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Drones
Special Issues
Optimal Design, Dynamics, and Navigation of Drones
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Optimal Design, Dynamics, and Navigation of Drones

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

Deadline for manuscript submissions: 27 September 2024 | Viewed by 5699

Special Issue Editors

Dr. Enrico Boni

E-Mail Website
Guest Editor
Department of Information Engineering, University of Florence, 50139 Florence, Italy
Interests: real-time electronics systems; drone architecture; high-performance computing platforms
Special Issues, Collections and Topics in MDPI journals
Dr. Michele Basso

E-Mail Website
Guest Editor
Department of Information Engineering, University of Florence, 50139 Florence, Italy
Interests: nonlinear control; real-time control systems; SPM microscopy; bifurcation theory

Special Issue Information

Dear Colleagues,

Drone technology is rapidly advancing, and researchers are exploring several new areas to improve drone design, dynamics, and navigation. One important research frontier is the development of optimal design algorithms that use mathematical optimization techniques to improve drone performance. These algorithms can help determine the drone specifications that maximize speed, battery life, payload capacity, and stability. Researchers are also exploring ways to improve the mathematical models of drone dynamics, which can help improve drone flight and navigation performance.

Another important area of research is autonomous navigation, where drones can autonomously navigate in complex environments. This requires the development of sophisticated artificial intelligence algorithms, which can enable drones to navigate safely and efficiently in areas, such as urban environments and congested airspaces. Additionally, advances in sensor technology, such as GNSS, LiDAR, and cameras, are enabling drones to perceive their environment and interact with it more effectively. Communication and coordination between drones are also essential for the operation of multi-drone systems, with the development of algorithms that can help drones work together seamlessly. This also includes advanced safety systems, such as obstacle detection and avoidance, which can help prevent drone accidents and collisions.

By exploring these new frontiers, research is driving innovation in drone technology, paving the way for new applications and use cases in a wide range of fields. These advancements have the potential to revolutionize industries, including agriculture, construction, and logistics, and provide new solutions to challenges of disaster response and environmental monitoring. As drone technology continues to evolve, there are still many exciting opportunities for further research and development.

This Special Issue aims to bring together the latest research in the field of drone technology, with a focus on the optimal design, dynamics, and navigation of drones. The papers included in this Issue will provide insights into the current state of the art and the future directions of drone technology.

Topics of interest include, but are not limited to, the following:

  • Novel drone designs and architectures
  • Aerodynamic modeling and optimization
  • Flight control and stability
  • Autonomous navigation and path planning
  • Sensor fusion and perception

Applications in various fields, such as agriculture, transportation, surveillance, and search and rescue.

Dr. Enrico Boni
Dr. Michele Basso
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.

Keywords

  • optimal design
  • dynamics
  • drones
  • flight control
  • stability
  • path planning
  • sensor fusion
  • perception
  • autonomous navigation

Published Papers (4 papers)

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Research

18 pages, 4298 KiB  
Article
Large-Sized Multirotor Design: Accurate Modeling with Aerodynamics and Optimization for Rotor Tilt Angle
by Anhuan Xie, Xufei Yan, Weisheng Liang, Shiqiang Zhu and Zheng Chen
Drones 2023, 7(10), 614; https://doi.org/10.3390/drones7100614 - 29 Sep 2023
Cited by 1 | Viewed by 1323
Abstract
Advancements in aerial mobility (AAM) are driven by needs in transportation, logistics, rescue, and disaster relief. Consequently, large-sized multirotor unmanned aerial vehicles (UAVs) with strong power and ample space show great potential. In order to optimize the design process for large-sized multirotors and [...] Read more.
Advancements in aerial mobility (AAM) are driven by needs in transportation, logistics, rescue, and disaster relief. Consequently, large-sized multirotor unmanned aerial vehicles (UAVs) with strong power and ample space show great potential. In order to optimize the design process for large-sized multirotors and reduce physical trial and error, a detailed dynamic model is firstly established, with an accurate aerodynamic model. In addition, the center of gravity (CoG) offset and actuator dynamics are also well considered, which are usually ignored in small-sized multirotors. To improve the endurance and maneuverability of large-sized multirotors, which is the key concern in real applications, a two-loop optimization method for rotor tilt angle design is proposed based on the mathematical model established previously. Its inner loop solves the dynamic equilibrium points to relax the complex dynamic constraints caused by aerodynamics in the overall optimization problem, which improves the solution efficiency. The ideal design results can be obtained through the offline process, which greatly reduces the difficulties of physical trial and error. Finally, various experiments are carried out to demonstrate the accuracy of the established model and the effectiveness of the optimization method. Full article
(This article belongs to the Special Issue Optimal Design, Dynamics, and Navigation of Drones)
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15 pages, 14652 KiB  
Article
Efficient Focus Autoencoders for Fast Autonomous Flight in Intricate Wild Scenarios
by Kaiyu Hu, Huanlin Li, Jiafan Zhuang, Zhifeng Hao and Zhun Fan
Drones 2023, 7(10), 609; https://doi.org/10.3390/drones7100609 - 27 Sep 2023
Cited by 1 | Viewed by 1052
Abstract
The autonomous navigation of aerial robots in unknown and complex outdoor environments is a challenging problem that typically requires planners to generate collision-free trajectories based on human expert rules for fast navigation. Presently, aerial robots suffer from high latency in acquiring environmental information, [...] Read more.
The autonomous navigation of aerial robots in unknown and complex outdoor environments is a challenging problem that typically requires planners to generate collision-free trajectories based on human expert rules for fast navigation. Presently, aerial robots suffer from high latency in acquiring environmental information, which limits the control strategies that the vehicle can implement. In this study, we proposed the SAC_FAE algorithm for high-speed navigation in complex environments using deep reinforcement learning (DRL) policies. Our approach consisted of a soft actor–critic (SAC) algorithm and a focus autoencoder (FAE). Our end-to-end DRL navigation policy enabled a flying robot to efficiently accomplish navigation tasks without prior map information by relying solely on the front-end depth frames and its own pose information. The proposed algorithm outperformed existing trajectory-based optimization approaches at flight speeds exceeding 3 m/s in multiple testing environments, which demonstrates its effectiveness and efficiency. Full article
(This article belongs to the Special Issue Optimal Design, Dynamics, and Navigation of Drones)
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19 pages, 4385 KiB  
Article
Dynamic Analysis and Experiment of Multiple Variable Sweep Wings on a Tandem-Wing MAV
by Liang Gao, Yanhe Zhu, Xizhe Zang, Junming Zhang, Boyang Chen, Liyi Li and Jie Zhao
Drones 2023, 7(9), 552; https://doi.org/10.3390/drones7090552 - 26 Aug 2023
Viewed by 1527
Abstract
The current morphing technologies are mostly regarded as auxiliary tools, providing additional control torques to enhance the flight maneuverability of unmanned aerial vehicles (UAVs), and they cannot exist independently of the traditional control surfaces. In this paper, we propose a tandem-wing micro aerial [...] Read more.
The current morphing technologies are mostly regarded as auxiliary tools, providing additional control torques to enhance the flight maneuverability of unmanned aerial vehicles (UAVs), and they cannot exist independently of the traditional control surfaces. In this paper, we propose a tandem-wing micro aerial vehicle (MAV) with multiple variable-sweep wings, which can reduce the additional inertia forces and moments and weaken the dynamic coupling between longitudinal and lateral motion while the MAV morphs symmetrically for pitch control or asymmetrically for roll control, thereby flying without the traditional aileron and elevator. First, load experiments were conducted on the MAV to verify the structural strength of the multiple variable sweep wings, and the control moments caused by the morphing of the MAV were presented through numerical simulations. Then, the effects caused by symmetric and asymmetric morphing were investigated via dynamic response simulations based on the Kane dynamic model of the MAV, and the generated additional inertia forces and moments were also analyzed during morphing. Finally, dynamic response experiments and open-loop flight experiments were conducted. The experimental results demonstrated that the morphing mode in this study could weaken the coupling between the longitudinal and lateral dynamics and that it was feasible for attitude control without the traditional aileron and elevator while flying. Full article
(This article belongs to the Special Issue Optimal Design, Dynamics, and Navigation of Drones)
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25 pages, 33023 KiB  
Article
Trust–Region Nonlinear Optimization Algorithm for Orientation Estimator and Visual Measurement of Inertial–Magnetic Sensor
by Nan Jia, Zongkang Wei and Bangyu Li
Drones 2023, 7(6), 351; https://doi.org/10.3390/drones7060351 - 27 May 2023
Viewed by 1012
Abstract
This paper proposes a novel robust orientation estimator to enhance the accuracy and robustness of orientation estimation for inertial–magnetic sensors of the small consumer–grade drones. The proposed estimator utilizes a trust–region strategy within a nonlinear optimization framework, transforming the orientation fusion problem into [...] Read more.
This paper proposes a novel robust orientation estimator to enhance the accuracy and robustness of orientation estimation for inertial–magnetic sensors of the small consumer–grade drones. The proposed estimator utilizes a trust–region strategy within a nonlinear optimization framework, transforming the orientation fusion problem into a nonlinear optimization problem based on the maximum likelihood principle. The proposed estimator employs a trust–region Dogleg gradient descent strategy to optimize orientation precision and incorporates a Huber robust kernel to minimize interference caused by acceleration during the maneuvering process of the drone. In addition, a novel method for evaluating the performance of orientation estimators is also presented based on visuals. The proposed method consists of two parts: offline calibration of the basic cube using Augmented Reality University of Cordoba (ArUco) markers and online orientation measurement of the sensor carrier using a nonlinear optimization solver. The proposed measurement method’s accuracy and the proposed estimator’s performance are evaluated under low–dynamic (rotation) and high–dynamic (shake) conditions in the experiment. The experimental findings indicate that the proposed measurement method obtains an average re–projection error of less than 0.1 pixels. The proposed estimator has the lowest average orientation error compared to conventional orientation estimation algorithms. Despite the time–consuming nature of the proposed estimator, it exhibits greater robustness and precision, particularly in highly dynamic environments. Full article
(This article belongs to the Special Issue Optimal Design, Dynamics, and Navigation of Drones)
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