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Drones
Special Issues
AAM Integration: Strategic Insights and Goals
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AAM Integration: Strategic Insights and Goals

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Innovative Urban Mobility".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 17035

Special Issue Editors

Dr. Georgi Georgiev

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Guest Editor
Dr. Georgiev Consulting GmbH, Munich, Germany
Interests: UAS integration in metropolitan regions; sustainable urban and regional planning; foresight analysis
Friedrich-Wilhelm Bauer

E-Mail Website
Guest Editor
Head of Project Laboratory for Materials in Flight Operations, Department of Mechanical Engineering, Hannover University of Applied Sciences and Arts, Ricklinger Stadtweg 120, 30459 Hannover, Germany
Interests: safe and secure UAS operations; aerospace materials; thermographic inspection methods
Prof. Dr. Ralf Sindelar

E-Mail Website1 Website2
Guest Editor
Head of Department of Materials Technology, Faculty II-Mechanical and Bioprocess Engineering, Hannover University of Applied Sciences and Arts, Ricklinger Stadtweg 120, 30459 Hannover, Germany
Interests: safety and durability in terms of mechanical aspects; energy storage
Prof. Dr. Diego González-Aguilera

E-Mail Website
Guest Editor
Cartographic and Land Engineering Department, Higher Polytechnic School of Avila, University of Salamanca, Hornos Caleros, 50, 05003 Avila, Spain
Interests: photogrammetry; laser scanning; 3D modeling; topography; cartography
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pablo Rodríguez-Gonzálvez

E-Mail Website
Guest Editor
Department of Mining Technology, Topography and Structures, University of León, Avda. Astorga, s/n, 24401 Ponferrada, Spain
Interests: photogrammetry; drones; laser scanning; radiometric calibration; remote sensing; RGB-D sensors; 3D modeling; mobile mapping; metrology; verification; inspection; quality control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Transportation of humans and goods has become an incredibly challenging task worldwide, both in metropolitan centers and rural regions. The answer to this is the use of urban airspace involving unmanned aircraft systems. The concept is highly promising, and the first successful trials have already been performed worldwide.

The UAS integration community has recognized the interdisciplinarity of the task, as well as the resulting demand of regulatory and business models and actions or local, regional, national, and international level.

The main challenge is the integration of advanced air mobility in terms of its operation in the airspace into the established mobility systems and processes—general aviation, ground mobility, etc. With this, advanced air mobility shall soon become a common type of mobility.

This Special Issue of Drones is to be understood as a foresight analysis on the overall AAM system—its development, integration, and operation. Moreover, it is dedicated to the regulatory, including standardization, and process management aspects of the successful, safe, and sustainable integration of UAS into the urban airspace and the existing multimodal mobility and transport systems.

Specific discussion theses for the expected article entries are:

  • When developing the legislative and/or regulative framework on AAM, focus more on the ground perspective—mobility, infrastructure, risks, integration in existing mobility modes and urban fabric.
  • Going from Geofencing towards Geoguiding.
  • High-added-value goods delivery by drones will demand a high volume of expertise and training for the existing personnel in the business.
  • UA queuing and hanging over urban areas—technological and processual aspects.
  • AAM = combination of A. aviation + B. urban mobility and planning, where B. is highly relevant for the successful mass replication.
  • Enabling safe and secure AAM-Ops within the U-Space development and integration.
  • Balancing between GNSS and advanced cellular communication systems.
  • Necessary high added value for the UAV-based transports performed: time, economic value, importance, and human lives.
  • AAM: Between aviation and automotives—basic requirements for operational safety.
  • Integration in urban and metropolitan planning and development.
  • Challenges in interoperability due to different levels of automation and/or autonomy within existing and novel mobility modes:
    • Mapping of the challenges;
    • Defining basic requirements.

Dr. Georgi Georgiev
Friedrich-Wilhelm Bauer
Prof. Dr. Ralf Sindelar
Prof. Dr. Diego González-Aguilera
Dr. Pablo Rodríguez-Gonzálvez
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. Drones 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 2600 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 aircraft systems
  • advanced air mobility
  • integration
  • existing mobility systems
  • situational awareness
  • urban airspace

Published Papers (8 papers)

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Research

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27 pages, 846 KiB  
Article
Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
by Mehmet Aksit, Hanne Say, Mehmet Arda Eren and Valter Vieira de Camargo
Drones 2023, 7(9), 565; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7090565 - 03 Sep 2023
Viewed by 1175
Abstract
To carry out required aid operations efficiently and effectively after an occurrence of a disaster such as an earthquake, emergency control centers must determine the effect of disasters precisely and and in a timely manner. Different kinds of data-gathering techniques can be used [...] Read more.
To carry out required aid operations efficiently and effectively after an occurrence of a disaster such as an earthquake, emergency control centers must determine the effect of disasters precisely and and in a timely manner. Different kinds of data-gathering techniques can be used to collect data from disaster areas, such as sensors, cameras, and unmanned aerial vehicles (UAVs). Furthermore, data-fusion techniques can be adopted to combine the data gathered from different sources to enhance the situation awareness. Recent research and development activities on advanced air mobility (AAM) and related unmanned aerial systems (UASs) provide new opportunities. Unfortunately, designing these systems for disaster situation analysis is a challenging task due to the topological complexity of urban areas, and multiplicity and variability of the available data sources. Although there are a considerable number of research publications on data fusion, almost none of them deal with estimating the optimal set of heterogeneous data sources that provide the best effectiveness and efficiency value in determining the effect of disasters. Moreover, existing publications are generally problem- and system-specific. This article proposes a model-based novel analysis and synthesis framework to determine the optimal data fusion set among possibly many alternatives, before expensive implementation and installation activities are carried out. Full article
(This article belongs to the Special Issue AAM Integration: Strategic Insights and Goals)
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15 pages, 2530 KiB  
Article
Enhancing Data Discretization for Smoother Drone Input Using GAN-Based IMU Data Augmentation
by Dmytro Petrenko, Yurii Kryvenchuk and Vitaliy Yakovyna
Drones 2023, 7(7), 463; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7070463 - 11 Jul 2023
Cited by 1 | Viewed by 1200
Abstract
This study investigates the use of generative adversarial network (GAN)-based data augmentation to enhance data discretization for smoother drone input. The goal is to improve unmanned aerial vehicles’ (UAVs) performance and maneuverability by incorporating synthetic inertial measurement unit (IMU) data. The GAN model [...] Read more.
This study investigates the use of generative adversarial network (GAN)-based data augmentation to enhance data discretization for smoother drone input. The goal is to improve unmanned aerial vehicles’ (UAVs) performance and maneuverability by incorporating synthetic inertial measurement unit (IMU) data. The GAN model is employed to generate synthetic IMU data that closely resemble real-world IMU measurements. The methodology involves training the GAN model using a dataset of real IMU data and then using the trained model to generate synthetic IMU data. The generated synthetic data are then combined with the real data for data discretization. The resulting improved data discretization is evaluated using statistical metrics and a similarity evaluation. The improved data discretization demonstrates enhanced drone performance in terms of flight stability, control accuracy, and smoothness of movements when compared to standard data discretization methods. These results highlight the potential of GAN-based data augmentation for enhancing data discretization and improving drone performance. The proposition of improved data discretization offers a tangible benefit for the successful integration of Advanced Air Mobility (AAM) systems. Enhancing the accuracy and reliability of data acquisition and processing in UAS makes UAS operations safer and more reliable. This improvement is crucial for achieving the goal of automated and autonomous operations in diverse settlement environments, encompassing multiple mobility modes such as ground and air transportation. Full article
(This article belongs to the Special Issue AAM Integration: Strategic Insights and Goals)
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27 pages, 4194 KiB  
Article
Flying Sensor and Edge Network-Based Advanced Air Mobility Systems: Reliability Analysis and Applications for Urban Monitoring
by Herman Fesenko, Oleg Illiashenko, Vyacheslav Kharchenko, Ihor Kliushnikov, Olga Morozova, Anatoliy Sachenko and Stanislav Skorobohatko
Drones 2023, 7(7), 409; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7070409 - 21 Jun 2023
Cited by 8 | Viewed by 1257
Abstract
Typical structures of monitoring systems (MSs) that are used in urban complex objects (UCOs) (such as large industrial facilities, power facilities, and others) during the post-accident period are combined with the technologies of flying sensor networks (FSNets) and flying edge networks (FENets) (FSNets [...] Read more.
Typical structures of monitoring systems (MSs) that are used in urban complex objects (UCOs) (such as large industrial facilities, power facilities, and others) during the post-accident period are combined with the technologies of flying sensor networks (FSNets) and flying edge networks (FENets) (FSNets and FENets); cloud/fog computing and artificial intelligence are also developed. An FSNets and FENets-based MS, composed of one of the Advanced Air Mobility (AAM) systems classes, which comprise main and virtual crisis centers, fleets of flying sensors, edge nodes, and a ground control station, is presented and discussed. Reliability and survivability models of the MS for the UCOs, considering various operation conditions and options of redundancy, are developed and explored. A tool to support the research on MS reliability, survivability, and the choice of parameters is developed and described. Crucially, this paper enhances the technique for assessing systems using the multi-parametrical deterioration of characteristics as a class of multi-state systems. Problems that may arise when using FSNets/FENet-based AAM systems are discussed. The main research results comprise a structural basis, a set of models, and a tool for calculating the reliability and survivability of FSNets/FENet-based AAM systems, with various options for distributing the processing and control resources between components, their failure rates, and degradation scenarios. Full article
(This article belongs to the Special Issue AAM Integration: Strategic Insights and Goals)
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39 pages, 9548 KiB  
Article
Assuring Safe and Efficient Operation of UAV Using Explainable Machine Learning
by Abdulrahman Alharbi, Ivan Petrunin and Dimitrios Panagiotakopoulos
Drones 2023, 7(5), 327; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7050327 - 19 May 2023
Cited by 2 | Viewed by 1856
Abstract
The accurate estimation of airspace capacity in unmanned traffic management (UTM) operations is critical for a safe, efficient, and equitable allocation of airspace system resources. While conventional approaches for assessing airspace complexity certainly exist, these methods fail to capture true airspace capacity, since [...] Read more.
The accurate estimation of airspace capacity in unmanned traffic management (UTM) operations is critical for a safe, efficient, and equitable allocation of airspace system resources. While conventional approaches for assessing airspace complexity certainly exist, these methods fail to capture true airspace capacity, since they fail to address several important variables (such as weather). Meanwhile, existing AI-based decision-support systems evince opacity and inexplicability, and this restricts their practical application. With these challenges in mind, the authors propose a tailored solution to the needs of demand and capacity management (DCM) services. This solution, by deploying a synthesized fuzzy rule-based model and deep learning will address the trade-off between explicability and performance. In doing so, it will generate an intelligent system that will be explicable and reasonably comprehensible. The results show that this advisory system will be able to indicate the most appropriate regions for unmanned aerial vehicle (UAVs) operation, and it will also increase UTM airspace availability by more than 23%. Moreover, the proposed system demonstrates a maximum capacity gain of 65% and a minimum safety gain of 35%, while possessing an explainability attribute of 70%. This will assist UTM authorities through more effective airspace capacity estimation and the formulation of new operational regulations and performance requirements. Full article
(This article belongs to the Special Issue AAM Integration: Strategic Insights and Goals)
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34 pages, 8892 KiB  
Article
UAV Path Planning Optimization Strategy: Considerations of Urban Morphology, Microclimate, and Energy Efficiency Using Q-Learning Algorithm
by Anderson Souto, Rodrigo Alfaia, Evelin Cardoso, Jasmine Araújo and Carlos Francês
Drones 2023, 7(2), 123; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7020123 - 09 Feb 2023
Cited by 9 | Viewed by 2501
Abstract
The use of unmanned aerial vehicles (UAVS) has been suggested as a potential communications alternative due to their fast implantation, which makes this resource an ideal solution to provide support in scenarios such as natural disasters or intentional attacks that may cause partial [...] Read more.
The use of unmanned aerial vehicles (UAVS) has been suggested as a potential communications alternative due to their fast implantation, which makes this resource an ideal solution to provide support in scenarios such as natural disasters or intentional attacks that may cause partial or complete disruption of telecommunications services. However, one limitation of this solution is energy autonomy, which affects mission life. With this in mind, our group has developed a new method based on reinforcement learning that aims to reduce the power consumption of UAV missions in disaster scenarios to circumvent the negative effects of wind variations, thus optimizing the timing of the aerial mesh in locations affected by the disruption of fiber-optic-based telecommunications. The method considers the K-means to stagger the position of the resource stations—from which the UAVS launched—within the topology of Stockholm, Sweden. For the UAVS’ locomotion, the Q-learning approach was used to investigate possible actions that the UAVS could take due to urban obstacles randomly distributed in the scenario and due to wind speed. The latter is related to the way the UAVS are arranged during the mission. The numerical results of the simulations have shown that the solution based on reinforcement learning was able to reduce the power consumption by 15.93% compared to the naive solution, which can lead to an increase in the life of UAV missions. Full article
(This article belongs to the Special Issue AAM Integration: Strategic Insights and Goals)
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31 pages, 7826 KiB  
Article
Deep Learning Architecture for UAV Traffic-Density Prediction
by Abdulrahman Alharbi, Ivan Petrunin and Dimitrios Panagiotakopoulos
Drones 2023, 7(2), 78; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7020078 - 22 Jan 2023
Cited by 4 | Viewed by 2996
Abstract
The research community has paid great attention to the prediction of air traffic flows. Nonetheless, research examining the prediction of air traffic patterns for unmanned aircraft traffic management (UTM) is relatively sparse at present. Thus, this paper proposes a one-dimensional convolutional neural network [...] Read more.
The research community has paid great attention to the prediction of air traffic flows. Nonetheless, research examining the prediction of air traffic patterns for unmanned aircraft traffic management (UTM) is relatively sparse at present. Thus, this paper proposes a one-dimensional convolutional neural network and encoder-decoder LSTM framework to integrate air traffic flow prediction with the intrinsic complexity metric. This adapted complexity metric takes into account the important differences between ATM and UTM operations, such as dynamic flow structures and airspace density. Additionally, the proposed methodology has been evaluated and verified in a simulation scenario environment, in which a drone delivery system that is considered essential in the delivery of COVID-19 sample tests, package delivery services from multiple post offices, an inspection of the railway infrastructure and fire-surveillance tasks. Moreover, the prediction model also considers the impacts of other significant factors, including emergency UTM operations, static no-fly zones (NFZs), and variations in weather conditions. The results show that the proposed model achieves the smallest RMSE value in all scenarios compared to other approaches. Specifically, the prediction error of the proposed model is 8.34% lower than the shallow neural network (on average) and 19.87% lower than the regression model on average. Full article
(This article belongs to the Special Issue AAM Integration: Strategic Insights and Goals)
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20 pages, 6265 KiB  
Article
Estimating the Economic Viability of Advanced Air Mobility Use Cases: Towards the Slope of Enlightenment
by Jan Pertz, Malte Niklaß, Majed Swaid, Volker Gollnick, Sven Kopera, Kolin Schunck and Stephan Baur
Drones 2023, 7(2), 75; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7020075 - 20 Jan 2023
Cited by 4 | Viewed by 2579
Abstract
While different vehicle configurations enter the AAM market, airlines declare different ticket fares for their operations. This research investigates the operating cost of an airline and the economic viability with the announced fare per km rates. For this purpose, three use cases in [...] Read more.
While different vehicle configurations enter the AAM market, airlines declare different ticket fares for their operations. This research investigates the operating cost of an airline and the economic viability with the announced fare per km rates. For this purpose, three use cases in the metropolitan area of Hamburg showcase representative applications of an AAM system, whereby a flight trajectory model calculates a flight time in each case. The direct operating cost are investigated for each use case individually and are sub-classified in five categories: fee, crew, maintenance, fuel and capital costs. Here, each use case has its own cost characteristics, in which different cost elements dominate. Additionally, a sensitivity analysis shows the effect of a variation of the flight cycles and load factor, that influences the costs as well as the airline business itself. Based on the occurring cost, a profit margin per available seat kilometer lead to a necessary fare per km, that an airline has to charge. Full article
(This article belongs to the Special Issue AAM Integration: Strategic Insights and Goals)
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Review

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24 pages, 489 KiB  
Review
Advanced Air Mobility and Evolution of Mobile Networks
by Lechosław Tomaszewski and Robert Kołakowski
Drones 2023, 7(9), 556; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7090556 - 29 Aug 2023
Viewed by 1321
Abstract
Advanced Air Mobility (AAM) is a promising field of services based on Unmanned Aerial Vehicles (UAVs), which aims to provide people and cargo transportation services in underserved areas. The recent advancements in the fields of aviation and mobile telecommunication networks have opened up [...] Read more.
Advanced Air Mobility (AAM) is a promising field of services based on Unmanned Aerial Vehicles (UAVs), which aims to provide people and cargo transportation services in underserved areas. The recent advancements in the fields of aviation and mobile telecommunication networks have opened up multiple opportunities for the development of disruptive AAM applications. This paper presents the overview and identifies the major requirements of emerging AAM use cases to confront them with the features provided by the 5G System (5GS), which is commonly considered the key enabler in providing commercial AAM services. The major benefits, gaps, and issues regarding using 5GS to serve AAM operations are identified and discussed. Finally, the future perspectives for AAM services are outlined with a focus on the potential benefit that can be provided as the mobile network evolves towards 6G. Full article
(This article belongs to the Special Issue AAM Integration: Strategic Insights and Goals)
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