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Aerospace
Special Issues
Unmanned Aerial Vehicles en-Route Modelling and Control
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Unmanned Aerial Vehicles en-Route Modelling and Control

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Air Traffic and Transportation".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 9997

Special Issue Editors

Prof. Dr. Keumjin Lee

E-Mail Website
Guest Editor
Air Transport and Logistics, Korea Aerospace University, Gyeonggi-do 10540, Korea
Interests: air traffic management; UAS traffic management; urban air mobility
Prof. Dr. Sang Hyun Kim

E-Mail Website
Guest Editor
School of Air Transport, Transportation, and Logistics, Korea Aerospace University, Gyeonggi-do 10540, Korea
Interests: operations of unmanned aircraft; urban air mobility; traditional air transportation

Special Issue Information

Dear Colleagues,

The market for unmanned aerial vehicles (UAVs), including urban air mobility (UAM), is expected to grow rapidly, garnering considerable research and significant investment worldwide. Enroute operation (e.g., in cruise or corridor) is critical to the safety, efficiency, robustness, and sustainability of UAV and UAM missions. This Special Issue intends to highlight recent technical advances to improve UAV enroute operations. Possible topics include, but are not limited to: 

  • UAV trajectory modeling, prediction, and optimization;
  • Mission planning and management for UAV operations;
  • Low altitude airspace design and management;
  • Separation assurance and other safety issues related to UAV operations;
  • Strategic route network design for UAV operations;
  • Noise and environmental issues of UAV operations;
  • Enroute traffic flow management for UAV;
  • Communication, Navigation, Surveillance infrastructure for UAV operations;
  • Simulation and performance evaluation for UAV operations;
  • Other topics related to UAV enroute modeling and control.

Prof. Dr. Keumjin Lee
Prof. Dr. Sang Hyun Kim
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. Aerospace is an international peer-reviewed open access monthly 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 vehicle
  • urban air mobility
  • en-route operation, corridor and airspace design

Published Papers (3 papers)

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Research

16 pages, 9395 KiB  
Article
Low-Cost Raspberry-Pi-Based UAS Detection and Classification System Using Machine Learning
by Carolyn J. Swinney and John C. Woods
Aerospace 2022, 9(12), 738; https://doi.org/10.3390/aerospace9120738 - 22 Nov 2022
Cited by 4 | Viewed by 2949
Abstract
Small Unmanned Aerial Systems (UAS) usage is undoubtedly increasing at a significant rate. However, alongside this expansion is a growing concern that dependable low-cost counter measures do not exist. To mitigate a threat in a restricted airspace, it must first be known that [...] Read more.
Small Unmanned Aerial Systems (UAS) usage is undoubtedly increasing at a significant rate. However, alongside this expansion is a growing concern that dependable low-cost counter measures do not exist. To mitigate a threat in a restricted airspace, it must first be known that a threat is present. With airport disruption from malicious UASs occurring regularly, low-cost methods for early warning are essential. This paper considers a low-cost early warning system for UAS detection and classification consisting of a BladeRF software-defined radio (SDR), wideband antenna and a Raspberry Pi 4 producing an edge node with a cost of under USD 540. The experiments showed that the Raspberry Pi using TensorFlow is capable of running a CNN feature extractor and machine learning classifier as part of an early warning system for UASs. Inference times ranged from 15 to 28 s for two-class UAS detection and 18 to 28 s for UAS type classification, suggesting that for systems that require timely results the Raspberry Pi would be better suited to act as a repeater of the raw SDR data, enabling the processing to be carried out on a higher powered central control unit. However, an early warning system would likely fuse multiple sensors. These experiments showed the RF machine learning classifier capable of running on a low-cost Raspberry Pi 4, which produced overall accuracy for a two-class detection system at 100% and 90.9% for UAS type classification on the UASs tested. The contribution of this research is a starting point for the consideration of low-cost early warning systems for UAS classification using machine learning, an SDR and Raspberry Pi. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles en-Route Modelling and Control)
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20 pages, 2108 KiB  
Article
UTM Architecture and Flight Demonstration in Korea
by Kyusur Jung, Songju Kim, Beechuilla Jung, Seyeon Kim, Hyunwoo Kang and Changbong Kang
Aerospace 2022, 9(11), 650; https://doi.org/10.3390/aerospace9110650 - 26 Oct 2022
Cited by 2 | Viewed by 4551
Abstract
Unmanned Aircraft System Traffic Management (UTM) is a traffic management system enabling drones to safely and efficiently fly in low-altitude airspace below 120~150m (400~500ft). UTM provides services such as communication, flight route management, location monitoring, and collision avoidance so that drones completing various [...] Read more.
Unmanned Aircraft System Traffic Management (UTM) is a traffic management system enabling drones to safely and efficiently fly in low-altitude airspace below 120~150m (400~500ft). UTM provides services such as communication, flight route management, location monitoring, and collision avoidance so that drones completing various missions can fly beyond visual line of sight (BVLOS) safely and increase the usability of airspace. In other words, UTM is a new air traffic management for drones with high levels of automation, advanced decision making and control. Many countries around the world are developing UTM systems that systematically manage the traffic of drones flying at low altitude. In Korea, UTM research has been ongoing as an R&D project since 2017. The purpose of this paper is to introduce the Korean UTM system and to apply it to actual flight demonstration through the developed operational procedures. The approach of this article is to establish Korean UTM architecture through existing references and examples from other countries, devise an operational procedure suitable for the system, and describe the results of using it for flight demonstration. In other words, this paper covers Korea’s UTM architecture, operational procedures, and flight demonstration through a macro approach to UTM. In addition, this paper presents policy and technical challenges that UTM must go through and that need to be solved in the future, which are classified into four categories. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles en-Route Modelling and Control)
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22 pages, 5357 KiB  
Article
Threat-Oriented Collaborative Path Planning of Unmanned Reconnaissance Mission for the Target Group
by Qihong Chen, Qingsong Zhao and Zhigang Zou
Aerospace 2022, 9(10), 577; https://doi.org/10.3390/aerospace9100577 - 4 Oct 2022
Cited by 3 | Viewed by 1519
Abstract
Unmanned aerial vehicle (UAV) cluster combat is a typical example of an intelligent cluster application, and it is characterized by its large scale, low cost, retrievability, and intra-cluster autonomous coordination. An unmanned reconnaissance mission for a target group (URMFTG) is a significant pattern [...] Read more.
Unmanned aerial vehicle (UAV) cluster combat is a typical example of an intelligent cluster application, and it is characterized by its large scale, low cost, retrievability, and intra-cluster autonomous coordination. An unmanned reconnaissance mission for a target group (URMFTG) is a significant pattern in UAV cluster combat. This paper discusses the collaborative path planning problem of unmanned aerial vehicle formations (UAVFs) and refueling tankers in a URMFTG with threat areas and fuel constraints. The purpose of collaborative path planning is to ensure that the UAVFs (with fuel constraints) can complete the reconnaissance mission for the target group with the assistance of refueling tankers, which is one of the most important constraints in the collaborative path planning. In this paper, a collaborative path-planning model is designed to analyze the relationship between the planning path of the UAVFs and the tankers, and a threat avoidance strategy is designed considering the threat area. This paper proposes a two-stage solution algorithm. It creates a UAVFs path-planning algorithm based on the fast search genetic algorithm (FSGA) and a refueling tanker path-planning algorithm based on the improved non-dominated sorting genetic algorithm II (NSGA-II). Based on simulation experiments, the solution method proposed in this paper can provide a better path-planning scheme for a URMFTG. That is, it decreases the rate of the UAVF’s distance growth from 3.1% to 2.2% for the path planning of UAVFs and provides a better Pareto solution set for the path planning of refueling tankers. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles en-Route Modelling and Control)
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