Feature Papers Collection of the World’s Top 2% Scientists in Drones

A topical collection in Drones (ISSN 2504-446X).

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Editors


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Collection 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

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Collection 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

Topical Collection Information

Dear Colleagues,

The rapid advancement of drone technology has resulted in an array of innovative applications that have far-reaching implications across various disciplines. Drones takes immense pride in spearheading the dissemination of groundbreaking research and providing insights pertaining to drone technology. Last year, Stanford University published an update of the list of the top 2% most widely cited scientists—the World’s Top 2% Scientists—and 25 Editorial Board Members from Drones were featured in it. In light of this, Drones is thrilled to announce a Topical Collection dedicated to showcasing the exceptional work of scholars from the World's Top 2% Scientists and the Highly Cited Researchers List.

The Topical Collection aims to encapsulate the latest advancements, breakthroughs, and visionary concepts in drone technology and its multifaceted applications. By focusing exclusively on the work of the world's most influential researchers, we seek to present a collection of research articles and reviews that collectively define the cutting edge of drone innovation.

This Topical Collection accepts manuscripts in the form of an original research article or a review in the realm of drones. Manuscripts will undergo rigorous peer-review to ensure the highest standards of quality and impact.

We eagerly anticipate your participation in this Collection.

Prof. Dr. Diego González-Aguilera
Prof. Dr. Pablo Rodríguez-Gonzálvez
Collection 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 collection 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 (3 papers)

2024

27 pages, 9560 KiB  
Article
Bifurcation Analysis and Sticking Phenomenon for Unmanned Rotor-Nacelle Systems with the Presence of Multi-Segmented Structural Nonlinearity
by Anthony Quintana, Brian Evan Saunders, Rui Vasconcellos and Abdessattar Abdelkefi
Drones 2024, 8(2), 59; https://doi.org/10.3390/drones8020059 - 08 Feb 2024
Viewed by 1005
Abstract
Whirl flutter is a phenomenon caused by an aeroelastic instability, causing oscillations to propagate in manned or unmanned rotor-nacelle type aircraft. Under the conditions where multi-segmented freeplay are present, complex behaviors can dominate these oscillations and can lead to disastrous consequences. This study [...] Read more.
Whirl flutter is a phenomenon caused by an aeroelastic instability, causing oscillations to propagate in manned or unmanned rotor-nacelle type aircraft. Under the conditions where multi-segmented freeplay are present, complex behaviors can dominate these oscillations and can lead to disastrous consequences. This study investigates a rotor-nacelle system with multi-segmented stiffnesses with a freeplay gap to encompass the real-world influences of aircraft. The mathematical aerodynamics model considers a quasi-steady application of strip theory along each blade to outline the external forces being applied. A free-body diagram is then used to incorporate the structural stiffness and damping terms with multi-segmented freeplay considered in the structural stiffness matrix. Multiple structural responses of the defined system are investigated and characterized to determine the influence of varying symmetric and asymmetric multi-segmented stiffnesses with varying gap parameters, including a route to impact investigation. The findings are characterized using phase portraits, Poincaré maps, time histories, and basins of attraction. It is found that under these conditions, the structural influences can lead to aperiodic oscillations with the existence of grazing bifurcations. Furthermore, these results unveil that under certain conditions and high freestream velocities, the sticking phenomenon becomes apparent which is strongly dependent on the strength of the multi-segmented representation, its gap sizes, and its symmetry. Lastly, a route to impact study shows the strong coupled influence between pitch and yaw when asymmetric conditions are applied and the possible presence of grazing-sliding bifurcations. The numerical simulations performed in this study can form a basis for drone designers to create reliable rotor-nacelle systems resistant to whirl flutter caused by freeplay effects. Full article
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15 pages, 16300 KiB  
Article
A Novel Technique Based on Machine Learning for Detecting and Segmenting Trees in Very High Resolution Digital Images from Unmanned Aerial Vehicles
by Loukas Kouvaras and George P. Petropoulos
Drones 2024, 8(2), 43; https://doi.org/10.3390/drones8020043 - 01 Feb 2024
Cited by 1 | Viewed by 1571
Abstract
The present study proposes a technique for automated tree crown detection and segmentation in digital images derived from unmanned aerial vehicles (UAVs) using a machine learning (ML) algorithm named Detectron2. The technique, which was developed in the python programming language, receives as input [...] Read more.
The present study proposes a technique for automated tree crown detection and segmentation in digital images derived from unmanned aerial vehicles (UAVs) using a machine learning (ML) algorithm named Detectron2. The technique, which was developed in the python programming language, receives as input images with object boundary information. After training on sets of data, it is able to set its own object boundaries. In the present study, the algorithm was trained for tree crown detection and segmentation. The test bed consisted of UAV imagery of an agricultural field of tangerine trees in the city of Palermo in Sicily, Italy. The algorithm’s output was the accurate boundary of each tree. The output from the developed algorithm was compared against the results of tree boundary segmentation generated by the Support Vector Machine (SVM) supervised classifier, which has proven to be a very promising object segmentation method. The results from the two methods were compared with the most accurate yet time-consuming method, direct digitalization. For accuracy assessment purposes, the detected area efficiency, skipped area rate, and false area rate were estimated for both methods. The results showed that the Detectron2 algorithm is more efficient in segmenting the relevant data when compared to the SVM model in two out of the three indices. Specifically, the Detectron2 algorithm exhibited a 0.959% and 0.041% fidelity rate on the common detected and skipped area rate, respectively, when compared with the digitalization method. The SVM exhibited 0.902% and 0.097%, respectively. On the other hand, the SVM classification generated better false detected area results, with 0.035% accuracy, compared to the Detectron2 algorithm’s 0.056%. Having an accurate estimation of the tree boundaries from the Detectron2 algorithm, the tree health assessment was evaluated last. For this to happen, three different vegetation indices were produced (NDVI, GLI and VARI). All those indices showed tree health as average. All in all, the results demonstrated the ability of the technique to detect and segment trees from UAV imagery. Full article
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24 pages, 414 KiB  
Review
Navigation and Deployment of Solar-Powered Unmanned Aerial Vehicles for Civilian Applications: A Comprehensive Review
by Siyuan Li, Zixuan Fang, Satish C. Verma, Jingwen Wei and Andrey V. Savkin
Drones 2024, 8(2), 42; https://doi.org/10.3390/drones8020042 - 31 Jan 2024
Viewed by 1329
Abstract
Unmanned aerial systems and renewable energy are two research areas that have developed rapidly over the last few decades. Solar-powered unmanned aerial vehicles (SUAVs) are likely to become dominant in the near future. They have the advantage of low cost and safe operation [...] Read more.
Unmanned aerial systems and renewable energy are two research areas that have developed rapidly over the last few decades. Solar-powered unmanned aerial vehicles (SUAVs) are likely to become dominant in the near future. They have the advantage of low cost and safe operation features that mitigate the barriers to their use in various environments. Developing effective algorithms for navigating and deploying SUAVs is essential for implementing this technology in real-life applications. Effective navigation and deployment algorithms also ensure the safety and efficiency of SUAV operations. This comprehensive review paper summarizes some state-of-the-art SUAV applications and provides an overview of the navigation and deployment algorithms for SUAVs. Some commonly used energy-harvesting models are described as well. Finally, some interesting and promising directions for future SUAV research are suggested. Full article
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