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Electronics
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Innovative Technologies and Services for Unmanned Aerial Vehicles
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Innovative Technologies and Services for Unmanned Aerial Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 7017

Special Issue Editors

Dr. Tao Hong

E-Mail Website
Guest Editor
School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
Interests: communication and networking; swarm intelligence and cooperative UAVs; autonomous flight systems
Dr. Fei Qi

E-Mail Website
Guest Editor
Mobile Technology Research Department, China Telecom Research Institute, Beijing 102209, China
Interests: Internet of Things; vehicular networks; massive-MIMO precoding; artificial intelligence; UAV communication; channel estimation

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to a Special Issue on "Innovative Technologies and Services for Unmanned Aerial Vehicles" in Electronics. Unmanned aerial vehicles (UAVs) have emerged as a transformative technology with a wide range of applications in various domains. This Special Issue aims to explore the latest developments in UAV technologies and services, highlighting innovative solutions that enhance their capabilities and applications.

The purpose of this Special Issue is to provide a platform for researchers, practitioners, and industry experts to share their knowledge, experiences, and perspectives on UAV technology. We aim to foster interdisciplinary discussions and collaborations to push the boundaries of UAV applications. By bringing together high-quality research articles and reviews, we seek to create a comprehensive overview of the advancements in this field and stimulate further research and development.

The scope of this Special Issue encompasses a broad range of topics related to innovative technologies and services for UAVs. We encourage submissions in areas including, but not limited to:

  • Autonomous navigation and control systems: advanced algorithms and techniques for autonomous UAV navigation, obstacle detection and avoidance, path planning, and formation flying.
  • Sensing and imaging technologies: integration of novel sensing technologies such as hyperspectral imaging, LiDAR, thermal imaging, and their applications in UAV data acquisition.
  • Communication and networking: efficient communication and networking solutions for UAVs operating in networked environments, including reliable and secure communication, network coordination, and swarm intelligence.
  • Payload and service delivery: innovative payload designs, delivery mechanisms, and applications of UAVs in payload transportation and service delivery, such as medical supply delivery or infrastructure inspections.
  • Energy efficiency and sustainability: research on energy harvesting, optimization algorithms, and sustainable power sources for UAVs to improve energy efficiency and sustainability.
  • Regulatory and legal aspects: discussions on the legal frameworks, privacy concerns, safety regulations, and ethical considerations associated with UAV operations.

We welcome original research articles, reviews, and case studies that address these topics. All submissions will undergo a rigorous peer-review process to ensure the quality and relevance of the contributions. Accepted papers will be published in this Special Issue, contributing to the collective knowledge and understanding of innovative technologies and services for UAVs.

We look forward to receiving your high-quality contributions to this Special Issue. Together, we can advance the field of UAV technology and its applications.

Dr. Tao Hong
Dr. Fei Qi
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 (UAVs)
  • control systems
  • sensing technologies
  • wireless communication
  • optimization algorithms

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

31 pages, 41889 KiB  
Article
Unmanned Aerial Vehicle Path Planning Using Acceleration-Based Potential Field Methods
by Mohammad R. Hayajneh, Mohammad H. Garibeh, Ahmad Bani Younes and Matthew A. Garratt
Electronics 2025, 14(1), 176; https://doi.org/10.3390/electronics14010176 - 3 Jan 2025
Viewed by 1115
Abstract
Online path planning for UAVs that are following a moving target is a critical component in applications that demand a soft landing over the target. In highly dynamic situations with accelerating targets, the classical potential field (PF) method, which considers only the relative [...] Read more.
Online path planning for UAVs that are following a moving target is a critical component in applications that demand a soft landing over the target. In highly dynamic situations with accelerating targets, the classical potential field (PF) method, which considers only the relative positions and/or velocities, cannot provide precision tracking and landing. Therefore, this work presents an improved acceleration-based potential field (ABPF) path planning method. This approach incorporates the relative accelerations of the UAV and the target in constructing an attractive field. By controlling the acceleration, the ABPF produces smoother trajectories and avoids sudden changes in the UAV’s motion. The proposed approach was implemented in different simulated scenarios with variable acceleration paths (i.e., circular, infinite, and helical). The simulation demonstrated the superiority of the proposed approach over the traditional PF. Moreover, similar path scenarios were experimentally evaluated using a quadrotor UAV in an indoor Vicon positioning system. To provide reliable estimations of the acceleration for the suggested method, a non-linear complementary filter was used to fuse information from the drone’s accelerometer and the Vicon system. The improved PF method was compared to the traditional PF method for each scenario. The results demonstrated a 50% improvement in the position, velocity, and acceleration accuracy across all scenarios. Furthermore, the ABPF responded faster to merging with the target path, with rising times of 1.5, 1.6, and 1.3 s for the circular, infinite, and helical trajectories, respectively. Full article
(This article belongs to the Special Issue Innovative Technologies and Services for Unmanned Aerial Vehicles)
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27 pages, 15683 KiB  
Article
GLBWOA: A Global–Local Balanced Whale Optimization Algorithm for UAV Path Planning
by Qiwu Wu, Weicong Tan, Renjun Zhan, Lingzhi Jiang, Li Zhu and Husheng Wu
Electronics 2024, 13(23), 4598; https://doi.org/10.3390/electronics13234598 - 21 Nov 2024
Viewed by 997
Abstract
To tackle the challenges of path planning for unmanned aerial vehicle (UAV) in complex environments, a global–local balanced whale optimization algorithm (GLBWOA) has been developed. Initially, to prevent the population from prematurely converging, a bubble net attack enhancement strategy is incorporated, and mutation [...] Read more.
To tackle the challenges of path planning for unmanned aerial vehicle (UAV) in complex environments, a global–local balanced whale optimization algorithm (GLBWOA) has been developed. Initially, to prevent the population from prematurely converging, a bubble net attack enhancement strategy is incorporated, and mutation operations are introduced at different stages of the algorithm to mitigate early convergence. Additionally, a failure parameter test mutation mechanism is integrated, along with a predefined termination rule to avoid excessive computation. The algorithm’s convergence is accelerated through mutation operations, further optimizing performance. Moreover, a random gradient-assisted optimization approach is applied, where the negative gradient direction is identified during each iteration, and an appropriate step size is selected to enhance the algorithm’s exploration capability toward finding the optimal solution. The performance of GLBWOA is benchmarked against several other algorithms, including SCA, BWO, BOA, and WOA, using the IEEE CEC2017 test functions. The results indicate that the GLBWOA outperforms other algorithms. Path-planning simulations are also conducted across four benchmark scenarios of varying complexity, revealing that the proposed algorithm achieves the lowest average total cost for flight path planning and exhibits high convergence accuracy, thus validating its reliability and superiority. Full article
(This article belongs to the Special Issue Innovative Technologies and Services for Unmanned Aerial Vehicles)
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19 pages, 21217 KiB  
Article
Air Traffic Flow Prediction in Aviation Networks Using a Multi-Dimensional Spatiotemporal Framework
by Cong Wu, Hui Ding, Zhongwang Fu and Ning Sun
Electronics 2024, 13(19), 3803; https://doi.org/10.3390/electronics13193803 - 25 Sep 2024
Viewed by 1300
Abstract
A novel, multi-dimensional, spatiotemporal prediction framework is proposed to enhance air traffic flow prediction in increasingly complex aviation networks. This framework incorporates graph convolutional networks (GCNs) with multi-dimensional Long Short-Term Memory (LSTM) networks and multi-scale, temporal convolution, employing an attention mechanism to effectively [...] Read more.
A novel, multi-dimensional, spatiotemporal prediction framework is proposed to enhance air traffic flow prediction in increasingly complex aviation networks. This framework incorporates graph convolutional networks (GCNs) with multi-dimensional Long Short-Term Memory (LSTM) networks and multi-scale, temporal convolution, employing an attention mechanism to effectively capture spatiotemporal dependencies. By addressing irregular topologies and dynamic temporal trends, the framework models local air traffic patterns with improved accuracy. The experimental results demonstrate significant predictive accuracy improvements over traditional methods, particularly in accounting for the complex nature of air traffic flows. The model’s scalability and adaptability extend its application to various aviation networks, encompassing all airspace units within three local networks, rather than focusing solely on airport traffic. These findings contribute to the development of more intelligent, accurate, and adaptive air traffic management systems, ultimately enhancing both operational efficiency and safety. Full article
(This article belongs to the Special Issue Innovative Technologies and Services for Unmanned Aerial Vehicles)
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17 pages, 1008 KiB  
Article
Full-Duplex Unmanned Aerial Vehicle Communications for Cellular Spectral Efficiency Enhancement Utilizing Device-to-Device Underlaying Structure
by Yuetian Zhou and Yang Li
Electronics 2024, 13(12), 2264; https://doi.org/10.3390/electronics13122264 - 9 Jun 2024
Viewed by 829
Abstract
Unmanned aerial vehicle (UAV) communications have gained recognition as a promising technology due to their unique characteristics of rapid deployment and flexible configuration. Meanwhile, device-to-device (D2D) and full-duplex (FD) technologies have emerged as promising methods for enhancing spectral efficiency and offloading traffic. One [...] Read more.
Unmanned aerial vehicle (UAV) communications have gained recognition as a promising technology due to their unique characteristics of rapid deployment and flexible configuration. Meanwhile, device-to-device (D2D) and full-duplex (FD) technologies have emerged as promising methods for enhancing spectral efficiency and offloading traffic. One significant advantage of UAVs is their ability to partition suitable D2D pairs to increase cell capacity. In this paper, we present a novel network model in which UAVs are considered D2D pairs underlaying cellular networks, integrating FD into the communication links between UAVs to improve spectral efficiency. We then investigate a resource allocation problem for the proposed FD-UAV D2D underlaying structure model, with the objective of maximizing the system’s sum rate. Specifically, the UAVs in our model operate in full-duplex mode as D2D users (DUs), allowing the reuse of both the uplink and downlink subcarrier resources of cellular users (CUs). This optimization challenge is formulated as a mixed-integer nonlinear programming problem, known for its NP-hard and intractable nature. To address this issue, we propose a heuristic algorithm (HA) that decomposes the problem into two steps: power allocation and user pairing. The optimal power allocation is solved as a nonlinear programming problem by searching among a finite set, while the user pairing problem is addressed using the Kuhn–Munkres algorithm. The numerical results indicate that our proposed FD-MaxSumCell-HA (full-duplex UAVs maximizing the cell sum rate with a heuristic algorithm) scheme for FD-UAV D2D underlaying models outperforms HD-UAV underlaying cellular networks, with improved access rates for UAVs in FD-MaxSumCell-HA compared to HD-UAV networks. Full article
(This article belongs to the Special Issue Innovative Technologies and Services for Unmanned Aerial Vehicles)
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15 pages, 9284 KiB  
Article
An Improved Lightweight Deep Learning Model and Implementation for Track Fastener Defect Detection with Unmanned Aerial Vehicles
by Qi Yu, Ao Liu, Xinxin Yang and Weimin Diao
Electronics 2024, 13(9), 1781; https://doi.org/10.3390/electronics13091781 - 5 May 2024
Cited by 5 | Viewed by 1565
Abstract
Track fastener defect detection is an essential component in ensuring railway safety operations. Traditional manual inspection methods no longer meet the requirements of modern railways. The use of deep learning image processing techniques for classifying and recognizing abnormal fasteners is faster, more accurate, [...] Read more.
Track fastener defect detection is an essential component in ensuring railway safety operations. Traditional manual inspection methods no longer meet the requirements of modern railways. The use of deep learning image processing techniques for classifying and recognizing abnormal fasteners is faster, more accurate, and more intelligent. With the widespread use of unmanned aerial vehicles (UAVs), conducting railway inspections using lightweight, low-power devices carried by UAVs has become a future trend. In this paper, we address the characteristics of track fastener detection tasks by improving the YOLOv4-tiny object detection model. We improved the model to output single-scale features and used the K-means++ algorithm to cluster the dataset, obtaining anchor boxes that were better suited to the dataset. Finally, we developed the FPGA platform and deployed the transformed model on this platform. The experimental results demonstrated that the improved model achieved an mAP of 95.1% and a speed of 295.9 FPS on the FPGA, surpassing the performance of existing object detection models. Moreover, the lightweight and low-powered FPGA platform meets the requirements for UAV deployment. Full article
(This article belongs to the Special Issue Innovative Technologies and Services for Unmanned Aerial Vehicles)
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