Emerging Technologies and Innovations in Unmanned Aerial Vehicle Control Systems

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Design and Development".

Deadline for manuscript submissions: 22 December 2024 | Viewed by 5594

Special Issue Editor


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Guest Editor
Department of Industrial Engineering (DIN), Forlì Campus, University of Bologna, 47121 Forlì, Italy
Interests: flight mechanics; aircraft design; performance analysis and optimization; battery-powered vehicles; state estimation; flight testing; model-based design; modeling, simulation, and control; underactuated systems; spacecraft attitude control

Special Issue Information

Dear Colleagues,

Unmanned aerial systems are becoming a key factor in reducing the required time, risk, and cost related to a wide range of both military and civil applications. Enforced by the growing availability of miniaturized avionic systems, many efforts have been devoted to the development of novel design tools for optimal performance, efficient control laws, and accurate state estimation algorithms. By taking advantage of consolidated experience in both unmanned and conventional aviation, manufacturers and transport stakeholders are also investigating concepts of personal air transportation systems, where reliability, efficiency, and flexibility of guidance, navigation, and control laws is paramount.

This Special Issue aims to collect new developments and emerging technologies in the field of UAV control with reviews, regular research papers, communications, and short notes. We encourage submissions which provide the community with the most recent advancements in all aspects of guidance, navigation, and control, including but not limited to:

  • Modeling, simulation, and dynamic analysis of new and unconventional configurations;
  • Design of new sensors and novel estimation and data fusion algorithms;
  • Development and application of nano technologies;
  • Challenges of urban air mobility and delivery applications;
  • Space–air–ground integrated networks for complex transportation scenarios;
  • Vehicle design for optimal performance and minimum carbon footprint;
  • Artificial intelligence, machine learning, and onboard computing;
  • Transition from manned to unmanned vehicle configurations;
  • Swarming and collaborative tasks.

Dr. Emanuele Luigi de Angelis
Guest Editor

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

  • guidance, navigation, and control
  • sensor design and state estimation
  • nano technologies
  • urban air mobility and delivery
  • space–air–ground integrated network
  • drone design and development
  • artificial intelligence
  • manned-to-unmanned transition
  • swarming and collaborative tasks
  • onboard computing

Published Papers (4 papers)

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Research

22 pages, 13426 KiB  
Article
Efficient Motion Primitives-Based Trajectory Planning for UAVs in the Presence of Obstacles
by Marta Manzoni, Roberto Rubinacci and Davide Invernizzi
Drones 2024, 8(6), 256; https://doi.org/10.3390/drones8060256 - 12 Jun 2024
Viewed by 296
Abstract
The achievement of full autonomy in Unmanned Aerial Vehicles (UAVs) is significantly dependent on effective motion planning. Specifically, it is crucial to plan collision-free trajectories for smooth transitions from initial to final configurations. However, finding a solution executable by the actual system adds [...] Read more.
The achievement of full autonomy in Unmanned Aerial Vehicles (UAVs) is significantly dependent on effective motion planning. Specifically, it is crucial to plan collision-free trajectories for smooth transitions from initial to final configurations. However, finding a solution executable by the actual system adds complexity: the planned motion must be dynamically feasible. This involves meeting rigorous criteria, including vehicle dynamics, input constraints, and state constraints. This work addresses optimal kinodynamic motion planning for UAVs in the presence of obstacles by employing a hybrid technique instead of conventional search-based or direct trajectory optimization approaches. This technique involves precomputing a library of motion primitives by solving several Two-Point-Boundary-Value Problems (TPBVP) offline. This library is then repeatedly used online within a graph-search framework. Moreover, to make the method computationally tractable, continuity between consecutive motion primitives is enforced only on a subset of the state variables. This approach is compared with a state-of-the-art quadrotor-tailored search-based approach, which generates motion primitives online through control input discretization and forward propagation of the dynamic equations of a simplified model. The effectiveness of both methods is assessed through simulations and real-world experiments, demonstrating their ability to generate resolution-complete, resolution-optimal, collision-free, and dynamically feasible trajectories. Finally, a comparative analysis highlights the advantages, disadvantages, and optimal usage scenarios for each approach. Full article
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28 pages, 524 KiB  
Article
Advancing Drone Operations through Lightweight Blockchain and Fog Computing Integration: A Systematic Review
by Rawabi Aldossri, Ahmed Aljughaiman and Abdullah Albuali
Drones 2024, 8(4), 153; https://doi.org/10.3390/drones8040153 - 16 Apr 2024
Cited by 1 | Viewed by 1172
Abstract
This paper presents a systematic literature review investigating the integration of lightweight blockchain and fog computing technologies to enhance the security and operational efficiency of drones. With a focus on critical applications such as military surveillance and emergency response, this review examines how [...] Read more.
This paper presents a systematic literature review investigating the integration of lightweight blockchain and fog computing technologies to enhance the security and operational efficiency of drones. With a focus on critical applications such as military surveillance and emergency response, this review examines how the combination of blockchain’s secure, decentralized ledger and fog computing’s low-latency, localized data processing can address the unique challenges of drone operations. By compiling and analyzing current research, this study highlights innovative approaches and solutions that leverage these technologies to improve data integrity, reduce communication latency, and facilitate real-time decision-making in drone missions. Our findings underscore the significant potential of this technological integration to advance the capabilities and reliability of drones in high-stakes scenarios. Full article
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14 pages, 3119 KiB  
Article
Comparison of Multiple Models in Decentralized Target Estimation by a UAV Swarm
by Fausto Francesco Lizzio, Martin Bugaj, Ján Rostáš and Stefano Primatesta
Drones 2024, 8(1), 5; https://doi.org/10.3390/drones8010005 - 27 Dec 2023
Viewed by 1766
Abstract
The decentralized estimation and tracking of a mobile target performed by a group of unmanned aerial vehicles (UAVs) is studied in this work. A flocking protocol is used for maintaining a collision-free formation, while a decentralized extended Kalman filter in the information form [...] Read more.
The decentralized estimation and tracking of a mobile target performed by a group of unmanned aerial vehicles (UAVs) is studied in this work. A flocking protocol is used for maintaining a collision-free formation, while a decentralized extended Kalman filter in the information form is employed to provide an estimate of the target state. In the prediction step of the filter, we adopt and compare three different models for the target motion with increasing levels of complexity, namely, a constant velocity (CV), a constant turn (CT), and a full-state (FS) model. Software-in-the-loop (SITL) simulations are conducted in ROS/Gazebo to compare the performance of the three models. The coupling between the formation and estimation tasks is evaluated since the tracking task is affected by the outcome of the estimation process. Full article
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27 pages, 4535 KiB  
Article
An Improved Method for Swing State Estimation in Multirotor Slung Load Applications
by Emanuele Luigi de Angelis and Fabrizio Giulietti
Drones 2023, 7(11), 654; https://doi.org/10.3390/drones7110654 - 31 Oct 2023
Viewed by 1450
Abstract
A method is proposed to estimate the swing state of a suspended payload in multirotor drone delivery scenarios. Starting from the equations of motion of the coupled slung load system, defined by two point masses interconnected by a rigid link, a recursive algorithm [...] Read more.
A method is proposed to estimate the swing state of a suspended payload in multirotor drone delivery scenarios. Starting from the equations of motion of the coupled slung load system, defined by two point masses interconnected by a rigid link, a recursive algorithm is developed to estimate cable swing angle and rate from acceleration measurements available from an onboard Inertial Measurement Unit, without the need for extra sensors. The estimation problem is addressed according to the Extended Kalman Filter structure. With respect to the classical linear formulation, the proposed approach allows for improved estimation accuracy in both stationary and maneuvering flight. As an additional contribution, filter performance is enhanced by accounting for aerodynamic disturbance force, which largely affects the estimation accuracy in windy flight conditions. The validity of the proposed methodology is demonstrated as follows. First, it is applied to an octarotor platform where propellers are modeled according to blade element theory and the load is suspended by an elastic cable. Numerical simulations show that estimated swing angle and rate represent suitable feedback variables for payload stabilization, with benefits on flying qualities and energy demand. The algorithm is finally implemented on a small-scale quadrotor and is investigated through an outdoor experimental campaign, thus proving the effectiveness of the approach in a real application scenario. Full article
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