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Special Issue "Unmanned Aerial Vehicle Control, Networks, System and Application"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (15 March 2021) | Viewed by 25888

Special Issue Editors

Prof. Dr. Sung Kyung Hong
E-Mail Website
Guest Editor
School of Mechanical and Aerospace Engineering, Sejong University, Neungdong-ro, Gwangjin-gu, Seoul 143-747(05006), Korea
Interests: flight control system; IMU application; UAV; multicopter drone
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Le Nhu Ngoc Thanh, Ha
E-Mail
Guest Editor
School of Intelligent Mechatronics Engineering, Sejong University Neungdong-ro, Gwangjin-gu, Seoul 143-747(05006), Korea
Interests: nonlinear control; disturbance observer-based control; collision avoidance control design for unmanned vehicles; UAV control system; mechatronics and robotics

Special Issue Information

Dear Colleagues,

In recent years, unmanned aerial vehicles (UAVs) have been used in a variety of applications. The majority of conventional guidance, navigation, and control methods are based on sensing to obtain the vehicle status and surrounding environment information through a signal processing technique. Recently, along with the rapid development of computer science, machine learning, electronics, control theories, and, particularly, artificial intelligence techniques, UAVs are becoming more and more maneuverable and smarter. Therefore, the applications of this technology are also expanding. To contribute to this expansion, a fully autonomous UAV must integrate many intelligent abilities such as environment recognition, collision prediction, tracking control, fault diagnosis and failure control, etc., and, particularly, the capacity to connect to other vehicles (multiple UAVs, cars and UAVs, ships and UAVs, etc.) to complete specific missions through various communication techniques (MALLink, ROS, wireless networks, individual vehicle networks, etc.). This Special Issue will develop and apply advanced technologies for smart UAV based on highlighted inventions or current challenging applications. We welcome original, state-of-the-art studies in the areas that contribute to academia and industry. The Special Issue will cover but is not limited to the following:

  • Autonomous control for unmanned aerial vehicles, multicopter drones
  • Artificial intelligence in collision prediction and tracking control
  • Deep learning-based sensor fusion and environment detection
  • Artificial intelligence-based fault diagnosis and failure control
  • Disturbance observer and robust control for multicopter UAVs
Prof. Dr. Sung Kyung Hong
Dr. Le Nhu Ngoc Thanh, Ha
Guest Editor

Manuscript Submission Information

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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

  • Autonomous UAVs
  • Collision prediction and control
  • Environment detection and tracking control
  • Disturbance observer and robust control
  • Fault diagnosis and failure control
  • Smart UAVs
  • UAV-vehicles networks and applications
  • Hardware in the loop simulation and UAVs networks

Published Papers (17 papers)

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Research

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Article
Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle
Sensors 2021, 21(8), 2848; https://doi.org/10.3390/s21082848 - 18 Apr 2021
Cited by 7 | Viewed by 1395
Abstract
Conventional and license-free radio-controlled drone activities are limited to a line-of-sight (LoS) operational range. One of the alternatives to operate the drones beyond the visual line-of-sight (BVLoS) range is replacing the drone wireless communications system from the conventional industrial, scientific, and medical (ISM) [...] Read more.
Conventional and license-free radio-controlled drone activities are limited to a line-of-sight (LoS) operational range. One of the alternatives to operate the drones beyond the visual line-of-sight (BVLoS) range is replacing the drone wireless communications system from the conventional industrial, scientific, and medical (ISM) radio band to a licensed cellular-connected system. The Long Term Evolution (LTE) technology that has been established for the terrestrial area allows command-and-control and payload communications between drone and ground station in real-time. However, with increasing height above the ground, the radio environment changes, and utilizing terrestrial cellular networks for drone communications may face new challenges. In this regard, this paper aims to develop an LTE-based control system prototype for low altitude small drones and investigate the feasibility and performance of drone cellular connectivity at different altitudes with measuring parameters such as latency, handover, and signal strength. The measurement results have shown that by increasing flight height from ground to 170 m the received signal power and the signal quality levels were reduced by 20 dBm and 10 dB respectively, the downlink data rate decreased to 70%, and latency increased up to 94 ms. It is concluded that although the existing LTE network can provide a minimum requirement for drone cellular connectivity, further improvements are still needed to enhance aerial coverage, eliminate interference, and reduce network latency. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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Article
Ground Speed Optical Estimator for Miniature UAV
Sensors 2021, 21(8), 2754; https://doi.org/10.3390/s21082754 - 13 Apr 2021
Cited by 3 | Viewed by 936
Abstract
In a conventional Unmanned aerial vehicles (UAV) navigational system Global Navigation Satellite System (GNSS) sensor is often a main source of data for trajectory generation. Even video tracking based systems need some GNSS data for proper work. The goal of this study is [...] Read more.
In a conventional Unmanned aerial vehicles (UAV) navigational system Global Navigation Satellite System (GNSS) sensor is often a main source of data for trajectory generation. Even video tracking based systems need some GNSS data for proper work. The goal of this study is to develop an optics-based system to estimate the ground speed of the UAV in the case of the GNSS failure, jamming, or unavailability. The proposed approach uses a camera mounted on the fuselage belly of the UAV. We can obtain the ground speed of the airplane by using the digital cropping, the stabilization of the real time image, and template matching algorithms. By combining the ground speed vector components with measurements of airspeed and altitude, the wind velocity and drift are computed. The obtained data were used to improve efficiency of the video-tracking based on a navigational system. An algorithm allows this computation to be performed in real time on board of a UAV. The algorithm was tested in Software-in-the-loop and implemented on the UAV hardware. Its effectiveness has been demonstrated through the experimental test results. The presented work could be useful for upgrading the existing MUAV products (with embedded cameras) already delivered to the customers only by updating their software. It is especially significant in the case when any necessary hardware upgrades would be economically unjustified or even impossible to be carried out. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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Article
Autonomous Mission of Multi-UAV for Optimal Area Coverage
Sensors 2021, 21(7), 2482; https://doi.org/10.3390/s21072482 - 02 Apr 2021
Cited by 2 | Viewed by 1039
Abstract
This study proposes an entire hardware and software architecture from operator input to motor command for the autonomous area coverage mission using multiple unmanned aerial vehicles. Despite the rapid growth of commercial drone services, there are many limitations on operations, such as a [...] Read more.
This study proposes an entire hardware and software architecture from operator input to motor command for the autonomous area coverage mission using multiple unmanned aerial vehicles. Despite the rapid growth of commercial drone services, there are many limitations on operations, such as a low decision-making autonomy and the need for experienced operators to intervene in the whole process. For performing the area coverage mission more efficiently and autonomously, this study newly designs an optimization problem that allocates waypoints created to cover that area to unmanned aerial vehicles. With an optimized list of waypoints, unmanned aerial vehicles can fill the given areas with their footprints in a minimal amount of time and do not overlap each other during the mission. In addition, this study performs both various simulations for quantitative analysis and an outdoor experiment through real hardware implementation in order to verify the performance sufficiently. The methodologies developed in this study could be applied to endless applications using unmanned aerial vehicles equipped with mission-specific sensors. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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Article
Bézier Curves-Based Optimal Trajectory Design for Multirotor UAVs with Any-Angle Pathfinding Algorithms
Sensors 2021, 21(7), 2460; https://doi.org/10.3390/s21072460 - 02 Apr 2021
Cited by 5 | Viewed by 1173
Abstract
Multirotor Unmanned Aerial Vehicles (UAVs) play an imperative role in many real-world applications in a variety of scenarios characterized by a high density of obstacles with different heights. Due to the complicated operation areas of UAVs and complex constraints associated with the assigned [...] Read more.
Multirotor Unmanned Aerial Vehicles (UAVs) play an imperative role in many real-world applications in a variety of scenarios characterized by a high density of obstacles with different heights. Due to the complicated operation areas of UAVs and complex constraints associated with the assigned mission, there should be a suitable path to fly. Therefore, the most relevant challenge is how to plan a flyable path for a UAV without collisions with obstacles. This paper demonstrates how a flyable and continuous trajectory was constructed by using any-angle pathfinding algorithms, which are Basic Theta*, Lazy Theta*, and Phi* algorithms for a multirotor UAV in a cluttered environment. The three algorithms were modified by adopting a modified cost function during their implementation that considers the elevation of nodes. First, suitable paths are generated by using a modified version of the three algorithms. After that, four Bézier curves-based approaches are proposed to smooth the generated paths to be converted to flyable paths (trajectories). To determine the most suitable approach, particularly when searching for an optimal and collision-free trajectory design, an innovative evaluation process is proposed and applied in a variety of different size environments. The evaluation process results show high success rates of the four approaches; however, the approach with the highest success rate is adopted. Finally, based on the results of the evaluation process, a novel algorithm is proposed to increase the efficiency of the selected approach to the optimality in the construction process of the trajectory. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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Article
Adaptive Robust Trajectory Tracking Control of Multiple Quad-Rotor UAVs with Parametric Uncertainties and Disturbances
Sensors 2021, 21(7), 2401; https://doi.org/10.3390/s21072401 - 31 Mar 2021
Cited by 9 | Viewed by 1273
Abstract
Recently, formation flying of multiple unmanned aerial vehicles (UAVs) found numerous applications in various areas such as surveillance, industrial automation and disaster management. The accuracy and reliability for performing group tasks by multiple UAVs is highly dependent on the applied control strategy. The [...] Read more.
Recently, formation flying of multiple unmanned aerial vehicles (UAVs) found numerous applications in various areas such as surveillance, industrial automation and disaster management. The accuracy and reliability for performing group tasks by multiple UAVs is highly dependent on the applied control strategy. The formation and trajectories of multiple UAVs are governed by two separate controllers, namely formation and trajectory tracking controllers respectively. In presence of environmental effects, disturbances due to wind and parametric uncertainties, the controller design process is a challenging task. This article proposes a robust adaptive formation and trajectory tacking control of multiple quad-rotor UAVs using super twisting sliding mode control method. In the proposed design, Lyapunov function-based adaptive disturbance estimators are used to compensate for the effects of external disturbances and parametric uncertainties. The stability of the proposed controllers is guaranteed using Lyapunov theorems. Two variants of the control schemes, namely fixed gain super twisting SMC (STSMC) and adaptive super twisting SMC (ASTSMC) are tested using numerical simulations performed in MATLAB/Simulink. From the results presented, it is verified that in presence of disturbances, the proposed ASTSMC controller exhibits enhanced robustness as compared to the fixed gain STSMC. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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Article
A UAV Maneuver Decision-Making Algorithm for Autonomous Airdrop Based on Deep Reinforcement Learning
Sensors 2021, 21(6), 2233; https://doi.org/10.3390/s21062233 - 23 Mar 2021
Cited by 2 | Viewed by 1087
Abstract
How to operate an unmanned aerial vehicle (UAV) safely and efficiently in an interactive environment is challenging. A large amount of research has been devoted to improve the intelligence of a UAV while performing a mission, where finding an optimal maneuver decision-making policy [...] Read more.
How to operate an unmanned aerial vehicle (UAV) safely and efficiently in an interactive environment is challenging. A large amount of research has been devoted to improve the intelligence of a UAV while performing a mission, where finding an optimal maneuver decision-making policy of the UAV has become one of the key issues when we attempt to enable the UAV autonomy. In this paper, we propose a maneuver decision-making algorithm based on deep reinforcement learning, which generates efficient maneuvers for a UAV agent to execute the airdrop mission autonomously in an interactive environment. Particularly, the training set of the learning algorithm by the Prioritized Experience Replay is constructed, that can accelerate the convergence speed of decision network training in the algorithm. It is shown that a desirable and effective maneuver decision-making policy can be found by extensive experimental results. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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Article
Web AR Solution for UAV Pilot Training and Usability Testing
Sensors 2021, 21(4), 1456; https://doi.org/10.3390/s21041456 - 19 Feb 2021
Cited by 5 | Viewed by 1740
Abstract
Data and services are available anywhere at any time thanks to the Internet and mobile devices. Nowadays, there are new ways of representing data through trendy technologies such as augmented reality (AR), which extends our perception of reality through the addition of a [...] Read more.
Data and services are available anywhere at any time thanks to the Internet and mobile devices. Nowadays, there are new ways of representing data through trendy technologies such as augmented reality (AR), which extends our perception of reality through the addition of a virtual layer on top of real-time images. The great potential of unmanned aerial vehicles (UAVs) for carrying out routine and professional tasks has encouraged their use in the creation of several services, such as package delivery or industrial maintenance. Unfortunately, drone piloting is difficult to learn and requires specific training. Since regular training is performed with virtual simulations, we decided to propose a multiplatform cloud-hosted solution based in Web AR for drone training and usability testing. This solution defines a configurable trajectory through virtual elements represented over barcode markers placed on a real environment. The main goal is to provide an inclusive and accessible training solution which could be used by anyone who wants to learn how to pilot or test research related to UAV control. For this paper, we reviewed drones, AR, and human–drone interaction (HDI) to propose an architecture and implement a prototype, which was built using a Raspberry Pi 3, a camera, and barcode markers. The validation was conducted using several test scenarios. The results show that a real-time AR experience for drone pilot training and usability testing is achievable through web technologies. Some of the advantages of this approach, compared to traditional methods, are its high availability by using the web and other ubiquitous devices; the minimization of technophobia related to crashes; and the development of cost-effective alternatives to train pilots and make the testing phase easier for drone researchers and developers through trendy technologies. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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Article
Modeling of a Novel Coaxial Ducted Fan Aerial Robot Combined with Corner Environment by Using Artificial Neural Network
Sensors 2020, 20(20), 5805; https://doi.org/10.3390/s20205805 - 14 Oct 2020
Cited by 2 | Viewed by 832
Abstract
A novel coaxial ducted fan aerial robot with a manipulator is proposed which can achieve some hover operation tasks in a corner environment, such as switching on and off a wall-attached button on the corner. In order to study the aerodynamic interference between [...] Read more.
A novel coaxial ducted fan aerial robot with a manipulator is proposed which can achieve some hover operation tasks in a corner environment, such as switching on and off a wall-attached button on the corner. In order to study the aerodynamic interference between the prototype and the environment when the aerial robot is hovering in the corner environment, a method for the comprehensive modeling of the prototype and corner environment based on the artificial neural network is presented. By using the CFD simulation software, the flow field of the prototype at different positions with the corner effect is analyzed. After determining the input, output and structure of the neural network model, the Adam and gradient descent algorithms are selected as the neural network training algorithms, respectively. In addition, to optimize the initial weights and biases of the neural network model, the genetic algorithm is precisely used. The three-dimensional prediction surfaces generated by the three methods of the neural network, kriging surface and the polynomial fitting are compared. The results show that the neural network has high prediction accuracy, and can be applied to the comprehensive modeling of the prototype and the corner environment. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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Article
CANsec: A Practical in-Vehicle Controller Area Network Security Evaluation Tool
Sensors 2020, 20(17), 4900; https://doi.org/10.3390/s20174900 - 30 Aug 2020
Cited by 8 | Viewed by 1835
Abstract
The Internet of Things (IoT) is an industry-recognized next intelligent life solution that increases the level of comfort, efficiency, and automation for citizens through numerous sensors, smart devices, and cloud stations connected physically. As an important application scenario of IoT, the Internet of [...] Read more.
The Internet of Things (IoT) is an industry-recognized next intelligent life solution that increases the level of comfort, efficiency, and automation for citizens through numerous sensors, smart devices, and cloud stations connected physically. As an important application scenario of IoT, the Internet of Vehicles (IoV) plays an extremely critical role in the intelligent transportation field. In fact, the In-Vehicle Network of smart vehicles that are recognized as the core roles in intelligent transportation is currently the Controller Area Network (CAN). However, the In-Vehicle CAN bus protocol has several vulnerabilities without any encryption, authentication, or integrity checking, which severely threatens the safety of drivers and passengers. Once malicious attackers hack the vehicular gateway and obtain the access right of the CAN, they may control the vehicle based on the vulnerabilities of the CAN bus protocol. Given the severe security risk of CAN, we proposed the CANsec, a practical In-Vehicle CAN security evaluation tool that simulates malicious attacks according to major attack models to evaluate the security risk of the In-Vehicle CAN. We also show a usage case of the CANsec without knowing any information from the vehicle manufacturer. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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Article
Optimal Cooperative Guidance Laws for Two UAVs Under Sensor Information Deficiency Constraints
Sensors 2020, 20(17), 4790; https://doi.org/10.3390/s20174790 - 25 Aug 2020
Viewed by 941
Abstract
This paper presents closed-form optimal cooperative guidance laws for two UAVs under information constraints that achieve the required relative approach angle. Two UAVs cooperate to optimize a common cost function under a coupled constraint on terminal velocity vectors and the information constraint which [...] Read more.
This paper presents closed-form optimal cooperative guidance laws for two UAVs under information constraints that achieve the required relative approach angle. Two UAVs cooperate to optimize a common cost function under a coupled constraint on terminal velocity vectors and the information constraint which defines the sensor information availability. To handle the information constraint, a general two-player partially nested decentralized optimal control problem is considered in the continuous finite-horizon time domain. It is shown that under the state-separation principle the optimal solution of the decentralized control problem can be obtained by solving two centralized subproblems which cover the prediction problem for the information-deficient player and the prediction error minimization problem for the player with full information. Based on the solution of the decentralized optimal control problem, the explicit closed-form cooperative guidance laws that can be efficiently implemented on conventional guidance computers are derived. The performance of the proposed guidance laws is investigated on both centralized and decentralized cooperative scenarios with nonlinear engagement kinematics of networked two-UAV systems. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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Article
Genetic Optimization-Based Consensus Control of Multi-Agent 6-DoF UAV System
Sensors 2020, 20(12), 3576; https://doi.org/10.3390/s20123576 - 24 Jun 2020
Cited by 22 | Viewed by 1448
Abstract
A consensus control law is proposed for a multi-agent system of quadrotors with leader–follower communication topology for three quadrotor agents. The genetic algorithm (GA) is the proposed optimization technique to tune the consensus control parameters. The complete nonlinear model is used without any [...] Read more.
A consensus control law is proposed for a multi-agent system of quadrotors with leader–follower communication topology for three quadrotor agents. The genetic algorithm (GA) is the proposed optimization technique to tune the consensus control parameters. The complete nonlinear model is used without any further simplifications in the simulations, while simplification in the model is used to theoretically design the controller. Different case studies and tests are done (i.e., trajectory tracking formation and switching topology) to show the effectiveness of the proposed controller. The results show good performance in all tests while achieving the consensus of the desired formations. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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Article
Flight and Interaction Control of an Innovative Ducted Fan Aerial Manipulator
Sensors 2020, 20(11), 3019; https://doi.org/10.3390/s20113019 - 26 May 2020
Cited by 2 | Viewed by 1095
Abstract
An innovative aerial manipulator with ducted fans is proposed to achieve side-on aerial manipulation tasks in a confined environment, such as canopy sampling in dense forests. The dynamic model of the novel design is studied, and on this basis a composite controller is [...] Read more.
An innovative aerial manipulator with ducted fans is proposed to achieve side-on aerial manipulation tasks in a confined environment, such as canopy sampling in dense forests. The dynamic model of the novel design is studied, and on this basis a composite controller is proposed to address the challenges of arm extension and physical interaction during the manipulation process. An adaptive controller is proposed for the aerial platform to achieve good stability and tracking performance under the manipulator motion, and an impedance controller is designed for the manipulator to ensure compliance and stability during physical contact. The experimental tests validate the effectiveness of the proposed prototype structure and controller design. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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Article
Emerging Inter-Swarm Collaboration for Surveillance Using Pheromones and Evolutionary Techniques
Sensors 2020, 20(9), 2566; https://doi.org/10.3390/s20092566 - 30 Apr 2020
Cited by 6 | Viewed by 1403
Abstract
In this article, we propose a new mobility model, called Attractor Based Inter-Swarm collaborationS (ABISS), for improving the surveillance of restricted areas performed by unmanned autonomous vehicles. This approach uses different types of vehicles which explore an area of interest following unpredictable trajectories [...] Read more.
In this article, we propose a new mobility model, called Attractor Based Inter-Swarm collaborationS (ABISS), for improving the surveillance of restricted areas performed by unmanned autonomous vehicles. This approach uses different types of vehicles which explore an area of interest following unpredictable trajectories based on chaotic solutions of dynamic systems. Collaborations between vehicles are meant to cover some regions of the area which are unreachable by members of one swarm, e.g., unmanned ground vehicles on water surface, by using members of another swarm, e.g., unmanned aerial vehicles. Experimental results demonstrate that collaboration is not only possible but also emerges as part of the configurations calculated by a specially designed and parameterised evolutionary algorithm. Experiments were conducted on 12 different case studies including 30 scenarios each, observing an improvement in the total covered area up to 11%, when comparing ABISS with a non-collaborative approach. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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Article
Mobility Control of Unmanned Aerial Vehicle as Communication Relay to Optimize Ground-to-Air Uplinks
Sensors 2020, 20(8), 2332; https://doi.org/10.3390/s20082332 - 19 Apr 2020
Cited by 8 | Viewed by 1270
Abstract
In recent years, unmanned aerial vehicles (UAVs) have been considered an ideal relay platform for enhancing the communication between ground agents, because they fly at high altitudes and are easy to deploy with strong adaptabilities. Their maneuvering allows them to adjust their location [...] Read more.
In recent years, unmanned aerial vehicles (UAVs) have been considered an ideal relay platform for enhancing the communication between ground agents, because they fly at high altitudes and are easy to deploy with strong adaptabilities. Their maneuvering allows them to adjust their location to optimize the performance of links, which brings out the relay UAV autonomous mobility control problem. This work addressed the problem in a novel scene with mobile agents and completely unknown wireless channel properties, using only online measured information of received signal strength (RSS) and agent positions. The problem is challenging because of the unknown and dynamic radio frequency (RF) environment cause by agents and UAV maneuvering. We present a framework for both end-to-end communication and multi-agent-inter communication applications, and focus on proposing: (1) least square estimation-based channel approximation with consideration of environment effects and, (2) gradient-based optimal relay position seeking. Simulation results show that considering the environmental effects on channel parameters is meaningful and beneficial in using UAV as relays for the communication of multiple ground agents, and validate that the proposed algorithms optimizes the network performance by controlling the heading of the UAV. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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Article
Counter a Drone in a Complex Neighborhood Area by Deep Reinforcement Learning
Sensors 2020, 20(8), 2320; https://doi.org/10.3390/s20082320 - 18 Apr 2020
Cited by 6 | Viewed by 2332
Abstract
Counter-drone technology by using artificial intelligence (AI) is an emerging technology and it is rapidly developing. Considering the recent advances in AI, counter-drone systems with AI can be very accurate and efficient to fight against drones. The time required to engage with the [...] Read more.
Counter-drone technology by using artificial intelligence (AI) is an emerging technology and it is rapidly developing. Considering the recent advances in AI, counter-drone systems with AI can be very accurate and efficient to fight against drones. The time required to engage with the target can be less than other methods based on human intervention, such as bringing down a malicious drone by a machine-gun. Also, AI can identify and classify the target with a high precision in order to prevent a false interdiction with the targeted object. We believe that counter-drone technology with AI will bring important advantages to the threats coming from some drones and will help the skies to become safer and more secure. In this study, a deep reinforcement learning (DRL) architecture is proposed to counter a drone with another drone, the learning drone, which will autonomously avoid all kind of obstacles inside a suburban neighborhood environment. The environment in a simulator that has stationary obstacles such as trees, cables, parked cars, and houses. In addition, another non-malicious third drone, acting as moving obstacle inside the environment was also included. In this way, the learning drone is trained to detect stationary and moving obstacles, and to counter and catch the target drone without crashing with any other obstacle inside the neighborhood. The learning drone has a front camera and it can capture continuously depth images. Every depth image is part of the state used in DRL architecture. There are also scalar state parameters such as velocities, distances to the target, distances to some defined geofences and track, and elevation angles. The state image and scalars are processed by a neural network that joints the two state parts into a unique flow. Moreover, transfer learning is tested by using the weights of the first full-trained model. With transfer learning, one of the best jump-starts achieved higher mean rewards (close to 35 more) at the beginning of training. Transfer learning also shows that the number of crashes during training can be reduced, with a total number of crashed episodes reduced by 65%, when all ground obstacles are included. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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Review

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Review
Drone Swarms as Networked Control Systems by Integration of Networking and Computing
Sensors 2021, 21(8), 2642; https://doi.org/10.3390/s21082642 - 09 Apr 2021
Cited by 11 | Viewed by 2022
Abstract
The study of multi-agent systems such as drone swarms has been intensified due to their cooperative behavior. Nonetheless, automating the control of a swarm is challenging as each drone operates under fluctuating wireless, networking and environment constraints. To tackle these challenges, we consider [...] Read more.
The study of multi-agent systems such as drone swarms has been intensified due to their cooperative behavior. Nonetheless, automating the control of a swarm is challenging as each drone operates under fluctuating wireless, networking and environment constraints. To tackle these challenges, we consider drone swarms as Networked Control Systems (NCS), where the control of the overall system is done enclosed within a wireless communication network. This is based on a tight interconnection between the networking and computational systems, aiming to efficiently support the basic control functionality, namely data collection and exchanging, decision-making, and the distribution of actuation commands. Based on a literature analysis, we do not find revision papers about design of drone swarms as NCS. In this review, we introduce an overview of how to develop self-organized drone swarms as NCS via the integration of a networking system and a computational system. In this sense, we describe the properties of the proposed components of a drone swarm as an NCS in terms of networking and computational systems. We also analyze their integration to increase the performance of a drone swarm. Finally, we identify a potential design choice, and a set of open research challenges for the integration of network and computing in a drone swarm as an NCS. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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Review
UAV Positioning Mechanisms in Landing Stations: Classification and Engineering Design Review
Sensors 2020, 20(13), 3648; https://doi.org/10.3390/s20133648 - 29 Jun 2020
Cited by 9 | Viewed by 2402
Abstract
Landing platforms’ automation is aimed at servicing vertical take-off and landing UAVs between flights and maintaining their airworthiness. Over the last few years, different designs for the landing platforms have been proposed. This shows a strong development and establishment of automatic landing platforms [...] Read more.
Landing platforms’ automation is aimed at servicing vertical take-off and landing UAVs between flights and maintaining their airworthiness. Over the last few years, different designs for the landing platforms have been proposed. This shows a strong development and establishment of automatic landing platforms with UAV positioning devices on the landing site. Positioning and safe fixation of the UAV are some of the main features of the landing platform, especially if it is mounted on a movable vehicle. This article focuses exclusively on the landing platform and its elements that provide the positioning of the UAV by affecting it during and after the landing. Both active devices and mechanisms and passive elements used for positioning are considered. This article, based on the review of recent patents and publications, gives the classification of positioning approaches used in landing stations with the analysis of the required landing precision, as well as the pros and cons of each type of approach. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Control, Networks, System and Application)
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