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AI-Assisted Control Strategies and Their Applications to the Stabilization, Guidance...
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AI-Assisted Control Strategies and Their Applications to the Stabilization, Guidance 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: 31 December 2024 | Viewed by 5685

Special Issue Editor

Dr. Agostino De Marco

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Naples Federico II, Napoli, Italy
Interests: flight dynamics modeling; aircraft stability and control; nonlinear aerodynamics modeling; aircraft design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Drones, also known as unmanned aerial vehicles (UAVs), have seen a significant surge in interest over the past decade. This is due to their potential for a wide range of applications, from commercial delivery and aerial photography to more complex tasks such as disaster management, environmental monitoring and military operations. The design, dynamics and navigation of drones are critical aspects that determine their performance, efficiency and applicability in these diverse scenarios.

The design of drones involves a multitude of factors, including their structure, power systems and onboard sensors. Optimizing these elements can enhance the drone's efficiency, durability and functionality, enabling it to perform better and withstand various operational conditions. The dynamics of drones, which involve their movement patterns, stability and control systems, are also crucial. Understanding and improving these dynamics can lead to more precise and reliable drone operations.

Navigation is another key aspect of drone technology. It involves the guidance, control and coordination mechanisms that allow drones to move from one location to another, avoid obstacles and perform their tasks. Improving navigation systems can enhance the autonomy of drones, enabling them to operate in more complex and unpredictable environments.

In recent years, machine learning (ML) has emerged as a powerful tool for advancing drone technology. ML techniques can be used to optimize drone design, model their dynamics and improve their navigation systems. For instance, ML algorithms can learn from data to predict optimal design parameters, understand complex dynamics and navigate in unknown environments. They can also help improve drone operations, such as object detection, tracking and collision avoidance.

This Special Issue aims to explore these topics and highlight the latest advancements in the field. It provides a platform for researchers, engineers and practitioners to share their findings, discuss challenges and foster collaborations. The issue underscores the importance of this research area, given the growing role of drones in our society and the potential of ML to revolutionize drone technology.

Aim:

Furthermore, it aims to bring together innovative research and developments in the field of drone technology, seeking to highlight advancements in the design, dynamics and navigation of drones, with a particular focus on the integration of machine learning (ML) techniques. The goal is to foster discussions and collaborations among researchers, engineers and practitioners working on drone technology and its applications.

Scope:

This Special Issue will cover a broad range of topics related to drone technology. It will focus on the optimal design of drones, exploring how to enhance their efficiency, durability and functionality. The issue will delve into the dynamics of drones, examining their movement patterns, stability and control systems. It will also cover the navigation of drones, including their guidance, control and coordination mechanisms.

In line with recent trends, the Special Issue will emphasize the application of machine learning in drone technology. This includes ML-based design optimization, ML-driven dynamic modeling and ML-aided navigation systems. The issue welcomes studies on the use of ML techniques for improving drone operations, such as object detection, tracking and collision avoidance, as well as for enhancing their autonomous capabilities.

Suggested themes and article types for submissions:

  1. Optimal Design of Drones: Articles focusing on the latest advancements in the design of drones, including structural design, power systems and onboard sensors.
  2. Dynamics of Drones: Submissions that delve into the movement patterns, stability and control systems of drones.
  3. Navigation Systems for Drones: Papers that explore the guidance, control and coordination mechanisms that allow drones to move from one location to another, avoid obstacles and perform their tasks.
  4. Machine Learning in Drone Technology: Articles that highlight the use of ML techniques in drone design, dynamics and navigation. This could include ML-based design optimization, ML-driven dynamic modeling and ML-aided navigation systems.
  5. Applications of Drones: Submissions that discuss the diverse applications of drones, from commercial delivery and aerial photography to disaster management, environmental monitoring and military operations.

Article Types for Submissions:

  1. Research Articles: Original research papers that present new findings in the field of drone design, dynamics, navigation and the application of ML in these areas.
  2. Review Articles: Comprehensive reviews that summarize the current state of research in the field, identify gaps, and suggest directions for future research.

Dr. Agostino De Marco
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

  • drone technology
  • optimal design
  • drone dynamics
  • drone navigation
  • machine learning
  • design optimization
  • dynamic modeling
  • navigation systems
  • autonomous drones
  • object detection
  • collision avoidance
  • tracking systems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

36 pages, 17153 KiB  
Article
YOLO-RWY: A Novel Runway Detection Model for Vision-Based Autonomous Landing of Fixed-Wing Unmanned Aerial Vehicles
by Ye Li, Yu Xia, Guangji Zheng, Xiaoyang Guo and Qingfeng Li
Drones 2024, 8(10), 571; https://doi.org/10.3390/drones8100571 - 10 Oct 2024
Viewed by 1180
Abstract
In scenarios where global navigation satellite systems (GNSSs) and radio navigation systems are denied, vision-based autonomous landing (VAL) for fixed-wing unmanned aerial vehicles (UAVs) becomes essential. Accurate and real-time runway detection in VAL is vital for providing precise positional and orientational guidance. However, [...] Read more.
In scenarios where global navigation satellite systems (GNSSs) and radio navigation systems are denied, vision-based autonomous landing (VAL) for fixed-wing unmanned aerial vehicles (UAVs) becomes essential. Accurate and real-time runway detection in VAL is vital for providing precise positional and orientational guidance. However, existing research faces significant challenges, including insufficient accuracy, inadequate real-time performance, poor robustness, and high susceptibility to disturbances. To address these challenges, this paper introduces a novel single-stage, anchor-free, and decoupled vision-based runway detection framework, referred to as YOLO-RWY. First, an enhanced data augmentation (EDA) module is incorporated to perform various augmentations, enriching image diversity, and introducing perturbations that improve generalization and safety. Second, a large separable kernel attention (LSKA) module is integrated into the backbone structure to provide a lightweight attention mechanism with a broad receptive field, enhancing feature representation. Third, the neck structure is reorganized as a bidirectional feature pyramid network (BiFPN) module with skip connections and attention allocation, enabling efficient multi-scale and across-stage feature fusion. Finally, the regression loss and task-aligned learning (TAL) assigner are optimized using efficient intersection over union (EIoU) to improve localization evaluation, resulting in faster and more accurate convergence. Comprehensive experiments demonstrate that YOLO-RWY achieves AP50:95 scores of 0.760, 0.611, and 0.413 on synthetic, real nominal, and real edge test sets of the landing approach runway detection (LARD) dataset, respectively. Deployment experiments on an edge device show that YOLO-RWY achieves an inference speed of 154.4 FPS under FP32 quantization with an image size of 640. The results indicate that the proposed YOLO-RWY model possesses strong generalization and real-time capabilities, enabling accurate runway detection in complex and challenging visual environments, and providing support for the onboard VAL systems of fixed-wing UAVs. Full article
(This article belongs to the Special Issue AI-Assisted Control Strategies and Their Applications to the Stabilization, Guidance and Navigation of Drones)
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24 pages, 7029 KiB  
Article
Multi-UAV Cooperative Pursuit of a Fast-Moving Target UAV Based on the GM-TD3 Algorithm
by Yaozhong Zhang, Meiyan Ding, Yao Yuan, Jiandong Zhang, Qiming Yang, Guoqing Shi, Frank Jiang and Meiqu Lu
Drones 2024, 8(10), 557; https://doi.org/10.3390/drones8100557 - 8 Oct 2024
Viewed by 673
Abstract
Recently, developing multi-UAVs to cooperatively pursue a fast-moving target has become a research hotspot in the current world. Although deep reinforcement learning (DRL) has made a lot of achievements in the UAV pursuit game, there are still some problems such as high-dimensional parameter [...] Read more.
Recently, developing multi-UAVs to cooperatively pursue a fast-moving target has become a research hotspot in the current world. Although deep reinforcement learning (DRL) has made a lot of achievements in the UAV pursuit game, there are still some problems such as high-dimensional parameter space, the ease of falling into local optimization, the long training time, and the low task success rate. To solve the above-mentioned issues, we propose an improved twin delayed deep deterministic policy gradient algorithm combining the genetic algorithm and maximum mean discrepancy method (GM-TD3) for multi-UAV cooperative pursuit of high-speed targets. Firstly, this paper combines GA-based evolutionary strategies with TD3 to generate action networks. Then, in order to avoid local optimization in the algorithm training process, the maximum mean difference (MMD) method is used to increase the diversity of the policy population in the updating process of the population parameters. Finally, by setting the sensitivity weights of the genetic memory buffer of UAV individuals, the mutation operator is improved to enhance the stability of the algorithm. In addition, this paper designs a hybrid reward function to accelerate the convergence speed of training. Through simulation experiments, we have verified that the training efficiency of the improved algorithm has been greatly improved, which can achieve faster convergence; the successful rate of the task has reached 95%, and further validated UAVs can better cooperate to complete the pursuit game task. Full article
(This article belongs to the Special Issue AI-Assisted Control Strategies and Their Applications to the Stabilization, Guidance and Navigation of Drones)
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19 pages, 16864 KiB  
Article
Hovering of Bi-Directional Motor Driven Flapping Wing Micro Aerial Vehicle Based on Deep Reinforcement Learning
by Haitian Hu, Zhiyuan Zhang, Zhaoguo Wang and Xuan Wang
Drones 2024, 8(9), 508; https://doi.org/10.3390/drones8090508 - 20 Sep 2024
Viewed by 762
Abstract
Inspired by hummingbirds and certain insects, flapping wing micro aerial vehicles (FWMAVs) exhibit potential energy efficiency and maneuverability advantages. Among them, the bi-directional motor-driven tailless FWMAV with simple structure prevails in research, but it requires active pose control for hovering. In this paper, [...] Read more.
Inspired by hummingbirds and certain insects, flapping wing micro aerial vehicles (FWMAVs) exhibit potential energy efficiency and maneuverability advantages. Among them, the bi-directional motor-driven tailless FWMAV with simple structure prevails in research, but it requires active pose control for hovering. In this paper, we employ deep reinforcement learning to train a low-level hovering strategy that directly maps the drone’s state to motor voltage outputs. To our knowledge, other FWMAVs in both reality and simulations still rely on classical proportional-derivative controllers for pose control. Our learning-based approach enhances strategy robustness through domain randomization, eliminating the need for manually fine-tuning gain parameters. The effectiveness of the strategy is validated in a high-fidelity simulation environment, showing that for an FWMAV with a wingspan of approximately 200 mm, the center of mass is maintained within a 20 mm radius during hovering. Furthermore, the strategy is utilized to demonstrate point-to-point flight, trajectory tracking, and controlled flight of multiple drones. Full article
(This article belongs to the Special Issue AI-Assisted Control Strategies and Their Applications to the Stabilization, Guidance and Navigation of Drones)
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18 pages, 7322 KiB  
Article
Aerial Map-Based Navigation by Ground Object Pattern Matching
by Youngjoo Kim, Seungho Back, Dongchan Song and Byung-Yoon Lee
Drones 2024, 8(8), 375; https://doi.org/10.3390/drones8080375 - 5 Aug 2024
Viewed by 1132
Abstract
This paper proposes a novel approach to map-based navigation for unmanned aircraft. The proposed approach employs pattern matching of ground objects, not feature-to-feature or image-to-image matching, between an aerial image and a map database. Deep learning-based object detection converts the ground objects into [...] Read more.
This paper proposes a novel approach to map-based navigation for unmanned aircraft. The proposed approach employs pattern matching of ground objects, not feature-to-feature or image-to-image matching, between an aerial image and a map database. Deep learning-based object detection converts the ground objects into labeled points, and the objects’ configuration is used to find the corresponding location in the map database. Using the deep learning technique as a tool for extracting high-level features reduces the image-based localization problem to a pattern-matching problem. The pattern-matching algorithm proposed in this paper does not require altitude information or a camera model to estimate the horizontal geographical coordinates of the vehicle. Moreover, it requires significantly less storage because the map database is represented as a set of tuples, each consisting of a label, latitude, and longitude. Probabilistic data fusion with the inertial measurements by the Kalman filter is incorporated to deliver a comprehensive navigational solution. Flight experiments demonstrate the effectiveness of the proposed system in real-world environments. The map-based navigation system successfully provides the position estimates with RMSEs within 3.5 m at heights over 90 m without the aid of the GNSS. Full article
(This article belongs to the Special Issue AI-Assisted Control Strategies and Their Applications to the Stabilization, Guidance and Navigation of Drones)
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18 pages, 7743 KiB  
Article
Design of a UAV Trajectory Prediction System Based on Multi-Flight Modes
by Zhuoyong Shi, Jiandong Zhang, Guoqing Shi, Longmeng Ji, Dinghan Wang and Yong Wu
Drones 2024, 8(6), 255; https://doi.org/10.3390/drones8060255 - 10 Jun 2024
Cited by 5 | Viewed by 1164
Abstract
With the burgeoning impact of artificial intelligence on the traditional UAV industry, the pursuit of autonomous UAV flight has emerged as a focal point of contemporary research. Addressing the imperative for advancing critical technologies in autonomous flight, this paper delves into the realm [...] Read more.
With the burgeoning impact of artificial intelligence on the traditional UAV industry, the pursuit of autonomous UAV flight has emerged as a focal point of contemporary research. Addressing the imperative for advancing critical technologies in autonomous flight, this paper delves into the realm of UAV flight state recognition and trajectory prediction. Presenting an innovative approach focused on improving the precision of unmanned aerial vehicle (UAV) path forecasting via the identification of flight states, this study demonstrates its efficacy through the implementation of two prediction models. Firstly, UAV flight data acquisition was realized in this paper by the use of multi-sensors. Finally, two models for UAV trajectory prediction were designed based on machine learning methods and classical mathematical prediction methods, respectively, and the results before and after flight pattern recognition are compared. The experimental results show that the prediction error of the UAV trajectory prediction method based on multiple flight modes is smaller than the traditional trajectory prediction method in different flight stages. Full article
(This article belongs to the Special Issue AI-Assisted Control Strategies and Their Applications to the Stabilization, Guidance and Navigation of Drones)
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