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Applied Sciences
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Design and Operation of Unmanned Aerial Systems
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Design and Operation of Unmanned Aerial Systems

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

Deadline for manuscript submissions: closed (10 June 2022) | Viewed by 9911

Special Issue Editors

Prof. Dr. Christina Bloebaum

E-Mail Website
Guest Editor
College of Aeronautics and Engineering, Kent State University, Kent, OH, USA
Interests: design of complex engineered systems; value-driven design for systems engineering; value models for ethical decision-making in UAS; Multidisciplinary Design Optimization (MDO)
Dr. Peng Wei

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, George Washington University, Washington, DC, USA
Interests: intersection of control, optimization, machine learning, and artificial intelligence for development of autonomy and decision support tools for aeronautics, aviation and aerial robotics; safety, efficiency, and scalability of decision-making systems in complex, uncertain and dynamic environments; applications in ATC/ATM, UTM, eVTOL UAM and ADR

Special Issue Information

Dear Colleagues,

Unmanned Aerial Systems (UAS) are revolutionizing aviation and aerospace industries, with the potential commercial economic and human capital impacts being significant and far-reaching. As the breadth of applications increase, so does the need and opportunity to address design and operation of UAS systematically. As new aircraft types and operational opportunities emerge in the civil airspace, such as small UAS and electric vertical take-off and landing (eVTOL) aircraft, new challenges emerge. These new aircraft potentially have different propulsion systems, higher autonomy levels, and significantly different designs and configurations to better match operational needs.

This special issue calls for innovative and groundbreaking publication contributions to address these design and operation challenges, with sample topics including (but not limited to): UAS traffic management; UAS design and operation for high-density airspace and mission risk analysis; machine learning, artificial intelligence and computer vision applications in UAS; UAS power system design; new design and operational models for urban air mobility; human-UAS interaction (including heterogeneous human-multi UAS teaming, safety and trust in human-UAS interaction, etc.); UAS communications (with other UAS, UTM, remote towers, etc.); development and incorporation of UAS regulation and policy in design and operation; and multi UAS teaming and control, among others.

Prof. Dr. Christina Bloebaum
Dr. Peng Wei
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. 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 Systems (UAS)
  • urban air mobility (UAM)
  • electric vertical take-off and landing (eVTOL) aircraft
  • machine learning
  • artificial intelligence
  • computer vision and autonomy applications in UAS and UAM
  • UAS traffic management (UTM)
  • risk analysis
  • human-autonomy teaming
  • multi-UAS cooperative control
  • UAS/UAM regulations Certification and policy

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Published Papers (2 papers)

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Research

15 pages, 519 KiB  
Article
Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning
by Ralvi Isufaj, Marsel Omeri and Miquel Angel Piera
Appl. Sci. 2022, 12(2), 610; https://doi.org/10.3390/app12020610 - 9 Jan 2022
Cited by 14 | Viewed by 3174
Abstract
Safety is the primary concern when it comes to air traffic. In-flight safety between Unmanned Aircraft Vehicles (UAVs) is ensured through pairwise separation minima, utilizing conflict detection and resolution methods. Existing methods mainly deal with pairwise conflicts, however, due to an expected increase [...] Read more.
Safety is the primary concern when it comes to air traffic. In-flight safety between Unmanned Aircraft Vehicles (UAVs) is ensured through pairwise separation minima, utilizing conflict detection and resolution methods. Existing methods mainly deal with pairwise conflicts, however, due to an expected increase in traffic density, encounters with more than two UAVs are likely to happen. In this paper, we model multi-UAV conflict resolution as a multiagent reinforcement learning problem. We implement an algorithm based on graph neural networks where cooperative agents can communicate to jointly generate resolution maneuvers. The model is evaluated in scenarios with 3 and 4 present agents. Results show that agents are able to successfully solve the multi-UAV conflicts through a cooperative strategy. Full article
(This article belongs to the Special Issue Design and Operation of Unmanned Aerial Systems)
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28 pages, 1722 KiB  
Article
Benefits of Advanced Air Mobility for Society and Environment: A Case Study of Ohio
by Esrat F. Dulia, Mir S. Sabuj and Syed A. M. Shihab
Appl. Sci. 2022, 12(1), 207; https://doi.org/10.3390/app12010207 - 26 Dec 2021
Cited by 13 | Viewed by 5894
Abstract
Advanced Air Mobility (AAM) is an emerging transportation system that will enable the safe and efficient low altitude operations and applications of unmanned aircraft (e.g., passenger transportation and cargo delivery) in the national airspace. This system is currently under active research and development [...] Read more.
Advanced Air Mobility (AAM) is an emerging transportation system that will enable the safe and efficient low altitude operations and applications of unmanned aircraft (e.g., passenger transportation and cargo delivery) in the national airspace. This system is currently under active research and development by NASA in collaboration with FAA, other federal partner agencies, industry, and academia to develop its infrastructure, information architecture, software functions, concepts of operation, operations management tools and other functional components. Existing studies have, however, not thoroughly analyzed the net positive impact of AAM on society and environment to justify investments in its infrastructure and implementation. In this work, we fill this gap by evaluating the non-monetary social impact of AAM in the state of Ohio for passengers, patients, farmers, logistics companies and their customers and bridge inspection entities, as well as its environmental impact, by conducting a thorough data-driven quantitative cost–benefit analysis of AAM from the perspective of the state government. To this end, the most relevant and significant benefit and cost factors are identified, monetized, and estimated. Existing ground transportation for the movement of passengers and goods within and across urban areas is considered as the base case. The findings demonstrate that AAM’s benefits are large and varied, far outweighing its costs. Insights on these benefits can help gain community acceptance of AAM, which is critical for successful implementation of AAM. The findings support decision-making for policymakers and provide justification for investments in AAM infrastructure by the government and private sector. Full article
(This article belongs to the Special Issue Design and Operation of Unmanned Aerial Systems)
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