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A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 38377

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Special Issue Editor

Dr. Mostafa Hassanalian

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Guest Editor

Department of Mechanical Engineering, New Mexico Tech, Weir Hall, Room 208, Socorro, NM 87801, USA
Interests: drones (UAV/MAV/NAV/PAV): fixed wings, flapping wings, tilt-rotor/wing drones, morphing drones, space and marine drones
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Special Issue Information

Dear Colleagues,

Today, there is a growing need for flying drones with diverse capabilities for both civilian and military applications. There is also a significant interest in the development of novel drones, which can autonomously fly in different environments and locations and perform various missions. In the past decade, the broad spectrum of applications of these drones has received a great deal of attention, which led to the invention of a large variety of drones of different sizes and weights. Depending on the flight missions of the drones, the size and type of installed equipment are different. Considerable advantages afforded by the drones have led to a myriad of studies focusing on the optimization and enhancement of the drones’ performances. According to the mentioned characteristics, drones benefit from the potential to carry out a variety of operations, including reconnaissance, patrolling, protection, transportation of loads, and aerology. They can carry various sensors: visual, acoustic, chemical, and biological. Drones often vary widely in their configurations, depending on the platform and mission. Drones can perform both outdoor and indoor missions in very challenging environments. The applications of drones can be categorized in different ways. They can be based on the type of missions (military/civil), type of flight zones (outdoor/indoor), and type of environments (underwater/on the water/ground/air/space). This Special Issue invites submissions that discuss the novel applications of drones, including but not limited to:

Inspection, survey, and mapping;
Agriculture and environment research;
Search and rescue (SAR) missions;
Mailing and delivery;
Military missions;
Marine and underwater missions;
Drones and planetary exploration;
Drones and underground spaces;
Miscellaneous applications of drones;
Drones and fashion industries;
Drone applications in the COVID-19 pandemic;
Drones and smart cities;
Drones and infrastructures;
Drones and 5G network coverages;
Drones and renewable energy power plants;
Integration of drones into airports.

Prof. Dr. Mostafa Hassanalian
Guest Editor

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Keywords

drone applications
space drones for planetary exploration
drones and marine environment
drones and indoor spaces
drones and COVID-19
future drone technologies

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Published Papers (10 papers)

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Research

22 pages, 21440 KiB

Open AccessArticle

Automatic Marine Debris Inspection

by Yu-Hsien Liao and Jih-Gau Juang

Aerospace 2023, 10(1), 84; https://doi.org/10.3390/aerospace10010084 - 14 Jan 2023

Cited by 1 | Viewed by 1685

Abstract

Plastic trash can be found anywhere, around the marina, beaches, and coastal areas in recent times. This study proposes a trash dataset called HAIDA and a trash detector that uses a YOLOv4-based object detection algorithm to monitor coastal trash pollution efficiently. Model selection, model evaluation, and hyperparameter tuning were applied to obtain the best model for the lowest generalization error in the real world. Comparison of the state-of-the-art object detectors based on YOLOv3, YOLOv4, and Scaled-YOLOv4 that used hyperparameter tuning, the three-way holdout method, and k-fold cross-validation have been presented. An unmanned aerial vehicle (UAV) was also employed to detect trash in coastal areas using the proposed method. The performance on image classification was satisfactory. Full article

(This article belongs to the Special Issue Applications of Drones)

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43 pages, 23211 KiB

Open AccessArticle

Multi-Domain Based Computational Investigations on Advanced Unmanned Amphibious System for Surveillances in International Marine Borders

by Vijayanandh Raja, Ramesh Murugesan, Parvathy Rajendran, Surya Palaniappan, Hussein A. Z. AL-bonsrulah, Darshan Kumar Jayaram and Mohammed Al-Bahrani

Aerospace 2022, 9(11), 652; https://doi.org/10.3390/aerospace9110652 - 26 Oct 2022

Cited by 5 | Viewed by 2824

Abstract

The conceptual design, component selection, and deployment experiments of an unmanned amphibious system (US) with a unique Becker in vertical stabilizer based on hydrodynamic research are included in this work. The use of USs is currently expanding significantly, and they are used for fish detection, oceanographic mapping, mining detection, monitoring marine life, and navy purposes. With a maximum forward speed of 30 m/s, the US’s hull is largely built with criteria for identifying and researching marine species. The significant lifetime decline of ocean species drives the deployment of unmanned vehicles for species monitoring from the water’s surface to 300 m below the surface. In addition, the medical team can help the species with health problems using this planned US because they have been identified. The conceptual design and estimated analytical equations encompass the fuselage, Becker rudder, propeller, and other sub-components. The locations of sensors, primarily used to locate mobile marine life, are also considered. A Becker rudder has been imposed to make sharp turns when the US is submerged in water. An advanced hydro propeller produces the propulsion with a 20 cm base diameter. Additionally, a piezoelectric patching-based energy extracting approach is used to the hydro-outside propeller’s surface. As a result, the electrical power generation for different lightweight materials is computed for the performance of US manoeuvrings. With the help of CATIA modelling of the intended USs and ANSYS Fluent hydrodynamic simulations, appropriate high-speed configurations are selected. Various stages of its mission profile, including the US in steady-level flight, the US in climb, and the US over the ocean surface, are subjected to computational simulations. Using an advanced computational technique and previously established experimental correlations, the reliability of these various computational solutions is examined and kept at an appropriate level. This US is highly suggested for marine-based real-time applications due to its acceptable output. Full article

(This article belongs to the Special Issue Applications of Drones)

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21 pages, 4885 KiB

Open AccessArticle

Earthwork Volume Calculation, 3D Model Generation, and Comparative Evaluation Using Vertical and High-Oblique Images Acquired by Unmanned Aerial Vehicles

by Kirim Lee and Won Hee Lee

Aerospace 2022, 9(10), 606; https://doi.org/10.3390/aerospace9100606 - 15 Oct 2022

Cited by 12 | Viewed by 4820

Abstract

In civil engineering and building construction, the earthwork volume calculation is one of the most important factors in the design and construction stages; therefore, an accurate calculation is necessary. Moreover, because managing earthworks is highly important, in this study, a three-dimensional (3D) model for earthwork calculation and management was performed using an unmanned aerial vehicle (UAV) and an RGB camera. Vertical and high-oblique images (45°, 60°, and 75°) were acquired at 50 and 100 m heights for accurate earthwork calculations and a 3D model, and data were generated by dividing the images into eight cases. Cases 1–4 were images acquired from a height of 50 m, and cases 5–8 were images acquired from a height of 100 m. (case 1: 90°, case 2: 90° + 45°, case 3: 90° + 60°, case 4: 90° + 75°, case 5: 90°, case 6: 90° + 45°, case 7: 90° + 60°, case 8: 90° + 75°). Three evaluations were performed on the data. First, the accuracy was evaluated through checkpoints for the orthophoto; second, the earthwork volumes calculated via a global positioning system and UAV were compared; finally, the 3D model was evaluated. Case 2, which showed the lowest root mean square error in the orthophoto accuracy evaluation, was the most accurate. Case 2 was the most accurate in the earthwork volume evaluation and 3D model compared to other cases. Through this study, the best results were obtained when using a vertical image and a high-oblique image of 40 to 50° when generating a 3D model for earthwork volume calculation and management. In addition, if the UAV is not affected by obstacles, it is better to shoot at about 50 m or less than to shoot the UAV height too high. Full article

(This article belongs to the Special Issue Applications of Drones)

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18 pages, 37728 KiB

Open AccessArticle

Aerial Footage Analysis Using Computer Vision for Efficient Detection of Points of Interest Near Railway Tracks

by Rohan Sharma, Kishan Patel, Sanyami Shah and Michal Aibin

Aerospace 2022, 9(7), 370; https://doi.org/10.3390/aerospace9070370 - 9 Jul 2022

Cited by 8 | Viewed by 3801

Abstract

Object detection is a fundamental part of computer vision, with a wide range of real-world applications. It involves the detection of various objects in digital images or video. In this paper, we propose a proof of concept usage of computer vision algorithms to improve the maintenance of railway tracks operated by Via Rail Canada. Via Rail operates about 500 trains running on 12,500 km of tracks. These tracks pass through long stretches of sparsely populated lands. Maintaining these tracks is challenging due to the sheer amount of resources required to identify the points of interest (POI), such as growing vegetation, missing or broken ties, and water pooling around the tracks. We aim to use the YOLO algorithm to identify these points of interest with the help of aerial footage. The solution shows promising results in detecting the POI based on unmanned aerial vehicle (UAV) images. Overall, we achieved a precision of 74% across all POI and a mean average precision @ 0.5 (mAP @ 0.5) of 70.7%. The most successful detection was the one related to missing ties, vegetation, and water pooling, with an average accuracy of 85% across all three POI. Full article

(This article belongs to the Special Issue Applications of Drones)

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26 pages, 17051 KiB

Open AccessArticle

Water Surface Flight Control of a Cross Domain Robot Based on an Adaptive and Robust Sliding Mode Barrier Control Algorithm

by Ke Wang, Yong Liu, Chengwei Huang and Wei Bao

Aerospace 2022, 9(7), 332; https://doi.org/10.3390/aerospace9070332 - 21 Jun 2022

Cited by 1 | Viewed by 1850

Abstract

When a cross-domain robot (CDR) flies on the water surface, the large pitch angle and roll angle may lead to water flooding into the robot cabin or even overturning. In addition, the CDR is influenced by some uncertain parameters and external disturbances, such as the water resistance and current. To constrain the robot attitude angle and improve the robustness of the controller, a non-singular terminal sliding mode asymmetric barrier control (NTSMABC) algorithm is proposed. All the uncertain disturbances are regarded as a lump disturbance, and a radial basis function neural network (RBFNN) is designed to compensate for the output of the controllers. Unlike the traditional quadrotors, the robot controls the yaw angle by paddles when the robot flies on the water surface. To prevent the actuator saturation and the robot from rolling over due to excessive yaw angular velocity, an adaptive integral sliding mode barrier control (AISMBC) algorithm is proposed to constrain the yaw angular velocity directly. This algorithm adaptively adjusts the gain of the sliding surface to suppress the influence of the lump disturbance on the robot. Another RBFNN is designed to compensate for the output of the controller. Simulation results demonstrate the effectiveness of the proposed control methods. Full article

(This article belongs to the Special Issue Applications of Drones)

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15 pages, 2594 KiB

Open AccessArticle

Fault-Tolerant Control for Hexacopter UAV Using Adaptive Algorithm with Severe Faults

by Ngoc Phi Nguyen, Nguyen Xuan Mung, Le Nhu Ngoc Thanh Ha and Sung Kyung Hong

Aerospace 2022, 9(6), 304; https://doi.org/10.3390/aerospace9060304 - 3 Jun 2022

Cited by 8 | Viewed by 3000

Abstract

In this paper, a fault-tolerant control method is proposed for a hexacopter under uncertainties. The proposed method is based on adaptive-sliding-mode control (ASMC) and a control allocation scheme. First, a mathematical model of the hexacopter is employed with model uncertainties. Next, the control allocation strategy is combined with ASMC to handle actuator faults, which can distribute the virtual control signal to redundant actuators. A modified fault-tolerant control is proposed to overcome this virtual input saturation. Finally, the system stability is validated using the Lyapunov theory. The performance of the proposed method is compared with that of normal ASMC. The simulation results show that the suggested strategy can realize quicker compensation under faulty conditions. Full article

(This article belongs to the Special Issue Applications of Drones)

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21 pages, 1687 KiB

Open AccessArticle

Optimal Aggressive Constrained Trajectory Synthesis and Control for Multi-Copters

by Tsung-Liang Liu and Kamesh Subbarao

Aerospace 2022, 9(6), 281; https://doi.org/10.3390/aerospace9060281 - 24 May 2022

Cited by 4 | Viewed by 2011

Abstract

In this paper, we propose a novel time and control effort optimal aggressive trajectory synthesis and control design methodology. The trajectory synthesis is a modified minimum snap design but with specific position and orientation constraints on a multi-copter, such as flying through tight spaces (windows) at specific orientations. The paper also introduces a means to stitch together multiple flight segments, enforce smoothness, and minimize segment times as well as the overall time, thereby resulting in very aggressive and feasible trajectories. A novel analysis for a specific scenario when no yaw angle specifications are provided is conducted, wherein a trade-off results in additional aggressiveness. The control algorithms to follow these trajectories are based on an inverse dynamics approach. Several candidate high-fidelity simulations are performed to verify the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue Applications of Drones)

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23 pages, 10908 KiB

Open AccessArticle

The Flying and Adhesion Robot Based on Approach and Vacuum

by Chengwei Huang, Yong Liu, Bing Bai and Ke Wang

Aerospace 2022, 9(5), 228; https://doi.org/10.3390/aerospace9050228 - 21 Apr 2022

Cited by 1 | Viewed by 2792

Abstract

The conventional flying and adhesion robot adsorbs on the wall by controlling the attitude angle to generate a horizontal-direction force combined with the negative-pressure device at the target position. However, when the robot is in contact with the wall, the wall will generate reaction forces and tilting moments on the robot, which increases the complexity of modeling and controlling the adsorption process. Therefore, inspired by perching mechanisms that geckos and tree frogs can use to jump and adsorb to vertical surfaces such as tree trunks, we propose a natural method based on approach adsorption. The method uses a suitable approaching velocity to achieve stable adsorption at the desired position. We investigate the effects of approach velocity, vacuum-pump flow rate and wall material on the adsorption performance. Furthermore, we design a unidirectional-approach-adsorption system and heading controller and establish a contact and negative-pressure model. The relevant parameters of the adsorption system are identified, and the ground-collision experiments and flight experiments for the flying and adhesion robot were carried out to validate the proposed method. Full article

(This article belongs to the Special Issue Applications of Drones)

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19 pages, 4242 KiB

Open AccessArticle

UAV Imagery for Automatic Multi-Element Recognition and Detection of Road Traffic Elements

by Liang Huang, Mulan Qiu, Anze Xu, Yu Sun and Juanjuan Zhu

Aerospace 2022, 9(4), 198; https://doi.org/10.3390/aerospace9040198 - 6 Apr 2022

Cited by 10 | Viewed by 2757

Abstract

Road traffic elements comprise an important part of roads and represent the main content involved in the construction of a basic traffic geographic information database, which is particularly important for the development of basic traffic geographic information. However, the following problems still exist for the extraction of traffic elements: insufficient data, complex scenarios, small targets, and incomplete element information. Therefore, a set of road traffic multielement remote sensing image datasets obtained by unmanned aerial vehicles (UAVs) is produced, and an improved YOLOv4 network algorithm combined with an attention mechanism is proposed to automatically recognize and detect multiple elements of road traffic in UAV imagery. First, the scale range of different objects in the datasets is counted, and then the size of the candidate box is obtained by the k-means clustering method. Second, mosaic data augmentation technology is used to increase the number of trained road traffic multielement datasets. Then, by integrating the efficient channel attention (ECA) mechanism into the two effective feature layers extracted from the YOLOv4 backbone network and the upsampling results, the network focuses on the feature information and then trains the datasets. At the same time, the complete intersection over union (CIoU) loss function is used to consider the geometric relationship between the object and the test object, to solve the overlapping problem of the juxtaposed dense test element anchor boxes, and to reduce the rate of missed detection. Finally, the mean average precision (mAP) is calculated to evaluate the experimental effect. The experimental results show that the mAP value of the proposed method is 90.45%, which is 15.80% better than the average accuracy of the original YOLOv4 network. The average detection accuracy of zebra crossings, bus stations, and roadside parking spaces is improved by 12.52%, 22.82%, and 12.09%, respectively. The comparison experiments and ablation experiments proved that the proposed method can realize the automatic recognition and detection of multiple elements of road traffic, and provide a new solution for constructing a basic traffic geographic information database. Full article

(This article belongs to the Special Issue Applications of Drones)

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20 pages, 6208 KiB

Open AccessArticle

Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

by Farhad Samadzadegan, Farzaneh Dadrass Javan, Farnaz Ashtari Mahini and Mehrnaz Gholamshahi

Aerospace 2022, 9(1), 31; https://doi.org/10.3390/aerospace9010031 - 10 Jan 2022

Cited by 41 | Viewed by 7470

Abstract

Drones are becoming increasingly popular not only for recreational purposes but also in a variety of applications in engineering, disaster management, logistics, securing airports, and others. In addition to their useful applications, an alarming concern regarding physical infrastructure security, safety, and surveillance at airports has arisen due to the potential of their use in malicious activities. In recent years, there have been many reports of the unauthorized use of various types of drones at airports and the disruption of airline operations. To address this problem, this study proposes a novel deep learning-based method for the efficient detection and recognition of two types of drones and birds. Evaluation of the proposed approach with the prepared image dataset demonstrates better efficiency compared to existing detection systems in the literature. Furthermore, drones are often confused with birds because of their physical and behavioral similarity. The proposed method is not only able to detect the presence or absence of drones in an area but also to recognize and distinguish between two types of drones, as well as distinguish them from birds. The dataset used in this work to train the network consists of 10,000 visible images containing two types of drones as multirotors, helicopters, and also birds. The proposed deep learning method can directly detect and recognize two types of drones and distinguish them from birds with an accuracy of 83%, mAP of 84%, and IoU of 81%. The values of average recall, average accuracy, and average F1-score were also reported as 84%, 83%, and 83%, respectively, in three classes. Full article

(This article belongs to the Special Issue Applications of Drones)

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