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Recent Advances in UAVs’ GN&C (Guidance, Navigation, and Control) Technologies

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

Deadline for manuscript submissions: closed (25 February 2024) | Viewed by 16429

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Special Issue Editors

Dr. Xueyun Wang

E-Mail Website
Guest Editor

School of Instrument Science and Optoelectronics Engineering, Beihang University, XueYuan Road No. 37, HaiDian District, Beijing 100191, China
Interests: UAV; guidance; navigation; control

Dr. Stefano Caizzone

E-Mail Website
Co-Guest Editor

Institute of Communications and Navigation, German Aerospace Center (DLR), 82234 Wessling, Germany
Interests: satellite navigation; Global Positioning System; adaptive antenna arrays; aircraft navigation; array signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Emerging applications in various types of UAVs, such as rotor aircrafts, tilt-rotor aircrafts, blended-wing-body planes, and hypersonic vehicles, require innovative advanced GN&C technologies with great robustness and good performance under uncertainty and disturbances. We are pleased to announce this Special Issue on UAVs’ GN&C (guidance, navigation, and control) and invite manuscripts that highlight recent advances in this field. The scope of this Special Issue will include:

Innovative designs for UAVs’ GN&C;
Novel navigation and detection concepts and architectures;
Improvements in data processing and sensor fusion techniques that enhance GN&C performance;
Research illustrating advances in integrated guidance and control for UAVs;
Methods and techniques of Artificial Intelligence for UAVs’ GN&C;
New methods of navigation, estimation, and tracking;
GN&C of multiple UAVs or UAV swarms.

Enquiries about the issue’s scope can be directed to the Guest Editors.

Dr. Xueyun Wang
Dr. Stefano Caizzone
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. Sensors 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 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

integrated guidance and control
intelligent guidance and control
optimized guidance and control
flight experiment and simulations
inertial navigation
sensor fusion
trajectory tracking guidance
attitude control
control of UAV swarms
navigation of UAV swarms
trajectory optimization

Published Papers (11 papers)

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Research

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27 pages, 3261 KiB

Open AccessArticle

OPTILOD: Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones

by Alireza Famili, Angelos Stavrou, Haining Wang and Jung-Min (Jerry) Park

Sensors 2024, 24(6), 1865; https://doi.org/10.3390/s24061865 - 14 Mar 2024

Cited by 1 | Viewed by 515

Abstract

For many applications, drones are required to operate entirely or partially autonomously. In order to fly completely or partially on their own, drones need to access location services for navigation commands. While using the Global Positioning System (GPS) is an obvious choice, GPS is not always available, can be spoofed or jammed, and is highly error-prone for indoor and underground environments. The ranging method using beacons is one of the most popular methods for localization, especially for indoor environments. In general, the localization error in this class is due to two factors: the ranging error, and the error induced by the relative geometry between the beacons and the target object to be localized. This paper proposes OPTILOD (Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones), an optimization algorithm for the optimal placement of beacons deployed in three-dimensional indoor environments. OPTILOD leverages advances in evolutionary algorithms to compute the minimum number of beacons and their optimal placement, thereby minimizing the localization error. These problems belong to the Mixed Integer Programming (MIP) class and are both considered NP-hard. Despite this, OPTILOD can provide multiple optimal beacon configurations that minimize the localization error and the number of deployed beacons concurrently and efficiently. Full article

(This article belongs to the Special Issue Recent Advances in UAVs’ GN&C (Guidance, Navigation, and Control) Technologies)

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19 pages, 2676 KiB

Open AccessArticle

Improved A-Star Search Algorithm for Probabilistic Air Pollution Detection Using UAVs

by Il-kyu Ha

Sensors 2024, 24(4), 1141; https://doi.org/10.3390/s24041141 - 9 Feb 2024

Viewed by 692

Abstract

Recently, air pollution problems in urban areas have become serious, and unmanned aerial vehicles (UAVs) can be used to monitor air pollution because they can perform spatial movement. However, because air pollution sources are fluid, probabilistic search methods are required to identify a target through the probability of its existence. This study proposes an efficient algorithm to detect air pollution in urban areas using UAVs. An improved A-star algorithm that can efficiently perform searches based on a probabilistic search model using a UAV is designed. In particular, in the proposed improved A-star algorithm, several special weights are used to calculate the probability of target existence. For example, a heuristic weight based on the expected target, a weight based on data collected from the drone sensor, and a weight based on the prior information of obstacles presence are determined. The method and procedure for applying the proposed algorithm to the stochastic search environment of a drone are described. Finally, the superiority of the proposed improved A-star algorithm is demonstrated by comparing it with existing stochastic search algorithms through various practical simulations. The proposed method exhibited more than 45% better performance in terms of successful search rounds compared with existing methods. Full article

(This article belongs to the Special Issue Recent Advances in UAVs’ GN&C (Guidance, Navigation, and Control) Technologies)

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17 pages, 15553 KiB

Open AccessArticle

Experimental Investigation of Relative Localization Estimation in a Coordinated Formation Control of Low-Cost Underwater Drones

by Thierry Soriano, Hoang Anh Pham and Valentin Gies

Sensors 2023, 23(6), 3028; https://doi.org/10.3390/s23063028 - 10 Mar 2023

Cited by 1 | Viewed by 1177

Abstract

This study presents a relative localization estimation method for a group of low-cost underwater drones (l-UD), which only uses visual feedback provided by an on-board camera and IMU data. It aims to design a distributed controller for a group of robots to reach a specific shape. This controller is based on a leader–follower architecture. The main contribution is to determine the relative position between the l-UD without using digital communication and sonar positioning methods. In addition, the proposed implementation of the EKF to fuse the vision data and the IMU data improves the prediction capability in cases where the robot is out of view of the camera. This approach allows the study and testing of distributed control algorithms for low-cost underwater drones. Finally, three robot operating system (ROS) platform-based BlueROVs are used in an experiment in a near-realistic environment. The experimental validation of the approach has been obtained by investigating different scenarios. Full article

(This article belongs to the Special Issue Recent Advances in UAVs’ GN&C (Guidance, Navigation, and Control) Technologies)

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18 pages, 5708 KiB

Open AccessArticle

Microphones as Airspeed Sensors for Unmanned Aerial Vehicles

by Momchil Makaveev, Mirjam Snellen and Ewoud J. J. Smeur

Sensors 2023, 23(5), 2463; https://doi.org/10.3390/s23052463 - 23 Feb 2023

Viewed by 2061

Abstract

This paper puts forward a novel design for an airspeed instrument aimed at small fixed-wing tail-sitter unmanned aerial vehicles. The working principle is to relate the power spectra of the wall-pressure fluctuations beneath the turbulent boundary layer present over the vehicle’s body in flight to its airspeed. The instrument consists of two microphones; one flush-mounted on the vehicle’s nose cone, which captures the pseudo-sound caused by the turbulent boundary layer, and a micro-controller that processes the signals and computes the airspeed. A feed-forward single-layer neural network is used to predict the airspeed based on the power spectra of the microphones’ signals. The neural network is trained using data obtained from wind tunnel and flight experiments. Several neural networks were trained and validated using only flight data, with the best one achieving a mean approximation error of 0.043 m/s and having a standard deviation of 1.039 m/s. The angle of attack has a significant impact on the measurement, but if the angle of attack is known, the airspeed could still be successfully predicted for a wide range of angles of attack. Full article

(This article belongs to the Special Issue Recent Advances in UAVs’ GN&C (Guidance, Navigation, and Control) Technologies)

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24 pages, 10157 KiB

Open AccessArticle

Thrust Vectoring Control of a Novel Tilt-Rotor UAV Based on Backstepping Sliding Model Method

by Zelong Yu, Jingjuan Zhang and Xueyun Wang

Sensors 2023, 23(2), 574; https://doi.org/10.3390/s23020574 - 4 Jan 2023

Cited by 3 | Viewed by 2090

Abstract

In this paper, a control method of a novel tilt-rotor UAV with a blended wing body layout is studied. The novel UAV is capable of vertical take-off and landing and has strong stealth capabilities that can be applied to carrier-borne reconnaissance aircraft. However, the high aspect ratio of blended wing body UAVs leads to a wingtip or oar-tip touchdown problem when adopting the conventional position-attitude control (CPAC) scheme with a large crosswind disturbance. Moreover, when the UAV is subject to interference during reconnaissance, aerial photography, and landing missions, the conventional scheme cannot provide both attitude stability and track accuracy. First, a direct thrust vectoring control (DTVC) scheme is proposed. The control authority of the rotor tilt mechanism was added to enable the decoupling of the attitude and trajectory and to improve the response rate and response bandwidth of the flight trajectory. Second, considering the problems of strong couplings and parameter uncertainties and the nonlinear features and mismatched perturbations that are inevitable in the tilt-rotor, we designed a robust UAV controller based on the backstepping sliding mode control method and determined the stability of the control system through the Lyapunov function. Finally, in the case of crosswire interference during vertical takeoff and landing and the aerial photography missions of the UAV, the numerical simulation of the CPAC scheme and the DTVC scheme was carried out, respectively, and the Monte Carlo random test method was introduced to conduct the statistical test of the landing accuracy. The simulation results show that the DTVC scheme improves the landing accuracy and speed compared to the CAPC scheme and decouples the position control loop from the attitude control loop, finally enabling the UAV to complete the flight control in the VTOL phase. Full article

(This article belongs to the Special Issue Recent Advances in UAVs’ GN&C (Guidance, Navigation, and Control) Technologies)

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15 pages, 1268 KiB

Open AccessArticle

Estimation of Airflow Parameters for Tail-Sitter UAV through a 5-Hole Probe Based on an ANN

by Xiaoda Li, Yongliang Wu, Xiaowen Shan, Haofan Zhang and Yang Chen

Sensors 2023, 23(1), 417; https://doi.org/10.3390/s23010417 - 30 Dec 2022

Cited by 3 | Viewed by 1667

Abstract

Fixed-wing vertical take-off and landing (VTOL) UAVs have received more and more attention in recent years, because they have the advantages of both fixed-wing UAVs and rotary-wing UAVs. To meet its large flight envelope, the VTOL UAV needs accurate measurement of airflow parameters, including angle of attack, sideslip angle and speed of incoming flow, in a larger range of angle of attack. However, the traditional devices for the measurement of airflow parameters are unsuitable for large-angle measurement. In addition, their performance is unsatisfactory when the UAV is at low speed. Therefore, for tail-sitter VTOL UAVs, we used a 5-hole pressure probe to measure the pressure of these holes and transformed the pressure data into the airflow parameters required in the flight process using an artificial neural network (ANN) method. Through a series of comparative experiments, we achieved a high-performance neural network. Through the processing and analysis of wind-tunnel-experiment data, we verified the feasibility of the method proposed in this paper, which can make more accurate estimates of airflow parameters within a certain range. Full article

(This article belongs to the Special Issue Recent Advances in UAVs’ GN&C (Guidance, Navigation, and Control) Technologies)

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27 pages, 4958 KiB

Open AccessArticle

Study of Modeling and Optimal Take-Off Scheme for a Novel Tilt-Rotor UAV

by Zelong Yu, Jingjuan Zhang and Xueyun Wang

Sensors 2022, 22(24), 9736; https://doi.org/10.3390/s22249736 - 12 Dec 2022

Cited by 1 | Viewed by 1282

Abstract

The optimal trajectory planning for a novel tilt-rotor unmanned aerial vehicle (UAV) in different take-off schemes was studied. A novel tilt-rotor UAV that possesses characteristics of both tilt-rotors and a blended wing body is introduced. The aerodynamic modeling of the rotor based on blade element momentum theory (BEMT) is established. An analytical method for determining the taking-off envelope of tilt angle versus airspeed is presented. A novel takeoff–tilting scheme, namely tilting take-off (TTO), is developed, and its optimal trajectory is designed based on the direct collocation method. Parameters such as the rotor thrust, tilt angle of rotor and angle of attack are chosen as control variables, and the forward velocity, vertical velocity and altitude are selected as state variables. The time and the energy consumption are considered in the performance optimization indexes. The optimal trajectories of the TTO scheme and other conventional schemes including vertical take-off (VTO) and short take-off (STO) are compared and analyzed. Simulation results indicate that the TTO scheme consumes 47 percent less time and 75 percent less energy than the VTO scheme. Moreover, with minor differences in time and energy consumption compared to the STO scheme, but without the need for sliding distance, TTO is the optimal take-off scheme to satisfy the flight constraints of a novel tilt-rotor UAV. Full article

(This article belongs to the Special Issue Recent Advances in UAVs’ GN&C (Guidance, Navigation, and Control) Technologies)

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16 pages, 7591 KiB

Open AccessArticle

Design and Installed Performance Analysis of a Miniaturized All-GNSS Bands Antenna Array for Robust Navigation on UAV Platforms

by Simon P. Hehenberger, Wahid Elmarissi and Stefano Caizzone

Sensors 2022, 22(24), 9645; https://doi.org/10.3390/s22249645 - 9 Dec 2022

Cited by 4 | Viewed by 1739

Abstract

Global navigation satellite systems (GNSS) are vital technologies of our age and serve a plethora of industries that rely on precise positioning for automation, efficiency, and safety. Emerging applications of unmanned aerial vehicles (UAV) in critical applications like security, surveillance, critical logistics and defense demand precise and robust navigation capabilities even in challenging environments with high multipath or (un-)intended interference. The design of robust GNSS receivers for UAV applications, capable of suppressing interfering signals, is challenging due to the need for multi-antenna systems and the stringent requirements on hardware to be lightweight and miniaturized enough to fit onto small mobile platforms. In order to overcome these limitations, the present article details a four-element wideband antenna array, fitting into a 100 mm diameter footprint. The array is capable to operate across all GNSS frequency bands while incorporating, if needed, a multipath mitigation solution. The antenna design relies on a modular concept with 3D printed Dielectric Resonator Antennas (DRAs) and vertical choke rings. The antenna performance is evaluated in terms of its radiation pattern via installed antenna simulations and measurements in an anechoic chamber. The effect of different installation heights on the antenna pattern is studied. Furthermore, GNSS measurements carried out with the array alone and mounted on the UAV are presented. Full article

(This article belongs to the Special Issue Recent Advances in UAVs’ GN&C (Guidance, Navigation, and Control) Technologies)

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23 pages, 6122 KiB

Open AccessArticle

A Low Complexity Persistent Reconnaissance Algorithm for FANET

by Yuan Guo, Hongying Tang and Ronghua Qin

Sensors 2022, 22(23), 9526; https://doi.org/10.3390/s22239526 - 6 Dec 2022

Cited by 3 | Viewed by 1198

Abstract

In recent years, with the rapid progress of unmanned aerial vehicle (UAV) technology, UAV-based systems have been widely used in both civilian and military applications. Researchers have proposed various network architectures and routing protocols to address the network connectivity problems associated with the high mobility of UAVs, and have achieved considerable results in a flying ad hoc network (FANET). Although scholars have noted various threats to UAVs in practical applications, such as local magnetic field variation, acoustic interference, and radio signal hijacking, few studies have taken into account the dynamic nature of these threat factors. Moreover, the UAVs’ high mobility combined with dynamic threats makes it more challenging to ensure connectivity while adapting to ever-changing scenarios. In this context, this paper introduces the concept of threat probability density function (threat PDF) and proposes a particle swarm optimization (PSO)-based threat avoidance and reconnaissance FANET construction algorithm (TARFC), which enables UAVs to dynamically adapt to avoid high-risk areas while maintaining FANET connectivity. Inspired by the graph editing distance, the total edit distance (TED) is defined to describe the alterations of the FANET and threat factors over time. Based on TED, a dynamic threat avoidance and continuous reconnaissance FANET operation algorithm (TA&CRFO) is proposed to realize semi-distributed control of the network. Simulation results show that both TARFC and TA&CRFO are effective in maintaining network connectivity and avoiding threats in dynamic scenarios. The average threat value of UAVs using TARFC and TA&CRFO is reduced by 3.99~27.51% and 3.07~26.63%, respectively, compared with the PSO algorithm. In addition, with limited distributed moderation, the complexity of the TA&CRFO algorithm is only 20.08% of that of TARFC. Full article

(This article belongs to the Special Issue Recent Advances in UAVs’ GN&C (Guidance, Navigation, and Control) Technologies)

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15 pages, 2306 KiB

Open AccessArticle

A Probabilistic–Geometric Approach for UAV Detection and Avoidance Systems

by Hae-In Lee, Hyo-Sang Shin and Antonios Tsourdos

Sensors 2022, 22(23), 9230; https://doi.org/10.3390/s22239230 - 27 Nov 2022

Cited by 1 | Viewed by 1320

Abstract

This paper proposes a collision avoidance algorithm for the detection and avoidance capabilities of Unmanned Aerial Vehicles (UAVs). The proposed algorithm aims to ensure minimum separation between UAVs and geofencing with multiple no-fly zones, considering the sensor uncertainties. The main idea is to compute the collision probability and to initiate collision avoidance manoeuvres determined by the differential geometry concept. The proposed algorithm is validated by both theoretical and numerical analysis. The results indicate that the proposed algorithm ensures minimum separation, efficiency, and scalability compared with other benchmark algorithms. Full article

(This article belongs to the Special Issue Recent Advances in UAVs’ GN&C (Guidance, Navigation, and Control) Technologies)

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Review

Jump to: Research

22 pages, 1652 KiB

Open AccessReview

Securing Your Airspace: Detection of Drones Trespassing Protected Areas

by Alireza Famili, Angelos Stavrou, Haining Wang, Jung-Min (Jerry) Park and Ryan Gerdes

Sensors 2024, 24(7), 2028; https://doi.org/10.3390/s24072028 - 22 Mar 2024

Cited by 1 | Viewed by 1575

Abstract

Unmanned Aerial Vehicle (UAV) deployment has risen rapidly in recent years. They are now used in a wide range of applications, from critical safety-of-life scenarios like nuclear power plant surveillance to entertainment and hobby applications. While the popularity of drones has grown lately, the associated intentional and unintentional security threats require adequate consideration. Thus, there is an urgent need for real-time accurate detection and classification of drones. This article provides an overview of drone detection approaches, highlighting their benefits and limitations. We analyze detection techniques that employ radars, acoustic and optical sensors, and emitted radio frequency (RF) signals. We compare their performance, accuracy, and cost under different operating conditions. We conclude that multi-sensor detection systems offer more compelling results, but further research is required. Full article

(This article belongs to the Special Issue Recent Advances in UAVs’ GN&C (Guidance, Navigation, and Control) Technologies)

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