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Control and Applications of Intelligent Unmanned Aerial Vehicle

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

Deadline for manuscript submissions: closed (15 September 2024) | Viewed by 17273

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Special Issue Editors

Dr. Yunda Yan

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Guest Editor

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

Dr. Dewei Yi

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Guest Editor

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

Dr. Hao Lu

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Guest Editor

Hangzhou Innovation Institute, Beihang University, Hangzhou 310052, China
Interests: UAV flight control; anti-disturbance control; cooperative task allocation

Dr. Lan Gao

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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

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Published Papers (13 papers)

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15 pages, 946 KiB

Open AccessArticle

An Energy-Efficient Scheme Design for NOMA-Based UAV-Assisted MEC Systems

by Shanshan Wang and Zhiyong Luo

Electronics 2024, 13(21), 4240; https://doi.org/10.3390/electronics13214240 - 29 Oct 2024

Viewed by 521

Abstract

UAV-assisted MEC networks provide extensive communication coverage and massive computation services for mobile terminals (MTs), which are considered a promising edge paradigm to support future air–ground integrated communications. In this paper, an energy-efficient scheme in NOMA-based UAV-assisted MEC systems is proposed to address the system’s energy constraints and its inability to support massive MT access. Our goal is to minimize system-weighted energy consumption by jointly optimizing the allocation of transmission power, computation resources, and UAV trajectory scheduling. As the formulated problem is non-convex and difficult to solve directly, we decompose it into two manageable sub-problems and propose an iterative algorithm based on successive convex approximations (SCA) to solve each sub-problem alternatively. Simulation results show that the proposed joint optimization algorithm achieves a significant performance improvement compared to other benchmark approaches. Full article

(This article belongs to the Special Issue Control and Applications of Intelligent Unmanned Aerial Vehicle)

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17 pages, 11512 KiB

Open AccessArticle

A Multi-Model Architecture Based on Deep Learning for Longitudinal Available Overload Prediction of Commercial Subsonic Aircraft with Actuator Faults

by Shengqiang Shan, Yuehua Cheng, Bin Jiang, Cheng Xu, Kun Guo and Xingyu Lin

Electronics 2024, 13(18), 3723; https://doi.org/10.3390/electronics13183723 - 19 Sep 2024

Viewed by 557

Abstract

Assessing the real-time longitudinal available overload onboard under fault conditions offers vital insights for the fault-tolerant reconfiguration and trajectory planning of commercial subsonic aircraft. After actuator failures in a commercial subsonic aircraft, its aerodynamic model undergoes changes. Traditional methods based on analytical models rely on precise aerodynamic models. However, due to the complexities of the flight environment and uncertainties in disturbances, establishing an accurate aerodynamic model after actuator failures is often challenging. Consequently, traditional methods can yield significant errors when evaluating the available overload under actuator faults. To address this, we introduce a multi-model architecture based on deep learning for the longitudinal available overload prediction of a commercial subsonic aircraft with actuator faults. For flight state data under different working conditions and different faults, Spearman correlation coefficient analysis and the gradient boosting decision tree (GBDT) algorithm are used to remove redundant feature parameters, thereby enhancing the training and prediction speed of the model while reducing the risk of overfitting. To meet prediction accuracy and speed demands, we employ the multi-layer perceptron (MLP) deep learning network to fully explore the environmental features, including uncertainties and disturbances, within the flight state, and the mapping relationships between the flight state and the available overload variations. We incorporate the light gradient boosting machine (LightGBM) and the categorical boosting (CatBoost) algorithms to enhance the model’s prediction speed and fuse it with a longitudinal available overload analytical model to elevate the model’s prediction accuracy, thereby achieving the real-time estimation of the commercial subsonic aircraft’s longitudinal available overload with actuator faults. The results demonstrate that the proposed method achieves a higher accuracy than traditional methods, with a relative error of less than 5%. Full article

(This article belongs to the Special Issue Control and Applications of Intelligent Unmanned Aerial Vehicle)

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24 pages, 872 KiB

Open AccessArticle

Timed Automata-Based Strategy for Controlling Drone Access to Critical Zones: A UPPAAL Modeling Approach

by Moez Krichen

Electronics 2024, 13(13), 2609; https://doi.org/10.3390/electronics13132609 - 3 Jul 2024

Viewed by 694

Abstract

Controlling access to critical zones by drones is crucial for ensuring safety and efficient operations in various applications. In this research, we propose a strategy for controlling the access of a set of drones to a critical zone using timed automata and UPPAAL. UPPAAL is a model checker and simulator for real-time systems, which allows for the modeling, simulation, and verification of timed automata. Our system consists of six drones, a controller, and a buffer, all modeled as timed automata. We present a formal model capturing the behavior and interactions of these components, considering the constraints of allowing only one drone in the critical zone at a time. Timed automata are a powerful formalism for modeling and analyzing real-time systems, as they can capture the temporal aspects of system behavior. The advantages of using timed automata include the ability to model time-critical systems, analyze safety and liveness properties, and verify the correctness of the system. We design a strategy that involves signaling the approaching drones, preventing collisions, and ensuring orderly access to the critical zone. We utilize UPPAAL for simulating and verifying the system, including the evaluation of properties such as validation properties, safety properties, liveness properties, and absence of deadlocks. However, a limitation of timed automata is that they can become complex and difficult to model for large-scale systems, and the analysis can be computationally expensive as the number of components and behaviors increases. Through simulations and formal verification, we demonstrate the effectiveness and correctness of our proposed strategy. The results highlight the ability of timed automata and UPPAAL to provide reliable and rigorous analysis of drone access control systems. Our research contributes to the development of robust and safe strategies for managing drone operations in critical zones. Full article

(This article belongs to the Special Issue Control and Applications of Intelligent Unmanned Aerial Vehicle)

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21 pages, 773 KiB

Open AccessFeature PaperArticle

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 784

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 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

Open AccessArticle

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 3 | Viewed by 1373

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 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

Open AccessArticle

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 1193

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 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

Open AccessArticle

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 1142

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 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

Open AccessArticle

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 5 | Viewed by 1651

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 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

Open AccessArticle

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 4 | Viewed by 2236

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 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

Open AccessArticle

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 2150

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 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

Open AccessArticle

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 4 | Viewed by 1613

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 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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13 pages, 4726 KiB

Open AccessTechnical Note

Extremum Seeking-Based Radio Signal Strength Optimization Algorithm for Hoverable UAV Path Planning

by Sunghun Jung and Young-Joon Kim

Electronics 2024, 13(20), 4064; https://doi.org/10.3390/electronics13204064 - 16 Oct 2024

Viewed by 601

Abstract

For the safe autonomous operations of unmanned aerial vehicles (UAVs) and ground control stations (GCS), including autonomous battery replacement, wireless power transfer, and more, the precise landing of UAVs on GCS is essential. Accurate landing is only possible when the link capacity strength exceeds a certain threshold, but this is often disturbed due to complex terrain. To address this, we developed an extremum seeking (ES)-based radio signal strength optimization (RSSO) algorithm, ES-RSSO, designed to find the optimal positions of the UAV using radio communication signals. This ensures energy-efficient path planning while guaranteeing the minimum received signal strength indication (RSSI) capacity. This algorithm is particularly useful in obstacle-rich environments, where UAVs are limited in power resources. Simulation results demonstrate a 2.37% decrease in the mean, a 62.08% improvement in variance, and a 3.72% decrease in the integration strength of the link capacity when ES-RSSO is applied. These results confirm that the RADIO.rssi maintenance ability remains above a critical boundary level, supporting robust communication links and energy-efficient path planning. Throughout the study, we showed how, in many cases, simply moving the UAV a few meters can significantly improve the communication link. 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

Open AccessBrief 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

Cited by 1 | Viewed by 1175

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 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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