Control and Applications of Intelligent Unmanned Aerial Vehicle

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 11091

Special Issue Editors

Department of Computer Science, University College London, London WC1E 6BT, UK
Interests: model predictive control; reinforcement learning; aerial robotics; autonomous systems
Special Issues, Collections and Topics in MDPI journals
Department of Computing Science, University of Aberdeen, Aberdeen AB24 3FX, UK
Interests: aerial visual perception; UAV-based remote sensing; machine learning for UAV
Special Issues, Collections and Topics in MDPI journals
Hangzhou Innovation Institute, Beihang University, Hangzhou 310052, China
Interests: UAV flight control; anti-disturbance control; cooperative task allocation

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Guest Editor
Hangzhou Innovation Institute, Beihang University, Hangzhou 310052, China
Interests: consensus for multiple UAVs; control of networked UAV systems

Special Issue Information

Dear Colleagues,

Embedding intelligence into the control design of unmanned aerial vehicles (UAVs) would benefit them when operating in uncertain environments, and even facing unpredictable events. To guarantee the system safety under the intention of completing tasks, optimisation and machine learning methods are attracting increasing attention in the UAV community due to witnessing compelling development of machine learning techniques. In recent years, UAVs have achieved great success in many interdisciplinary applications, including remote sensing, precision agriculture, and offshore asset inspection, etc. With the growing mature technique of machine learning, many precocious challenges related to intelligent autonomous system development of UAVs may meet alternatively potential solutions and explanations.  

It is our pleasure to invite you to submit original research papers, short communications, or state-of-the-art reviews within the scope of this Special Issue. Contributions can range from pioneering trajectory/path planning and following with complex spatial-temporal missions, advanced control design of UAVs in adverse environments (such as winds) to practical UAV applications and modern intelligent guidance and control system design and implementation. Moreover, this Special Issue is also interested in the practical applications of UAVs, such as remote sensing, precision agriculture, asset inspection, and other UAV-based applications.

The list of possible topics for this Special Issue includes, but is not limited to:

  • UAV perception;
  • Intelligent control;
  • Optimisation-based flight control;
  • Disturbance estimation and rejection;
  • Cooperative control;
  • UAV-based remote sensing;
  • Asset inspection;
  • UAV applications of agriculture.

Dr. Yunda Yan
Dr. Dewei Yi
Dr. Hao Lu
Dr. Lan Gao
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. Electronics is an international peer-reviewed open access semimonthly 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

  • UAV perception
  • intelligent control
  • optimisation-based flight control
  • disturbance estimation and rejection
  • cooperative control UAV-based remote sensing
  • asset inspection
  • UAV applications of agriculture

Published Papers (9 papers)

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21 pages, 773 KiB  
Article
Robust Distributed Containment Control with Adaptive Performance and Collision Avoidance for Multi-Agent Systems
by Charalampos P. Bechlioulis
Electronics 2024, 13(8), 1439; https://doi.org/10.3390/electronics13081439 - 11 Apr 2024
Viewed by 495
Abstract
This paper deals with the containment control problem for multi-agent systems. The objective is to develop a distributed control scheme that leads a sub-group of the agents, called followers, within the convex hull that is formed by the leaders, which operate autonomously. Towards [...] Read more.
This paper deals with the containment control problem for multi-agent systems. The objective is to develop a distributed control scheme that leads a sub-group of the agents, called followers, within the convex hull that is formed by the leaders, which operate autonomously. Towards this direction, we propose a twofold approach comprising the following: (i) a cyber layer, where the agents establish, through the communication network, a consensus on a reference trajectory that converges exponentially fast within the convex hull of the leaders and (ii) a physical layer, where each agent tracks the aforementioned trajectory while avoiding collisions with other members of the multi-agent team. The main contributions of this work lie in the robustness of the proposed framework in both the trajectory estimation and the tracking control tasks, as well as the guaranteed collision avoidance, despite the presence of dynamic leaders and bounded but unstructured disturbances. A simulation study of a multi-agent system composed of five followers and four leaders demonstrates the applicability of the proposed scheme and verifies its robustness against both external disturbances that act on the follower model and the dynamic motion of the leaders. A comparison with a related work is also included to outline the strong properties of the proposed approach. Full article
(This article belongs to the Special Issue Control and Applications of Intelligent Unmanned Aerial Vehicle)
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17 pages, 12823 KiB  
Article
Towards Fully Autonomous UAV: Damaged Building-Opening Detection for Outdoor-Indoor Transition in Urban Search and Rescue
by Ali Surojaya, Ning Zhang, John Ray Bergado and Francesco Nex
Electronics 2024, 13(3), 558; https://doi.org/10.3390/electronics13030558 - 30 Jan 2024
Cited by 1 | Viewed by 900
Abstract
Autonomous unmanned aerial vehicle (UAV) technology is a promising technology for minimizing human involvement in dangerous activities like urban search and rescue missions (USAR), both in indoor and outdoor. Automated navigation from outdoor to indoor environments is not trivial, as it encompasses the [...] Read more.
Autonomous unmanned aerial vehicle (UAV) technology is a promising technology for minimizing human involvement in dangerous activities like urban search and rescue missions (USAR), both in indoor and outdoor. Automated navigation from outdoor to indoor environments is not trivial, as it encompasses the ability of a UAV to automatically map and locate the openings in a damaged building. This study focuses on developing a deep learning model for the detection of damaged building openings in real time. A novel damaged building-opening dataset containing images and mask annotations, as well as a comparison between single and multi-task learning-based detectors are given. The deep learning-based detector used in this study is based on YOLOv5. First, this study compared the different versions of YOLOv5 (i.e., small, medium, and large) capacity to perform damaged building-opening detections. Second, a multitask learning YOLOv5 was trained on the same dataset and compared with the single-task detector. The multitask learning (MTL) was developed based on the YOLOv5 object detection architecture, adding a segmentation branch jointly with the detection head. This study found that the MTL-based YOLOv5 can improve detection performance by combining detection and segmentation losses. The YOLOv5s-MTL trained on the damaged building-opening dataset obtained 0.648 mAP, an increase of 0.167 from the single-task-based network, while its inference speed was 73 frames per second on the tested platform. Full article
(This article belongs to the Special Issue Control and Applications of Intelligent Unmanned Aerial Vehicle)
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20 pages, 8609 KiB  
Article
Modeling and Accuracy Assessment of Determining the Coastline Course Using Geodetic, Photogrammetric and Satellite Measurement Methods: Case Study in Gdynia Beach in Poland
by Francesco Giuseppe Figliomeni, Mariusz Specht, Claudio Parente, Cezary Specht and Andrzej Stateczny
Electronics 2024, 13(2), 412; https://doi.org/10.3390/electronics13020412 - 19 Jan 2024
Viewed by 872
Abstract
The coastal environment represents a resource from both a natural and economic point of view, but it is subject to continuous transformations due to climate change, human activities, and natural risks. Remote sensing techniques have enormous potential in monitoring coastal areas. However, one [...] Read more.
The coastal environment represents a resource from both a natural and economic point of view, but it is subject to continuous transformations due to climate change, human activities, and natural risks. Remote sensing techniques have enormous potential in monitoring coastal areas. However, one of the main tasks is accurately identifying the boundary between waterbodies such as oceans, seas, lakes or rivers, and the land surface. The aim of this research is to evaluate the accuracy of coastline extraction using different datasets. The images used come from UAV-RGB and the Landsat-9 and Sentinel-2 satellites. The method applied for extracting the coast feature involves a first phase of application of the Normalized Difference Water Index (NDWI), only for satellite data, and consequent application of the maximum likelihood classification, with automatic vectorization. To carry out a direct comparison with the extracted data, a coastline obtained through a field survey using a Global Navigation Satellite System (GNSS) device was used. The results are very satisfactory as they meet the minimum requirements specified by the International Hydrographic Organization (IHO) S-44. Both the UAV and the Sentinel-2 reach the maximum order, called the Exclusive order (Total Horizontal Uncertainty (THU) of 5 m with a confidence level of 95%), while the Landsat-9 falls into the Special order (THU of 10 m with a confidence level of 95%). Full article
(This article belongs to the Special Issue Control and Applications of Intelligent Unmanned Aerial Vehicle)
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13 pages, 5780 KiB  
Article
Unmanned Aerial Vehicle-Based Automated Path Generation of Rollers for Smart Construction
by Hyung-Jin Kim, Jae-Yoon Kim, Ji-Woo Kim, Sung-Keun Kim and Wongi S. Na
Electronics 2024, 13(1), 138; https://doi.org/10.3390/electronics13010138 - 28 Dec 2023
Cited by 1 | Viewed by 853
Abstract
The construction industry is continuously evolving, seeking innovative solutions to enhance efficiency, reduce costs, and improve safety. Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as a transformative technology in the construction sector, offering numerous advantages in data collection and site [...] Read more.
The construction industry is continuously evolving, seeking innovative solutions to enhance efficiency, reduce costs, and improve safety. Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as a transformative technology in the construction sector, offering numerous advantages in data collection and site management. This paper presents a novel approach for utilizing UAVs to automate the path generation of rollers, a crucial element in the construction of roads and other large-scale infrastructure projects. A UAV was used to scan the target area to create a model; the next step was to generate the path for the rollers. Traditionally, the process of determining optimal roller paths is labor-intensive and reliant on manual surveys and engineering expertise. This study proposes a streamlined workflow that harnesses UAVs equipped with computer vision technology to capture high-resolution topographical data of construction sites. This data is then processed through an algorithm created by the authors that automatically generates optimized roller paths based on several factors. Full article
(This article belongs to the Special Issue Control and Applications of Intelligent Unmanned Aerial Vehicle)
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20 pages, 6918 KiB  
Article
Unmanned Aerial Vehicle Path-Planning Method Based on Improved P-RRT* Algorithm
by Xing Xu, Feifan Zhang and Yun Zhao
Electronics 2023, 12(22), 4576; https://doi.org/10.3390/electronics12224576 - 9 Nov 2023
Cited by 2 | Viewed by 1124
Abstract
This paper proposed an improved potential rapidly exploring random tree star (P-RRT*) algorithm for unmanned aerial vehicles (UAV). The algorithm has faster expansion and convergence speeds and better path quality. Path planning is an important part of the UAV control system. Rapidly exploring [...] Read more.
This paper proposed an improved potential rapidly exploring random tree star (P-RRT*) algorithm for unmanned aerial vehicles (UAV). The algorithm has faster expansion and convergence speeds and better path quality. Path planning is an important part of the UAV control system. Rapidly exploring random tree (RRT) is a path-planning algorithm that is widely used, including in UAV, and its altered body, P-RRT*, is an asymptotic optimal algorithm with bias sampling. The algorithm converges slowly and has a large random sampling area. To overcome the above drawbacks, we made the following improvements. First, the algorithm used the direction of the artificial potential field (APF) to determine whether to perform greedy expansion, increasing the search efficiency. Second, as the random tree obtained the initial path and updated the path cost, the algorithm rejected high-cost nodes and sampling points based on the heuristic cost and current path cost to speed up the convergence rate. Then, the random tree was pruned to remove the redundant nodes in the path. The simulation results demonstrated that the proposed algorithm could significantly decrease the path cost and inflection points, speed up initial path obtaining and convergence, and is suitable for the path planning of UAVs. Full article
(This article belongs to the Special Issue Control and Applications of Intelligent Unmanned Aerial Vehicle)
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14 pages, 2654 KiB  
Article
Vision-Based UAV Landing with Guaranteed Reliability in Adverse Environment
by Zijian Ge, Jingjing Jiang, Ewan Pugh, Ben Marshall, Yunda Yan and Liang Sun
Electronics 2023, 12(4), 967; https://doi.org/10.3390/electronics12040967 - 15 Feb 2023
Cited by 3 | Viewed by 1682
Abstract
Safe and accurate landing is crucial for Unmanned Aerial Vehicles (UAVs). However, it is a challenging task, especially when the altitude of the landing target is different from the ground and when the UAV is working in adverse environments, such as coasts where [...] Read more.
Safe and accurate landing is crucial for Unmanned Aerial Vehicles (UAVs). However, it is a challenging task, especially when the altitude of the landing target is different from the ground and when the UAV is working in adverse environments, such as coasts where winds are usually strong and changing rapidly. UAVs controlled by traditional landing algorithms are unable to deal with sudden large disturbances, such as gusts, during the landing process. In this paper, a reliable vision-based landing strategy is proposed for UAV autonomous landing on a multi-level platform mounted on an Unmanned Ground Vehicle (UGV). With the proposed landing strategy, visual detection can be retrieved even with strong gusts and the UAV is able to achieve robust landing accuracy in a challenging platform with complex ground effects. The effectiveness of the landing algorithm is verified through real-world flight tests. Experimental results in farm fields demonstrate the proposed method’s accuracy and robustness to external disturbances (e.g., wind gusts). Full article
(This article belongs to the Special Issue Control and Applications of Intelligent Unmanned Aerial Vehicle)
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18 pages, 4839 KiB  
Article
Air Defense Interception Plan Generation Method Based on Modified A* Optimization Algorithm
by Xiaocheng Song, Zhi Li, Shuting Feng, You Li, Liang Sun and Jingjing Jiang
Electronics 2023, 12(3), 719; https://doi.org/10.3390/electronics12030719 - 1 Feb 2023
Cited by 1 | Viewed by 1697
Abstract
Aiming at the air defense task requirements for an enemy’s large-scale aircraft attack, this paper presents a plan generation algorithm which can quickly give an interception scheme. The main contribution of this paper is the modification of the standard A* algorithm and its [...] Read more.
Aiming at the air defense task requirements for an enemy’s large-scale aircraft attack, this paper presents a plan generation algorithm which can quickly give an interception scheme. The main contribution of this paper is the modification of the standard A* algorithm and its combination of the optimization algorithm and air-defence mission. Firstly, the enemy’s attack weapon and our defense platform are modeled, and kinetic equations and interception efficiency functions are constructed, and the intercepted criterions are established. Then, the interception-cost mixed optimal function is established to clarify the system optimization objective. Secondly, aiming at the characteristics of strong time sensitivity of air defense interception, a modified A* optimization algorithm with fast convergence characteristics is used to solve the optimization problems, the standard A* algorithm is modified and the optimal air defense interception plan under the condition of mixed performance index is given. Finally, the proposed method is verified by numerical simulations. Full article
(This article belongs to the Special Issue Control and Applications of Intelligent Unmanned Aerial Vehicle)
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19 pages, 4473 KiB  
Article
Pruning Multi-Scale Multi-Branch Network for Small-Sample Hyperspectral Image Classification
by Yu Bai, Meng Xu, Lili Zhang and Yuxuan Liu
Electronics 2023, 12(3), 674; https://doi.org/10.3390/electronics12030674 - 29 Jan 2023
Cited by 3 | Viewed by 1324
Abstract
In recent years, the use of deep learning models has developed rapidly in the field of hyperspectral image (HSI) classification. However, most network models cannot make full use of the rich spatial-spectral features in hyperspectral images, being disadvantaged by their complex models and [...] Read more.
In recent years, the use of deep learning models has developed rapidly in the field of hyperspectral image (HSI) classification. However, most network models cannot make full use of the rich spatial-spectral features in hyperspectral images, being disadvantaged by their complex models and low classification accuracy for small-sample data. To address these problems, we present a lightweight multi-scale multi-branch hybrid convolutional network for small-sample classification. The network contains two new modules, a pruning multi-scale multi-branch block (PMSMBB) and a 3D-PMSMBB, each of which contains a multi-branch part and a pruning part. Each branch of the multi-branch part contains a convolutional kernel of different scales. In the training phase, the multi-branch part can extract rich feature information through different perceptual fields using the asymmetric convolution feature, which can effectively improve the classification accuracy of the model. To make the model lighter, pruning is introduced in the master branch of each multi-branch module, and the pruning part can remove the insignificant parameters without affecting the learning of the multi-branch part, achieving a light weight model. In the testing phase, the multi-branch part and the pruning part are jointly transformed into one convolution, without adding any extra parameters to the network. The study method was tested on three datasets: Indian Pines (IP), Pavia University (PU), and Salinas (SA). Compared with other advanced classification models, this pruning multi-scale multi-branch hybrid convolutional network (PMSMBN) had significant advantages in HSI small-sample classification. For instance, in the SA dataset with multiple crops, only 1% of the samples were selected for training, and the proposed method achieved an overall accuracy of 99.70%. Full article
(This article belongs to the Special Issue Control and Applications of Intelligent Unmanned Aerial Vehicle)
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17 pages, 5080 KiB  
Brief Report
Concept of an Innovative System for Dimensioning and Predicting Changes in the Coastal Zone Topography Using UAVs and USVs (4DBatMap System)
by Oktawia Specht, Mariusz Specht, Andrzej Stateczny and Cezary Specht
Electronics 2023, 12(19), 4112; https://doi.org/10.3390/electronics12194112 - 30 Sep 2023
Viewed by 905
Abstract
This publication is aimed at developing a concept of an innovative system for dimensioning and predicting changes in the coastal zone topography using Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs). The 4DBatMap system will consist of four components: 1. Measurement data [...] Read more.
This publication is aimed at developing a concept of an innovative system for dimensioning and predicting changes in the coastal zone topography using Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs). The 4DBatMap system will consist of four components: 1. Measurement data acquisition module. Bathymetric and photogrammetric measurements will be carried out with a specific frequency in the coastal zone using a UAV equipped with a Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS), Light Detection And Ranging (LiDAR) and a photogrammetric camera, as well as a USV equipped with a GNSS Real Time Kinematic (RTK) receiver and a MultiBeam EchoSounder (MBES). 2. Multi-sensor geospatial data fusion module. Low-altitude aerial imagery, hydrographic and LiDAR data acquired using UAVs and USVs will be integrated into one. The result will be an accurate and fully covered with measurements terrain of the coastal zone. 3. Module for predicting changes in the coastal zone topography. As part of this module, a computer application will be created, which, based on the analysis of a time series, will determine the optimal method for describing the spatial and temporal variability (long-term trend and seasonal fluctuations) of the coastal zone terrain. 4. Module for imaging changes in the coastal zone topography. The final result of the 4DBatMap system will be a 4D bathymetric chart to illustrate how the coastal zone topography changes over time. Full article
(This article belongs to the Special Issue Control and Applications of Intelligent Unmanned Aerial Vehicle)
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