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Challenges and Future Trends in Unmanned Aerial Vehicles: Control, Sensor Integration, Networks, Systems and Applications

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

Deadline for manuscript submissions: 30 August 2026 | Viewed by 3451

Special Issue Editors


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Guest Editor
1. Instituto de Investigación en Informática de Albacete, 02071 Albacete, Spain
2. Escuela Técnica Superior de Ingeniería Industrial de Albacete, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
Interests: unmanned aerial vehicles; drones; service robotics; control; artificial intelligence; virtual reality

Special Issue Information

Dear Colleagues,

UAVs are Unmanned Aerial Vehicles or drones, commonly considered to be an aircraft with no pilot on board. In recent years, UAVs have emerged as a ubiquitous and integral part of our society, appearing in great diversity in a multiplicity of applications for economic, commercial, leisure, military, and academic purposes. This is caused by rapid advances in control, artificial intelligence, sensor integration, and computerization, together with the capacity to connect to other vehicles, which leads to safer, lighter, and more robust, accessible, and cost-effective UAVs. The identification of potential applications and research directions for control, sensor integration, networks, systems, and applications, as well as the exploration of the obstacles and challenges of UAVs, will prove highly valuable to the industrial community, research institutes, and universities, with the aim to improve the knowledge needed by this sector. However, there is still a lot of room for improvement in solving problems associated with UAVs and how to remediate their vulnerabilities.

This Special Issue aims to showcase the latest research achievements, findings, and ideas behind smart UAVs based on enlightening inventions or current challenging applications. Researchers are invited to contribute original research and review articles that summarize the latest developments and ideas surrounding these technologies.

Potential topics include, but are not limited to, the following:

  • UAV applications and UAV-based systems;
  • Network protocols and communication systems for UAV-based solutions;
  • Algorithms and artificial intelligence solutions for UAV-based deployments;
  • Sensing solutions based on UAVs;
  • Machine/deep learning-based sensor fusion and environment detection;
  • UAV swarms and interaction between UAVs and IoT solutions;
  • Artificial intelligence-based fault diagnosis and failure control;
  • Disturbance observer and robust control for UAVs.

Dr. Lidia Belmonte Moreno
Prof. Dr. Rafael Morales
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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

  • unmanned aerial vehicles
  • drones
  • control
  • sensor integration
  • networks
  • systems and applications

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Published Papers (3 papers)

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Research

18 pages, 1283 KB  
Article
Human Perceptions of Reliability of Autonomous Drone Systems Under Dynamic Disturbances
by Barnabás Kiss, Miklós Kuczmann and Áron Ballagi
Appl. Sci. 2026, 16(9), 4353; https://doi.org/10.3390/app16094353 - 29 Apr 2026
Viewed by 88
Abstract
This study analyzes how dynamic disturbances influence the decisions made during the human supervision of autonomous unmanned aerial vehicles. While previous research has primarily focused on control algorithms and system stability, the effect of disturbances originating from system dynamics on operator intervention behavior [...] Read more.
This study analyzes how dynamic disturbances influence the decisions made during the human supervision of autonomous unmanned aerial vehicles. While previous research has primarily focused on control algorithms and system stability, the effect of disturbances originating from system dynamics on operator intervention behavior has been less extensively investigated. To examine this problem, a hardware-in-the-loop (HIL) experimental framework was developed, which is based on a previously validated unmanned aerial vehicles (UAVs) test platform and was adapted in this study to enable the investigation of human supervisory decision-making. Participants observed the behavior of an autonomously operating system under controlled disturbances and were provided with the possibility to intervene by activating an emergency landing mechanism. The results indicate that the disturbance intensity had a significant effect on intervention decisions, while the reaction times did not show notable differences. This finding suggests that supervisory behavior is primarily determined by the evaluation of the system state rather than by timing characteristics. It also identifies that subjective risk perception plays a decisive role in the formation of intervention decisions, indicating the presence of an implicit decision threshold for participant behavior. The research findings offer a novel approach to the interpretation of human–UAV interaction by emphasizing the role of system dynamics in shaping user decisions. The presented method may provide a foundation for the development of predictive and adaptive supervisory systems that take into account the characteristics of human decision-making, thereby contributing to the design of safer and more efficient autonomous systems. Full article
24 pages, 1625 KB  
Article
Multi-UAV Navigation for Surveillance of Moving Ground Vehicles on Uneven Terrains via Beam-Search MPC
by Yuanzhen Liu and Andrey V. Savkin
Appl. Sci. 2026, 16(9), 4128; https://doi.org/10.3390/app16094128 - 23 Apr 2026
Viewed by 266
Abstract
This paper investigates the trajectory planning problem for multiple unmanned aerial vehicles (UAVs) tasked with monitoring ground targets in complex, uneven terrains. The key challenge lies in maintaining continuous Line-of-Sight (LoS) while satisfying non-holonomic motion constraints and handling terrain-induced occlusions. To address this [...] Read more.
This paper investigates the trajectory planning problem for multiple unmanned aerial vehicles (UAVs) tasked with monitoring ground targets in complex, uneven terrains. The key challenge lies in maintaining continuous Line-of-Sight (LoS) while satisfying non-holonomic motion constraints and handling terrain-induced occlusions. To address this problem, we propose a Beam-search Model Predictive Control (BMPC) framework. The method integrates a first-order kinematic predictor for target motion estimation and a proactive safety altitude margin to guide UAVs toward favorable viewpoints before occlusions occur. The proposed approach is validated through extensive simulations based on high-resolution Digital Elevation Models (DEMs). Monte Carlo results demonstrate a significant reduction in LoS occlusion, decreasing the average occlusion rate from 38.75±26.12% to near zero in the noise-free case, compared with conventional reactive MPC methods. Under perception noise with a standard deviation of 1.5 m, the LoS retention rate remains above 99%, indicating strong robustness to sensing uncertainty. In addition, the algorithm maintains stable computational performance, with an average execution time of approximately 1.68 s per step in a non-optimized simulation environment. The proposed framework provides an effective solution for autonomous aerial surveillance in environments with substantial elevation variations, such as mountainous regions and urban canyons, by achieving a balance between tracking continuity and computational tractability. Full article
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17 pages, 1563 KB  
Article
Feasibility of Drone-Mounted Camera for Real-Time MA-rPPG in Smart Mirror Systems
by Mohammad Afif Kasno, Yong-Sik Choi and Jin-Woo Jung
Appl. Sci. 2026, 16(5), 2307; https://doi.org/10.3390/app16052307 - 27 Feb 2026
Viewed by 443
Abstract
Remote photoplethysmography (rPPG) enables contactless estimation of cardiovascular signals from video, but most existing studies assume a fixed, stationary camera. This study investigates the feasibility of performing real-time moving-average rPPG (MA-rPPG) using a drone-mounted camera, where platform motion, vibration, and viewing distance introduce [...] Read more.
Remote photoplethysmography (rPPG) enables contactless estimation of cardiovascular signals from video, but most existing studies assume a fixed, stationary camera. This study investigates the feasibility of performing real-time moving-average rPPG (MA-rPPG) using a drone-mounted camera, where platform motion, vibration, and viewing distance introduce additional challenges. Building on our previously validated real-time MA-rPPG smart mirror platform, we reuse the smart mirror interface as a unified frontend for visualization, synchronization, and logging while adapting the MA-rPPG pipeline to operate on live video streamed from an off-the-shelf DJI Tello micro-drone. Feasibility experiments were conducted with 10 participants under controlled indoor lighting and constrained flight conditions, where the drone maintained a stable hover in front of a standing subject and facial video was processed in real time to estimate heart rate from a forehead region of interest. To avoid cross-modality bias and clarify the effect of the aerial imaging platform, drone-derived MA-rPPG outputs were compared against a fixed desktop-camera MA-rPPG reference using the same trained model, enabling a controlled, like-for-like evaluation. The results indicate that continuous heart-rate estimation from a drone camera is feasible in our controlled hover-only setup, while agreement tended to vary with hover stability and effective facial resolution. This work is presented strictly as a feasibility-stage investigation and does not claim clinical validity. The findings provide an experimental baseline and operating-envelope insight for future motion-robust rPPG on mobile and aerial health-sensing platforms. Full article
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