Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
4.8
Journals
Drones
Special Issues
Selected Papers from the 2023 International Conference on Unmanned Aircraft...
share announcement

Selected Papers from the 2023 International Conference on Unmanned Aircraft Systems (ICUAS 2023)

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 4086

Special Issue Editors

Prof. Dr. Yangquan Chen

grade E-Mail Website
Guest Editor
Department of Mechanical Engineering (ME), University of California, Merced, CA 95343, USA
Interests: drone-based remote sensing; 3D photogrammetry; optimal sensing in cyber-physical systems; distributed collaborative sensing in distributed parameter systems; mobile sensor networks; virtual metrology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. George Nikolakopoulos

E-Mail Website
Guest Editor
Department of Computer Science, Electrical and Space Engineering at Luleå University of Technology, SE-97187 Luleå, Sweden
Interests: robotics and AI; control applications
Special Issues, Collections and Topics in MDPI journals
Dr. Anna Konert

E-Mail Website
Guest Editor
Faculty of Law and Administration, Lazarski University, 02-662 Warszawa, Poland
Interests: aviation law; space law; civil law (liabilities); European law; international law
Prof. Dr. Andrea Monteriù

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy
Interests: intelligent household; Internet of Things; interoperability; smart monitoring; smart living; safety; Security; Energy Management; home automation; smart sensors; wireless sensor networks; machine learning; home appliances and devices; digital twin; cyber-physical system; risk assessment; operators’ safety
Special Issues, Collections and Topics in MDPI journals
Dr. Benjamyn Scott

E-Mail Website
Guest Editor
International Institute of Air and Space Law, Leiden University, 2311 ES Leiden, The Netherlands
Interests: aerospace law; air law; cybersecurity; unmanned aircraft

Special Issue Information

Dear Colleagues,

This Special Issue is inspired by “The 2023 Int´l Conference on Unmanned Aircraft Systems”, which will be held on June 6–9, 2023 in Warsaw, Poland. The most exciting and innovative papers presented at ICUAS 2023 will be selected to be extended and recommended to this Special Issue. Additionally, we wish to invite you to contribute by submitting articles concerning your recent research, experimental work, reviews, and/or case studies related, but not limited, to the following topics:

Airspace Control

Networked UAS

Airspace Management

Payloads

Airworthiness

Path Planning and Navigation

Autonomy

Reliability of UAS

Biologically Inspired UAS

Risk Analysis

Certification

See/Sense-Detect-and-Avoid Systems

Control Architectures

Security

Energy Efficient UAS

Sensor Fusion

Environmental Issues

Smart Sensors

Fail-Safe Systems

Standardization

Frequency Management

Technology Challenges

Integration

Training

Interoperability

UAS Applications

Levels of Safety

UAS Communications

Manned/Unmanned Aviation

UAS Testbeds

Micro- and Mini-UAS

UAS Transportation Management (UTM)

Prof. Dr. Yangquan Chen
Prof. Dr. George Nikolakopoulos
Dr. Anna Konert
Dr. Andrea Monteriù
Dr. Benjamyn Scott
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.

 

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

19 pages, 2422 KiB  
Article
Rule-Based Verification of Autonomous Unmanned Aerial Vehicles
by Christoph Sieber, Luis Miguel Vieira da Silva, Kilian Grünhagen and Alexander Fay
Drones 2024, 8(1), 26; https://doi.org/10.3390/drones8010026 - 20 Jan 2024
Viewed by 1231
Abstract
Automation enhances the capabilities of unmanned aerial vehicles (UAVs) by enabling self-determined behavior, while reducing the need for extensive human involvement. Future concepts envision a single human operator commanding multiple autonomous UAVs with minimal supervision. Despite advances in automation, there remains a demand [...] Read more.
Automation enhances the capabilities of unmanned aerial vehicles (UAVs) by enabling self-determined behavior, while reducing the need for extensive human involvement. Future concepts envision a single human operator commanding multiple autonomous UAVs with minimal supervision. Despite advances in automation, there remains a demand for a “human in command” to assume overall responsibility, driven by concerns about UAV safety and regulatory compliance. In response to these challenges, a method for runtime verification of UAVs using a knowledge-based system is introduced. This method empowers human operators to identify unsafe behavior without assuming full control of the UAV. Aspects of automated formalization, updating and processing of knowledge elements at runtime, coupled with an automatic reasoning process, are considered. The result is an ontology-based approach for runtime verification, addressing the growing complexity of UAVs and the need to ensure safety in the context of evolving aviation regulations. Full article
(This article belongs to the Special Issue Selected Papers from the 2023 International Conference on Unmanned Aircraft Systems (ICUAS 2023))
►▼ Show Figures

Figure 1

Review

Jump to: Research

34 pages, 3241 KiB  
Review
A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs
by Serhat Sönmez, Matthew J. Rutherford and Kimon P. Valavanis
Drones 2024, 8(4), 116; https://doi.org/10.3390/drones8040116 - 22 Mar 2024
Viewed by 839
Abstract
Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Their navigation controllers include onboard sensor suites that facilitate safe, autonomous or semi-autonomous multirotor flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying [...] Read more.
Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Their navigation controllers include onboard sensor suites that facilitate safe, autonomous or semi-autonomous multirotor flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying in uncertain and dynamically changing environments. During the last decade, given the available computational power, different learning-based algorithms have been derived, implemented, and tested to navigate and control, among other systems, multirotor UAVs. Learning algorithms have been and are used to derive data-driven based models, to identify parameters, to track objects, to develop navigation controllers, and to learn the environments in which multirotors operate. Learning algorithms combined with model-based control techniques have proven beneficial when applied to multirotors. This survey summarizes the research published since 2015, dividing algorithms, techniques, and methodologies into offline and online learning categories and then further classifying them into machine learning, deep learning, and reinforcement learning sub-categories. An integral part and focus of this survey is on online learning algorithms as applied to multirotors, with the aim to register the type of learning techniques that are either hard or almost hard real-time implementable, as well as to understand what information is learned, why, how, and how fast. The outcome of the survey offers a clear understanding of the recent state of the art and of the type and kind of learning-based algorithms that may be implemented, tested, and executed in real time. Full article
(This article belongs to the Special Issue Selected Papers from the 2023 International Conference on Unmanned Aircraft Systems (ICUAS 2023))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop