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Aerospace
Special Issues
UAV System Modelling Design and Simulation
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UAV System Modelling Design and Simulation

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: 31 January 2025 | Viewed by 5287

Special Issue Editors

Prof. Dr. Wojciech Skarka

E-Mail Website
Guest Editor
Department of Fundamentals of Machinery Design, Silesian University of Technology, Stanislawa Konarskiego 18A, 44-100 Gliwice, Poland
Interests: aircraft design; UAVs; aircraft structures; KBE; electric propulsion; composite structures; modelling and simulation; multidisciplinary design optimization; model-based design; simulation-based engineering; transdisciplinary engineering; fuel cells; energy systems; design methodologies; design and optimization
Dr. Miha Brojan

E-Mail Website
Guest Editor
Faculty Of Mechanical Engineering, University Of Ljubljana, Ljubljana, Slovenia
Interests: continuum mechanics; thin shell deformation; stability of structures; approximate theories in nonlinear mechanics; experimental methods; applied mathematics; fractional order/variable order calculus; flight mechanics; composites
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of Unmanned Aerial Vehicles (UAVs) is a highly dynamic area of aviation research. UAVs encompass an extensive group systems, including classic fixed-wing or rotorcraft systems; however, this group also includes multirotor systems, hybrid systems or other interesting solutions that combine the advantages of multirotors and fixed-wing aircraft, such as tailsitters, as well as ornithopters and many other systems never seen before in practice. Due to their specific interaction with the environment, it is necessary to study in detail the environment in which UAVs operate due to the autonomy of the operations they perform. The wide range of issues that need to be considered when designing UAV systems results in the intensive use of modelling and simulation methods.

The scope of the Special Issue will include, but is not limited to, the following topics associated with the modelling and simulation of UAV systems:

  • Novel drone designs and architectures;
  • Aerodynamic modelling and optimization;
  • Flight control and stability;
  • High-precision robust and fast maneuver control of UAVs;
  • Autonomous navigation and path planning;
  • Autonomous localization of UAVs under GPS-denied environment;
  • Autonomous/cooperative decision and planning for UAVs/swarms;
  • Sensor fusion and perception;
  • Application of novel sensing technologies in UAVs;
  • Propulsion and power supply systems;
  • New discoveries in UAV energy and power systems;
  • Artificial intelligence technologies in the field of UAVs.
  • Application of innovative design methods:
  • Model-based design;
  • Model-based system engineering.

We welcome original research articles and review articles that address any of the above topics or related areas. We look forward to receiving high-quality submissions that contribute to the advancement of the field of UAV system modelling, design and simulation.

Prof. Dr. Wojciech Skarka
Dr. Miha Brojan
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
  • drones
  • electric propulsion
  • simulation
  • numerical modelling

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Published Papers (3 papers)

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Research

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16 pages, 481 KiB  
Article
Topology Perception and Relative Positioning of UAV Swarm Formation Based on Low-Rank Optimization
by Chengliang Di and Xiaozhou Guo
Aerospace 2024, 11(6), 466; https://doi.org/10.3390/aerospace11060466 - 11 Jun 2024
Viewed by 1088
Abstract
In a satellite-denied environment, a swarm of drones is capable of achieving relative positioning and navigation by leveraging the high-precision ranging capabilities of the inter-drone data link. However, because of factors such as high drone mobility, complex and time-varying channel environments, electromagnetic interference, [...] Read more.
In a satellite-denied environment, a swarm of drones is capable of achieving relative positioning and navigation by leveraging the high-precision ranging capabilities of the inter-drone data link. However, because of factors such as high drone mobility, complex and time-varying channel environments, electromagnetic interference, and poor communication link quality, distance errors and even missing distance values between some nodes are inevitable. To address these issues, this paper proposes a low-rank optimization algorithm based on the eigenvalue scaling of the distance matrix. By gradually limiting the eigenvalues of the observed distance matrix, the algorithm reduces the rank of the matrix, bringing the observed distance matrix closer to the true value without errors or missing data. This process filters out distance errors, estimates and completes missing distance elements, and ensures high-precision calculations for subsequent topology perception and relative positioning. Simulation experiments demonstrate that the algorithm exhibits significant error filtering and missing element completion capabilities. Using the F-norm metric to measure the relative deviation from the true value, the algorithm can optimize the relative deviation of the observed distance matrix from 11.18% to 0.25%. Simultaneously, it reduces the relative positioning error from 518.05 m to 35.24 m, achieving robust topology perception and relative positioning for the drone swarm formation. Full article
(This article belongs to the Special Issue UAV System Modelling Design and Simulation)
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36 pages, 22100 KiB  
Article
Modeling Wind and Obstacle Disturbances for Effective Performance Observations and Analysis of Resilience in UAV Swarms
by Abhishek Phadke, F. Antonio Medrano, Tianxing Chu, Chandra N. Sekharan and Michael J. Starek
Aerospace 2024, 11(3), 237; https://doi.org/10.3390/aerospace11030237 - 18 Mar 2024
Cited by 15 | Viewed by 2179
Abstract
UAV swarms have multiple real-world applications but operate in a dynamic environment where disruptions can impede performance or stop mission progress. Ideally, a UAV swarm should be resilient to disruptions to maintain the desired performance and produce consistent outputs. Resilience is the system’s [...] Read more.
UAV swarms have multiple real-world applications but operate in a dynamic environment where disruptions can impede performance or stop mission progress. Ideally, a UAV swarm should be resilient to disruptions to maintain the desired performance and produce consistent outputs. Resilience is the system’s capability to withstand disruptions and maintain acceptable performance levels. Scientists propose novel methods for resilience integration in UAV swarms and test them in simulation scenarios to gauge the performance and observe the system response. However, current studies lack a comprehensive inclusion of modeled disruptions to monitor performance accurately. Existing approaches in compartmentalized research prevent a thorough coverage of disruptions to test resilient responses. Actual resilient systems require robustness in multiple components. The challenge begins with recognizing, classifying, and implementing accurate disruption models in simulation scenarios. This calls for a dedicated study to outline, categorize, and model interferences that can be included in current simulation software, which is provided herein. Wind and in-path obstacles are the two primary disruptions, particularly in the case of aerial vehicles. This study starts a multi-step process to implement these disruptions in simulations accurately. Wind and obstacles are modeled using multiple methods and implemented in simulation scenarios. Their presence in simulations is demonstrated, and suggested scenarios and targeted observations are recommended. The study concludes that introducing previously absent and accurately modeled disruptions, such as wind and obstacles in simulation scenarios, can significantly change how resilience in swarm deployments is recorded and presented. A dedicated section for future work includes suggestions for implementing other disruptions, such as component failure and network intrusion. Full article
(This article belongs to the Special Issue UAV System Modelling Design and Simulation)
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Review

Jump to: Research

33 pages, 6205 KiB  
Review
Hybrid Machine Learning and Reinforcement Learning Framework for Adaptive UAV Obstacle Avoidance
by Wojciech Skarka and Rukhseena Ashfaq
Aerospace 2024, 11(11), 870; https://doi.org/10.3390/aerospace11110870 - 24 Oct 2024
Viewed by 1317
Abstract
This review explores the integration of machine learning (ML) and reinforcement learning (RL) techniques in enhancing the navigation and obstacle avoidance capabilities of Unmanned Aerial Vehicles (UAVs). Various RL algorithms are assessed for their effectiveness in teaching UAVs autonomous navigation, with a focus [...] Read more.
This review explores the integration of machine learning (ML) and reinforcement learning (RL) techniques in enhancing the navigation and obstacle avoidance capabilities of Unmanned Aerial Vehicles (UAVs). Various RL algorithms are assessed for their effectiveness in teaching UAVs autonomous navigation, with a focus on state representation from UAV sensors and real-time environmental interaction. The review identifies the strengths and limitations of current methodologies and highlights gaps in the literature, proposing future research directions to advance UAV technology. Interdisciplinary approaches combining robotics, AI, and aeronautics are suggested to improve UAV performance in complex environments. Full article
(This article belongs to the Special Issue UAV System Modelling Design and Simulation)
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