relocation remains feasible, this study proposes several alternative methodologies for adjusting launch positions to minimize total flight distances across multiple drone operations. The investigation employed extensive experimentation involving diverse configurations with varying station counts and available drone units. Results demonstrate that repositioning the launch pad to serve as an optimal center point for all drone routes yields substantial improvements in total distance minimization, ranging from 4% to 22% across different operational scenarios. The geometric median approach consistently outperformed alternative positioning strategies, achieving these improvements while maintaining computational efficiency. These findings contribute to sustainable drone operations by reducing energy consumption through optimized flight planning. The methodology proves particularly valuable for applications requiring flexible launch point positioning, offering practical solutions for enhancing operational efficiency in environmental monitoring, precision agriculture, and infrastructure inspection tasks where energy conservation directly impacts mission success and operational viability. Full article
(This article belongs to the Special Issue Artificial Intelligence in Drone and UAV)
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Review

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36 pages, 1547 KB  
Review
UAV–Ground Vehicle Collaborative Delivery in Emergency Response: A Review of Key Technologies and Future Trends
by Yizhe Wang, Jie Li, Xiaoguang Yang and Qing Peng
Appl. Sci. 2025, 15(17), 9803; https://doi.org/10.3390/app15179803 - 6 Sep 2025
Viewed by 1778
Abstract
UAV delivery and ground transfer scheduling in emergency scenarios represent critical technological systems for enhancing disaster response capabilities and safeguarding lives and property. This study systematically reviews recent advances across eight core research domains: UAV emergency delivery systems, ground–air integrated transportation coordination, emergency [...] Read more.
UAV delivery and ground transfer scheduling in emergency scenarios represent critical technological systems for enhancing disaster response capabilities and safeguarding lives and property. This study systematically reviews recent advances across eight core research domains: UAV emergency delivery systems, ground–air integrated transportation coordination, emergency logistics optimization, UAV path planning and scheduling algorithms, collaborative optimization between ground vehicles and UAVs, emergency response decision support systems, low-altitude economy and urban air traffic management, and intelligent transportation system integration. Research findings indicate that UAV delivery technologies in emergency contexts have evolved from single-aircraft applications to intelligent multi-modal collaborative systems, demonstrating significant advantages in medical supply distribution, disaster relief, and search-and-rescue operations. Current technological development exhibits four major trends: hybrid optimization algorithms, multi-UAV cooperation, artificial intelligence enhancement, and real-time adaptation capabilities. However, critical challenges persist, including regulatory framework integration, adverse weather adaptability, cybersecurity protection, human–machine interface design, cost–benefit assessment, and standardization deficiencies. Future research should prioritize distributed decision architectures, robustness optimization, cross-domain collaboration mechanisms, emerging technology integration, and practical application validation. This comprehensive review provides systematic theoretical foundations and practical guidance for emergency management agencies in formulating technology development strategies, enterprises in investment planning, and research institutions in determining research priorities. Full article
(This article belongs to the Special Issue Artificial Intelligence in Drone and UAV)
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