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Electronics
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UAV (Unmanned Aerial Vehicles) Networks: Recent Developments and Emerging Trends
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UAV (Unmanned Aerial Vehicles) Networks: Recent Developments and Emerging Trends

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (15 November 2024) | Viewed by 6603

Special Issue Editors

Prof. Dr. Yannis Marinakis

E-Mail Website
Guest Editor
School of Production Engineering and Management, Technical University of Crete, 73100 Chania, Greece
Interests: unmanned aerial vehicles; electric vehicles; electric vehicle routing problem; vehicle routing problem with drones; swarm intelligence algorithms; optimization
Dr. Magdalene Marinaki

E-Mail Website
Guest Editor
School of Production Engineering and Management, Technical University of Crete, 73100 Chania, Greece
Interests: unmanned aerial vehicles; vehicle routing problem; electric vehicle routing problem; vehicle routing problem with drones; swarm intelligence; metaheuristic algorithms; control; optimization
Dr. Nikolaos A. Kyriakakis

E-Mail Website
Guest Editor
School of Production Engineering and Management, Technical University of Crete, 73100 Chania, Greece
Interests: unmanned aerial vehicles; electric vehicle routing problem; humanitarian logistics; metaheuristic algorithms; optimization
Dr. Themistoklis Stamadianos

E-Mail Website
Guest Editor
School of Production Engineering and Management, Technical University of Crete, 73100 Chania, Greece
Interests: electric vehicles and routing problems; optimization; metaheuristic algorithms; unmanned aerial vehicles

Special Issue Information

Dear Colleagues,

The rapid uptake of unmanned aerial vehicles (UAVs) across various industries is revolutionizing traditional processes by offering cost-effective solutions, enhanced data collection capabilities, increased operational efficiency, and remote operation. Subsequently, UAVs find applications in diverse fields such as agriculture, surveillance, environmental monitoring, infrastructure inspection, disaster response, and more.

The emphasis of this Special Issue lies in state-of-the-art UAV applications, specifically highlighting scenarios where UAVs function collectively as a swarm. Such UAV swarms can collaborate and synchronize efforts to accomplish intricate tasks that surpass the capabilities of individual UAVs.

The spotlight is directed towards the energy efficiency and ecological footprint of UAVs, given their pivotal role in shaping novel UAV applications. Leveraging a UAV swarm can enhance both aspects, thereby augmenting their adaptability and sustainability.

Operational reliability and safety constitute an additional focal point. With the expanding sphere of UAV utilization, guaranteeing their secure and consistent operation becomes paramount, particularly in swarm scenarios. Consequently, this Special Issue invites research contributions that tackle issues of operational reliability and safety, alongside research that present novel applications for UAV swarms.

This Special Issue's scope encompasses a diverse array of cutting-edge UAV applications, spanning across humanitarian, commercial, geomatics and monitoring domains. Additionally, research contributions from various disciplines like policy, planning, routing, sensing, and communication are eagerly encouraged, fostering a holistic comprehension of the spectrum of challenges and prospects within UAV applications.

The purpose of this Special Issue is threefold. Firstly, it aims to highlight the state-of-the-art in UAV applications and discuss established approaches.  Secondly, it strives to enrich the UAV literature with captivating, potentially revolutionary, novel applications and principles. Lastly, it endeavours to incorporate research from diverse fields, approaching the subject from a multitude of angles, thus serving as an invaluable repository of insights for researchers, practitioners, and policymakers intrigued by both present and prospective trajectories in UAV applications.

Prof. Dr. Yannis Marinakis
Dr. Magdalene Marinaki
Dr. Nikolaos A. Kyriakakis
Dr. Themistoklis Stamadianos
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 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. Electronics 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
  • optimization algorithms
  • humanitarian applications
  • supply chain management
  • swarm intelligence

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Published Papers (4 papers)

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Research

23 pages, 2029 KiB  
Article
Task Offloading and Resource Allocation for Augmented Reality Applications in UAV-Based Networks Using a Dual Network Architecture
by Dat Van Anh Duong, Shathee Akter and Seokhoon Yoon
Electronics 2024, 13(18), 3590; https://doi.org/10.3390/electronics13183590 - 10 Sep 2024
Cited by 1 | Viewed by 1047
Abstract
This paper proposes a novel UAV-based edge computing system for augmented reality (AR) applications, addressing the challenges posed by the limited resources in mobile devices. The system uses UAVs equipped with edge computing servers (UECs) specifically to enable efficient task offloading and resource [...] Read more.
This paper proposes a novel UAV-based edge computing system for augmented reality (AR) applications, addressing the challenges posed by the limited resources in mobile devices. The system uses UAVs equipped with edge computing servers (UECs) specifically to enable efficient task offloading and resource allocation for AR tasks with dependent relationships. This work specifically focuses on the problem of dependent tasks in AR applications within UAV-based networks. This problem has not been thoroughly addressed in previous research. A dual network architecture-based task offloading (DNA-TO) algorithm is proposed, leveraging the DNA framework to enhance decision-making in reinforcement learning while mitigating noise. In addition, a Karush–Kuhn–Tucker-based resource allocation (KKT-RA) algorithm is proposed to optimize resource allocation. Various simulations using real-world movement data are conducted. The results indicate that our proposed algorithm outperforms existing approaches in terms of latency and energy efficiency. Full article
(This article belongs to the Special Issue UAV (Unmanned Aerial Vehicles) Networks: Recent Developments and Emerging Trends)
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18 pages, 2476 KiB  
Article
A Deep Reinforcement Learning Algorithm for Trajectory Planning of Swarm UAV Fulfilling Wildfire Reconnaissance
by Kubilay Demir, Vedat Tumen, Selahattin Kosunalp and Teodor Iliev
Electronics 2024, 13(13), 2568; https://doi.org/10.3390/electronics13132568 - 30 Jun 2024
Cited by 4 | Viewed by 2397
Abstract
Wildfires have long been one of the critical environmental disasters that require a careful monitoring system. An intelligent system has the potential to both prevent/extinguish the fire and deliver urgent requirements postfire. In recent years, unmanned aerial vehicles (UAVs), with the ability to [...] Read more.
Wildfires have long been one of the critical environmental disasters that require a careful monitoring system. An intelligent system has the potential to both prevent/extinguish the fire and deliver urgent requirements postfire. In recent years, unmanned aerial vehicles (UAVs), with the ability to detect missions in high-risk areas, have been gaining increasing interest, particularly in forest fire monitoring. Taking a large-scale area involved in a fire into consideration, a single UAV is often insufficient to accomplish the task of covering the whole disaster zone. This poses the challenge of multi-UAVs optimum path planning with a key focus on limitations such as energy constraints and connectivity. To narrow down this issue, this paper proposes a deep reinforcement learning-based trajectory planning approach for multi-UAVs that permits UAVs to extract the required information within the disaster area on time. A target area is partitioned into several identical subareas in terms of size to enable UAVs to perform their patrol duties over the subareas. This subarea-based arrangement converts the issue of trajectory planning into allowing UAVs to frequently visit each subarea. Each subarea is initiated with a risk level by creating a fire risk map optimizing the UAV patrol route more precisely. Through a set of simulations conducted with a real trace of the dataset, the performance outcomes confirmed the superiority of the proposed idea. Full article
(This article belongs to the Special Issue UAV (Unmanned Aerial Vehicles) Networks: Recent Developments and Emerging Trends)
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25 pages, 6099 KiB  
Article
Adaptive Multi-Surface Sliding Mode Control with Radial Basis Function Neural Networks and Reinforcement Learning for Multirotor Slung Load Systems
by Clevon Peris, Michael Norton and Suiyang Khoo
Electronics 2024, 13(12), 2424; https://doi.org/10.3390/electronics13122424 - 20 Jun 2024
Cited by 3 | Viewed by 1432
Abstract
While using multirotor UAVs for transport of suspended payloads, there is a need for stability along the desired path, in addition to avoidance of any excessive payload oscillations, and a good level of precision in maintaining the desired path of the vehicle. However, [...] Read more.
While using multirotor UAVs for transport of suspended payloads, there is a need for stability along the desired path, in addition to avoidance of any excessive payload oscillations, and a good level of precision in maintaining the desired path of the vehicle. However, due to the nonlinear and underactuated nature of the system, in addition to the presence of mismatched uncertainties, the development of a control system for this application poses an interesting research problem. This paper proposes a control architecture for a multirotor slung load system by integrating a Multi-Surface Sliding Mode Control, aided by a Radial Basis Function Neural Network, with a Deep Q-Network Reinforcement Learning agent. The former will be used to ensure asymptotic tracking stability, while the latter will be used to suppress payload oscillations. First, we will present the dynamics of a multirotor slung load system, represented here as a quadrotor with a single pendulum load suspended from it. We will then propose a control method in which a multi-surface sliding mode controller, based on an adaptive RBF Neural Network for trajectory tracking of the quadrotor, works in tandem with a Deep Q-Network Reinforcement Learning agent whose reward function aims to suppress the oscillations of the single pendulum slung load. Simulation results demonstrate the effectiveness and potential of the proposed approach in achieving precise and reliable control of multirotor slung load systems. Full article
(This article belongs to the Special Issue UAV (Unmanned Aerial Vehicles) Networks: Recent Developments and Emerging Trends)
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22 pages, 22021 KiB  
Article
Development of an Uneven Terrain Decision-Aid Landing System for Fixed-Wing Aircraft Based on Computer Vision
by Chin-Sheng Chuang and Chao-Chung Peng
Electronics 2024, 13(10), 1946; https://doi.org/10.3390/electronics13101946 - 15 May 2024
Cited by 3 | Viewed by 1074
Abstract
This paper presents a computer vision-based standalone decision-aid landing system for light fixed-wing aircraft, aiming to enhance safety during emergency landings. Current landing assistance systems in airports, such as Instrument Landing Systems (ILSs) and Precision Approach Path Indicators (PAPIs), often rely on costly [...] Read more.
This paper presents a computer vision-based standalone decision-aid landing system for light fixed-wing aircraft, aiming to enhance safety during emergency landings. Current landing assistance systems in airports, such as Instrument Landing Systems (ILSs) and Precision Approach Path Indicators (PAPIs), often rely on costly and location-specific ground equipment, limiting their utility for low-payload light aircraft. Especially in emergency conditions, the pilot may be forced to land on an arbitrary runway where the road flatness and glide angle cannot be ensured. To address these issues, a stereo vision-based auxiliary landing system is proposed, which is capable of estimating an appropriate glide slope based on the terrain, to assist pilots in safe landing decision-making. Moreover, in real-world scenarios, challenges with visual-based methods arise when attempting emergency landings on complex terrains with diverse objects, such as roads and buildings. This study solves this problem by employing the Gaussian Mixture Model (GMM) to segment the color image and extract ground points, while the iterative weighted plane fitting (IWPF) algorithm is introduced to mitigate the interference of outlier feature points, reaching a highly robust plane normal estimation. With the aid of the proposed system, the pilot is able to evaluate the landing glide angle/speed with respect to the uneven terrain. Simulation results demonstrate that the proposed system can successfully achieve landing guidance in unknown environments by providing glide angle estimations with an average error of less than 1 degree. Full article
(This article belongs to the Special Issue UAV (Unmanned Aerial Vehicles) Networks: Recent Developments and Emerging Trends)
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