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Volume 5, March

Drones, Volume 5, Issue 2 (June 2021) – 30 articles

Cover Story (view full-size image): Nowadays, Unmanned Aerial Vehicles (UAVs) are being widely used for multiple operations, such as precision agriculture, environmental data collection, search-and-rescue operations, surveillance, etc. When designing effective UAV swarm-based applications, a key “ingredient” is a multi-protocol communication layer that is shared between flying drones and ground Base Transceiver Stations (BTSs); this design will often benefit from the presence of Line-of-Sight (LOS) links between UAVs (flying at altitudes of tens of meters) and terrestrial stations. Therefore, the adoption of hybrid networking strategies and architectures for UAV swarms, leveraging heterogeneous radio mesh networking communication protocols (e.g., IEEE 802.11s and LoRa), allows one to exchange data more robustly and more flexibly than single protocol-based architectures. View this paper.
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Concept Paper
Unmanned Autogyro for Mars Exploration: A Preliminary Study
Drones 2021, 5(2), 53; https://doi.org/10.3390/drones5020053 (registering DOI) - 18 Jun 2021
Viewed by 121
Abstract
Starting from the Martian environment, we examine all the necessary requirements for a UAV and outline the architecture of a gyroplane optimized for scientific research and support for (future) Mars explorers, highlighting its advantages and criticalities. After a careful trade-off between different vehicles [...] Read more.
Starting from the Martian environment, we examine all the necessary requirements for a UAV and outline the architecture of a gyroplane optimized for scientific research and support for (future) Mars explorers, highlighting its advantages and criticalities. After a careful trade-off between different vehicles suitable for a typical mission, some parameters are established to optimize the size and performance. In the second part, the project of the Spider gyroplane and the methodology used to balance the longitudinal masses are presented; in the third part, the parameters of the aerodynamic forces acting on the aircraft are highlighted to be able to focus them during the fluid dynamics simulations. Full article
(This article belongs to the Collection Feature Papers of Drones)
Article
Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy
Drones 2021, 5(2), 52; https://doi.org/10.3390/drones5020052 - 17 Jun 2021
Viewed by 229
Abstract
With the rise of Deep Learning approaches in computer vision applications, significant strides have been made towards vehicular autonomy. Research activity in autonomous drone navigation has increased rapidly in the past five years, and drones are moving fast towards the ultimate goal of [...] Read more.
With the rise of Deep Learning approaches in computer vision applications, significant strides have been made towards vehicular autonomy. Research activity in autonomous drone navigation has increased rapidly in the past five years, and drones are moving fast towards the ultimate goal of near-complete autonomy. However, while much work in the area focuses on specific tasks in drone navigation, the contribution to the overall goal of autonomy is often not assessed, and a comprehensive overview is needed. In this work, a taxonomy of drone navigation autonomy is established by mapping the definitions of vehicular autonomy levels, as defined by the Society of Automotive Engineers, to specific drone tasks in order to create a clear definition of autonomy when applied to drones. A top–down examination of research work in the area is conducted, focusing on drone navigation tasks, in order to understand the extent of research activity in each area. Autonomy levels are cross-checked against the drone navigation tasks addressed in each work to provide a framework for understanding the trajectory of current research. This work serves as a guide to research in drone autonomy with a particular focus on Deep Learning-based solutions, indicating key works and areas of opportunity for development of this area in the future. Full article
(This article belongs to the Topic Autonomy for Enabling the Next Generation of UAVs)
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Article
Efficient Reactive Obstacle Avoidance Using Spirals for Escape
Drones 2021, 5(2), 51; https://doi.org/10.3390/drones5020051 - 07 Jun 2021
Viewed by 378
Abstract
The usage of unmanned aerial vehicles (UAV) has increased in recent years and new application scenarios have emerged. Some of them involve tasks that require a high degree of autonomy, leading to increasingly complex systems. In order for a robot to be autonomous, [...] Read more.
The usage of unmanned aerial vehicles (UAV) has increased in recent years and new application scenarios have emerged. Some of them involve tasks that require a high degree of autonomy, leading to increasingly complex systems. In order for a robot to be autonomous, it requires appropriate perception sensors that interpret the environment and enable the correct execution of the main task of mobile robotics: navigation. In the case of UAVs, flying at low altitude greatly increases the probability of encountering obstacles, so they need a fast, simple, and robust method of collision avoidance. This work covers the problem of navigation in unknown scenarios by implementing a simple, yet robust, environment-reactive approach. The implementation is done with both CPU and GPU map representations to allow wider coverage of possible applications. This method searches for obstacles that cross a cylindrical safety volume, and selects an escape point from a spiral for avoiding the obstacle. The algorithm is able to successfully navigate in complex scenarios, using both a high and low-power computer, typically found aboard UAVs, relying only on a depth camera with a limited FOV and range. Depending on the configuration, the algorithm can process point clouds at nearly 40 Hz in Jetson Nano, while checking for threats at 10 kHz. Some preliminary tests were conducted with real-world scenarios, showing both the advantages and limitations of CPU and GPU-based methodologies. Full article
(This article belongs to the Collection Feature Papers of Drones)
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Article
Evaluating the Performance of sUAS Photogrammetry with PPK Positioning for Infrastructure Mapping
Drones 2021, 5(2), 50; https://doi.org/10.3390/drones5020050 - 01 Jun 2021
Viewed by 539
Abstract
Traditional acquisition methods for generating digital surface models (DSMs) of infrastructure are either low resolution and slow (total station-based methods) or expensive (LiDAR). By contrast, photogrammetric methods have recently received attention due to their ability to generate dense 3D models quickly for low [...] Read more.
Traditional acquisition methods for generating digital surface models (DSMs) of infrastructure are either low resolution and slow (total station-based methods) or expensive (LiDAR). By contrast, photogrammetric methods have recently received attention due to their ability to generate dense 3D models quickly for low cost. However, existing frameworks often utilize many manually measured control points, require a permanent RTK/PPK reference station, or yield a reconstruction accuracy too poor to be useful in many applications. In addition, the causes of inaccuracy in photogrammetric imagery are complex and sometimes not well understood. In this study, a small unmanned aerial system (sUAS) was used to rapidly image a relatively even, 1 ha ground surface. Model accuracy was investigated to determine the importance of ground control point (GCP) count and differential GNSS base station type. Results generally showed the best performance for tests using five or more GCPs or when a Continuously Operating Reference Station (CORS) was used, with vertical root mean square errors of 0.026 and 0.027 m in these cases. However, accuracy outputs generally met comparable published results in the literature, demonstrating the viability of analyses relying solely on a temporary local base with a one hour dwell time and no GCPs. Full article
(This article belongs to the Special Issue UAV Photogrammetry for 3D Modeling)
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Article
UAV Forensics: DJI Mini 2 Case Study
Drones 2021, 5(2), 49; https://doi.org/10.3390/drones5020049 - 01 Jun 2021
Viewed by 574
Abstract
Rapid technology advancements, especially in the past decade, have allowed off-the-shelf unmanned aerial vehicles (UAVs) that weigh less than 250 g to become available for recreational use by the general population. Many well-known manufacturers (e.g., DJI) are now focusing on this segment of [...] Read more.
Rapid technology advancements, especially in the past decade, have allowed off-the-shelf unmanned aerial vehicles (UAVs) that weigh less than 250 g to become available for recreational use by the general population. Many well-known manufacturers (e.g., DJI) are now focusing on this segment of UAVs, and the new DJI Mini 2 drone is one of many that falls under this category, which enables easy access to be purchased and used without any Part 107 certification and Remote ID registration. The versatility of drones and drone models is appealing for customers, but they pose many challenges to forensic tools and digital forensics investigators due to numerous hardware and software variations. In addition, different devices can be associated and used for controlling these drones (e.g., Android and iOS smartphones). Moreover, according to the Federal Aviation Administration (FAA), the adoption of Remote ID is not going to be required for people without the 107 certifications for this segment at least until 2023, which creates finding personally identifiable information a necessity in these types of investigations. In this research, we conducted a comprehensive investigation of DJI Mini 2 and its data stored across multiple devices (e.g., SD cards and mobile devices) that are associated with the drone. The aim of this paper is to (1) create several criminal-like scenarios, (2) acquire and analyze the created scenarios using leading forensics software (e.g., Cellebrite and Magnet Axiom) that are commonly used by law enforcement agencies, (3) and present findings associated with potential criminal activities. Full article
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Article
A Novel Link Failure Detection and Switching Algorithm for Dissimilar Redundant UAV Communication
Drones 2021, 5(2), 48; https://doi.org/10.3390/drones5020048 - 01 Jun 2021
Viewed by 513
Abstract
Unmanned Aerial Vehicles (UAVs) used for humanitarian applications require simple, accessible and reliable components. For example, a communication system between UAV and the Ground Control Station (GCS) is essential in order to monitor UAV status; various communication protocols are available in the industry. [...] Read more.
Unmanned Aerial Vehicles (UAVs) used for humanitarian applications require simple, accessible and reliable components. For example, a communication system between UAV and the Ground Control Station (GCS) is essential in order to monitor UAV status; various communication protocols are available in the industry. Such systems must be simple for non-technical personnel (e.g., healthcare workers) to operate. In this study, a novel link failure detection and switching algorithm was proposed for a dissimilar redundant UAV communication system designed for long-range vaccine delivery in rural areas. The algorithm would ease the workload of the operators and address a research gap in the design of such algorithms. A two-layer design is proposed: A baseline layer using the heartbeat method, and optimisations to speed up local failure detection. To dynamically tune the heartbeat timeout for the algorithm’s baseline without intervention from ground operators, the modified Jacobson’s algorithm was used. Lab simulations found that the algorithm was generally accurate in converging to an optimal value, but has less satisfactory performance at poor or unpredictable connectivity, or when link switches get triggered frequently. Improvements have been suggested for the algorithm. This study contributes to ongoing research on ensuring reliable UAV communication for humanitarian purposes. Full article
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Article
Evaluation of Unmanned Aerial Vehicles (UAV) as a Tool to Predict Biomass and Carbon of Tectona grandis in Silvopastoral Systems (SPS) in Costa Rica
Drones 2021, 5(2), 47; https://doi.org/10.3390/drones5020047 - 01 Jun 2021
Viewed by 486
Abstract
The main objective of this research was to evaluate the use of unmanned aerial vehicles (UAVs) in estimating the aboveground biomass and carbon, and the dasometric characteristics at three different spacings (2.5 m × 1.0 m, 2.5 m × 2.0 m and 2.5 [...] Read more.
The main objective of this research was to evaluate the use of unmanned aerial vehicles (UAVs) in estimating the aboveground biomass and carbon, and the dasometric characteristics at three different spacings (2.5 m × 1.0 m, 2.5 m × 2.0 m and 2.5 m × 3.0 m) in a silvopastoral system (SPS) for the biomass production of Tectona grandis. A total of 90 trees were sampled, 63 of which were used to perform a dasometric evaluation (vertical and horizontal) in a spacing test in an SPS, and the rest to evaluate the use of UAVs in estimating the aboveground biomass in the spacing test. The results showed significant differences in average diameter at breast height (dbh) between spacings, and in aboveground biomass per tree. The amount of aboveground biomass and carbon per hectare increases at smaller spacings, but the differences were not statistically significant. A logarithmic model was prepared to estimate the dbh based on the crown diameter from the data collected taken in the field, since estimating this variable by means of UAVs is difficult. Significant differences were found in the aboveground biomass estimated using the field data compared to UAV data. The estimation of the crown diameter of the selected trees, hindered by the canopy closure in the SPS, was not adequate, which could influence the amount of aboveground biomass estimated using UAV data. Full article
(This article belongs to the Section Drones in Agriculture and Forestry)
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Article
On the Dominant Factors of Civilian-Use Drones: A Thorough Study and Analysis of Cross-Group Opinions Using a Triple Helix Model (THM) with the Analytic Hierarchy Process (AHP)
Drones 2021, 5(2), 46; https://doi.org/10.3390/drones5020046 - 26 May 2021
Viewed by 535
Abstract
This study explores the experts’ opinions during the consultation stage before law-making for civilian drones. A thorough literature study is first undertaken to have the set of influencing factors that should be suitable for the investigation from the perspective of designing and selecting [...] Read more.
This study explores the experts’ opinions during the consultation stage before law-making for civilian drones. A thorough literature study is first undertaken to have the set of influencing factors that should be suitable for the investigation from the perspective of designing and selecting civilian drones. Several rounds of surveys using the Delphi method, followed by an analytic hierarchy process (AHP), are performed to conform to the organized tree structure of constructs and factors and to obtain the knowledge about the opinions of the expert groups, with the expert sample being intentionally partitioned into three opinion groups at the beginning: academia (A), industry (I), and research institutes (R). Doing so facilitates a “mind-mining” process using the triple helix model (THM), while the opinions across the groups can also be visualized and compared. This exploits a new set of knowledge for the design and selection of civilian drones on a scientific yet empirical basis, and the observed differences and similarities among the groups may benefit their future negotiations to propose the drafts for regulating the design, manufacturing, and uses of civilian drones. As several significant implications and insights are also drawn and gained from the abovementioned results eventually, some possible research directions are worthwhile. The proposed hybrid methodological flow is another novelty. Full article
(This article belongs to the Collection Feature Papers of Drones)
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Review
A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data
Drones 2021, 5(2), 45; https://doi.org/10.3390/drones5020045 - 25 May 2021
Viewed by 538
Abstract
Recent developments in technology and data processing for Unoccupied Aerial Vehicles (UAVs) have revolutionized the scope of ecosystem monitoring, providing novel pathways to fill the critical gap between limited-scope field surveys and limited-customization satellite and piloted aerial platforms. These advances are especially ground-breaking [...] Read more.
Recent developments in technology and data processing for Unoccupied Aerial Vehicles (UAVs) have revolutionized the scope of ecosystem monitoring, providing novel pathways to fill the critical gap between limited-scope field surveys and limited-customization satellite and piloted aerial platforms. These advances are especially ground-breaking for supporting management, restoration, and conservation of landscapes with limited field access and vulnerable ecological systems, particularly wetlands. This study presents a scoping review of the current status and emerging opportunities in wetland UAV applications, with particular emphasis on ecosystem management goals and remaining research, technology, and data needs to even better support these goals in the future. Using 122 case studies from 29 countries, we discuss which wetland monitoring and management objectives are most served by this rapidly developing technology, and what workflows were employed to analyze these data. This review showcases many ways in which UAVs may help reduce or replace logistically demanding field surveys and can help improve the efficiency of UAV-based workflows to support longer-term monitoring in the face of wetland environmental challenges and management constraints. We also highlight several emerging trends in applications, technology, and data and offer insights into future needs. Full article
(This article belongs to the Special Issue Ecological Applications of Drone-Based Remote Sensing)
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Article
Development of a Solar-Powered Unmanned Aerial Vehicle for Extended Flight Endurance
Drones 2021, 5(2), 44; https://doi.org/10.3390/drones5020044 - 24 May 2021
Viewed by 468
Abstract
Having an exciting array of applications, the scope of unmanned aerial vehicle (UAV) application could be far wider one if its flight endurance can be prolonged. Solar-powered UAV, promising notable prolongation in flight endurance, is drawing increasing attention in the industries’ recent research [...] Read more.
Having an exciting array of applications, the scope of unmanned aerial vehicle (UAV) application could be far wider one if its flight endurance can be prolonged. Solar-powered UAV, promising notable prolongation in flight endurance, is drawing increasing attention in the industries’ recent research and development. This work arose from a Bachelor’s degree capstone project at Hong Kong Polytechnic University. The project aims to modify a 2-metre wingspan remote-controlled (RC) UAV available in the consumer market to be powered by a combination of solar and battery-stored power. The major objective is to greatly increase the flight endurance of the UAV by the power generated from the solar panels. The power system is first designed by selecting the suitable system architecture and then by selecting suitable components related to solar power. The flight control system is configured to conduct flight tests and validate the power system performance. Under fair experimental conditions with desirable weather conditions, the solar power system on the aircraft results in 22.5% savings in the use of battery-stored capacity. The decrease rate of battery voltage during the stable level flight of the solar-powered UAV built is also much slower than the same configuration without a solar-power system. Full article
(This article belongs to the Section Drone Design and Development)
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Article
Assessment of the Influence of Survey Design and Processing Choices on the Accuracy of Tree Diameter at Breast Height (DBH) Measurements Using UAV-Based Photogrammetry
Drones 2021, 5(2), 43; https://doi.org/10.3390/drones5020043 - 24 May 2021
Viewed by 462
Abstract
This work provides a systematic evaluation of how survey design and computer processing choices (such as the software used or the workflow/parameters chosen) influence unmanned aerial vehicle (UAV)-based photogrammetry retrieval of tree diameter at breast height (DBH), an important 3D structural parameter in [...] Read more.
This work provides a systematic evaluation of how survey design and computer processing choices (such as the software used or the workflow/parameters chosen) influence unmanned aerial vehicle (UAV)-based photogrammetry retrieval of tree diameter at breast height (DBH), an important 3D structural parameter in forest inventory and biomass estimation. The study areas were an agricultural field located in the province of Málaga, Spain, where a small group of olive trees was chosen for the UAV surveys, and an open woodland area in the outskirts of Sofia, the capital of Bulgaria, where a 10 ha area grove, composed mainly of birch trees, was overflown. A DJI Phantom 4 Pro quadcopter UAV was used for the image acquisition. We applied structure from motion (SfM) to generate 3D point clouds of individual trees, using Agisoft and Pix4D software packages. The estimation of DBH in the point clouds was made using a RANSAC-based circle fitting tool from the TreeLS R package. All trees modeled had their DBH tape-measured on the ground for accuracy assessment. In the first study site, we executed many diversely designed flights, to identify which parameters (flying altitude, camera tilt, and processing method) gave us the most accurate DBH estimations; then, the resulting best settings configuration was used to assess the replicability of the method in the forested area in Bulgaria. The best configuration tested (flight altitudes of about 25 m above tree canopies, camera tilt 60°, forward and side overlaps of 90%, Agisoft ultrahigh processing) resulted in root mean square errors (RMSEs; %) of below 5% of the tree diameters in the first site and below 12.5% in the forested area. We demonstrate that, when carefully designed methodologies are used, SfM can measure the DBH of single trees with very good accuracy, and to our knowledge, the results presented here are the best achieved so far using (above-canopy) UAV-based photogrammetry. Full article
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Article
A Comparative UAV Forensic Analysis: Static and Live Digital Evidence Traceability Challenges
Drones 2021, 5(2), 42; https://doi.org/10.3390/drones5020042 - 21 May 2021
Viewed by 480
Abstract
The raising accessibility of Unmanned Aerial Vehicles (UAVs), colloquially known as drones, is rapidly increasing. Recent studies have discussed challenges that may come in tow with the growing use of this technology. These studies note that in-depth examination is required, especially when addressing [...] Read more.
The raising accessibility of Unmanned Aerial Vehicles (UAVs), colloquially known as drones, is rapidly increasing. Recent studies have discussed challenges that may come in tow with the growing use of this technology. These studies note that in-depth examination is required, especially when addressing challenges that carry a high volume of software data between sensors, actuators, and control commands. This work underlines static and live digital evidence traceability challenges to further enhance the UAV incident response plan. To study the live UAV forensic traceability issues, we apply the ‘purple-teaming’ exercise on small UAVs while conducting UAV forensic examination to determine technical challenges related to data integrity and repeatability. In addition, this research highlights current static technical challenges that could pose more challenges in justifying the discovered digital evidence. Additionally, this study discusses potential drone anti-forensic techniques and their association with the type of use, environment, attack vector, and level of expertise. To this end, we propose the UAV Kill Chain and categorize the impact and complexity of all highlighted challenges based on the conducted examination and the presented scientific contribution in this work. To the best of our knowledge, there has not been any contribution that incorporates ‘Purple-Teaming’ tactics to evaluate UAV-related research in cybersecurity and digital forensics. This work also proposes a categorization model that classifies the discovered UAV static and live digital evidence challenges based on their complexity and impact levels. Full article
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Article
Visual SLAM for Indoor Livestock and Farming Using a Small Drone with a Monocular Camera: A Feasibility Study
Drones 2021, 5(2), 41; https://doi.org/10.3390/drones5020041 - 19 May 2021
Viewed by 508
Abstract
Real-time data collection and decision making with drones will play an important role in precision livestock and farming. Drones are already being used in precision agriculture. Nevertheless, this is not the case for indoor livestock and farming environments due to several challenges and [...] Read more.
Real-time data collection and decision making with drones will play an important role in precision livestock and farming. Drones are already being used in precision agriculture. Nevertheless, this is not the case for indoor livestock and farming environments due to several challenges and constraints. These indoor environments are limited in physical space and there is the localization problem, due to GPS unavailability. Therefore, this work aims to give a step toward the usage of drones for indoor farming and livestock management. To investigate on the drone positioning in these workspaces, two visual simultaneous localization and mapping (VSLAM)—LSD-SLAM and ORB-SLAM—algorithms were compared using a monocular camera onboard a small drone. Several experiments were carried out in a greenhouse and a dairy farm barn with the absolute trajectory and the relative pose error being analyzed. It was found that the approach that suits best these workspaces is ORB-SLAM. This algorithm was tested by performing waypoint navigation and generating maps from the clustered areas. It was shown that aerial VSLAM could be achieved within these workspaces and that plant and cattle monitoring could benefit from using affordable and off-the-shelf drone technology. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems)
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Article
A UAV-GPR Fusion Approach for the Characterization of a Quarry Excavation Area in Falconara Albanese, Southern Italy
Drones 2021, 5(2), 40; https://doi.org/10.3390/drones5020040 - 18 May 2021
Viewed by 482
Abstract
The characterization of a quarry site which is suitable for railway ballast aggregate production represents a big challenge for the mining industry. The knowledge of structural discontinuities within local geological materials is fundamental to guide mining operations, optimize investments, and guarantee quarry security. [...] Read more.
The characterization of a quarry site which is suitable for railway ballast aggregate production represents a big challenge for the mining industry. The knowledge of structural discontinuities within local geological materials is fundamental to guide mining operations, optimize investments, and guarantee quarry security. This research work presents an innovative methodology for the subsurface investigation of a quarry excavation area down to a depth of about 50 m in Falconara Albanese, Calabria, Italy. The proposed methodological approach incorporates photogrammetry, drone technology, and GPR data acquisition and processing. Photogrammetry represents the first step for obtaining a 3D topographical model reconstruction of the whole quarry, helping to detail the acquisition approach and properly plan the subsequent drone survey. In particular, two 120 MHz antennas have been mounted on the drone and two profiles have been acquired above and across the quarry. Results show the presence of fractured material and demonstrate the applicability of the method for identification of areas that are more suitable for railway ballast production. The presented method is therefore capable of detecting subsurficial fractures at a quarry site by means of a relatively fast and cost-effective procedure. Results are achieved within the framework of an industry project. Full article
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Article
Monitoring Dynamic Braided River Habitats: Applicability and Efficacy of Aerial Photogrammetry from Manned Aircraft versus Unmanned Aerial Systems
Drones 2021, 5(2), 39; https://doi.org/10.3390/drones5020039 - 17 May 2021
Viewed by 684
Abstract
Despite growing interest in using lightweight unmanned aerial systems (UASs) for ecological research and conservation, review of the operational aspects of these evolving technologies is limited in the scientific literature. To derive an objective framework for choosing among technologies we calculated efficiency measures [...] Read more.
Despite growing interest in using lightweight unmanned aerial systems (UASs) for ecological research and conservation, review of the operational aspects of these evolving technologies is limited in the scientific literature. To derive an objective framework for choosing among technologies we calculated efficiency measures and conducted a data envelopment productivity frontier analysis (DEA) to compare the efficacy of using manned aircraft (Cessna with Aviatrix triggered image capture using a 50 mm lens) and UAS (Mavic Pro 2) for photogrammetric monitoring of restoration efforts in dynamic braided rivers in Southern New Zealand. Efficacy assessment was based on the technological, logistical, administrative, and economic requirements of pre (planning), peri (image acquiring) and post (image processing) phases. The results reveal that the technological and logistic aspects of UASs were more efficient than manned aircraft flights. Administratively, the first deployment of UASs is less efficient but was very flexible for subsequent deployment. Manned aircraft flights were more productive in terms of the number of acquired images, but the ground resolution of those images was lower compared with those from UASs. Frontier analysis confirmed that UASs would be economical for regular monitoring of habitats—and even more so if research personnel are trained to fly the UASs. Full article
(This article belongs to the Collection Feature Papers of Drones)
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Review
How Do Dangerous Goods Regulations Apply to Uncrewed Aerial Vehicles Transporting Medical Cargos?
Drones 2021, 5(2), 38; https://doi.org/10.3390/drones5020038 - 13 May 2021
Viewed by 471
Abstract
Commercial operations of uncrewed aerial vehicles (UAVs or drones) are expanding, with medical logistics using UAVs as part of health service supply chains being targeted. The ability to transport cargos that include items classified as Dangerous Goods (DG) is a significant factor in [...] Read more.
Commercial operations of uncrewed aerial vehicles (UAVs or drones) are expanding, with medical logistics using UAVs as part of health service supply chains being targeted. The ability to transport cargos that include items classified as Dangerous Goods (DG) is a significant factor in enabling UAV logistics to assist medical supply chains, but DG regulations for air transport have developed from the perspective of crewed aircraft and not UAVs. This paper provides an important audit of the current DG regulations, best practice in their application and the development of much-needed new governance that will be required to fully exploit UAVs for the safe transport of DG in medical logistics. Findings from the audit provide a summary of the circumstances and potential challenges resulting from the application of DG regulations as they stand to UAV operations, particularly for medical logistics, and convenient guidance on the practical implications of DG regulations for UAV operators. The main conclusion is that this is an under-researched domain, not yet given full consideration in a holistic way by regulators, governments, industry bodies, practitioners or academia. Full article
(This article belongs to the Special Issue Drones for Medicine Delivery and Healthcare Logistics)
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Article
Experimental Evaluation of Computer Vision and Machine Learning-Based UAV Detection and Ranging
Drones 2021, 5(2), 37; https://doi.org/10.3390/drones5020037 - 09 May 2021
Viewed by 558
Abstract
We consider the problem of vision-based detection and ranging of a target UAV using the video feed from a monocular camera onboard a pursuer UAV. Our previously published work in this area employed a cascade classifier algorithm to locate the target UAV, which [...] Read more.
We consider the problem of vision-based detection and ranging of a target UAV using the video feed from a monocular camera onboard a pursuer UAV. Our previously published work in this area employed a cascade classifier algorithm to locate the target UAV, which was found to perform poorly in complex background scenes. We thus study the replacement of the cascade classifier algorithm with newer machine learning-based object detection algorithms. Five candidate algorithms are implemented and quantitatively tested in terms of their efficiency (measured as frames per second processing rate), accuracy (measured as the root mean squared error between ground truth and detected location), and consistency (measured as mean average precision) in a variety of flight patterns, backgrounds, and test conditions. Assigning relative weights of 20%, 40% and 40% to these three criteria, we find that when flying over a white background, the top three performers are YOLO v2 (76.73 out of 100), Faster RCNN v2 (63.65 out of 100), and Tiny YOLO (59.50 out of 100), while over a realistic background, the top three performers are Faster RCNN v2 (54.35 out of 100, SSD MobileNet v1 (51.68 out of 100) and SSD Inception v2 (50.72 out of 100), leading us to recommend Faster RCNN v2 as the recommended solution. We then provide a roadmap for further work in integrating the object detector into our vision-based UAV tracking system. Full article
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Article
Comparing UAS LiDAR and Structure-from-Motion Photogrammetry for Peatland Mapping and Virtual Reality (VR) Visualization
Drones 2021, 5(2), 36; https://doi.org/10.3390/drones5020036 - 09 May 2021
Cited by 1 | Viewed by 714
Abstract
The mapping of peatland microtopography (e.g., hummocks and hollows) is key for understanding and modeling complex hydrological and biochemical processes. Here we compare unmanned aerial system (UAS) derived structure-from-motion (SfM) photogrammetry and LiDAR point clouds and digital surface models of an ombrotrophic bog, [...] Read more.
The mapping of peatland microtopography (e.g., hummocks and hollows) is key for understanding and modeling complex hydrological and biochemical processes. Here we compare unmanned aerial system (UAS) derived structure-from-motion (SfM) photogrammetry and LiDAR point clouds and digital surface models of an ombrotrophic bog, and we assess the utility of these technologies in terms of payload, efficiency, and end product quality (e.g., point density, microform representation, etc.). In addition, given their generally poor accessibility and fragility, peatlands provide an ideal model to test the usability of virtual reality (VR) and augmented reality (AR) visualizations. As an integrated system, the LiDAR implementation was found to be more straightforward, with fewer points of potential failure (e.g., hardware interactions). It was also more efficient for data collection (10 vs. 18 min for 1.17 ha) and produced considerably smaller file sizes (e.g., 51 MB vs. 1 GB). However, SfM provided higher spatial detail of the microforms due to its greater point density (570.4 vs. 19.4 pts/m2). Our VR/AR assessment revealed that the most immersive user experience was achieved from the Oculus Quest 2 compared to Google Cardboard VR viewers or mobile AR, showcasing the potential of VR for natural sciences in different environments. We expect VR implementations in environmental sciences to become more popular, as evaluations such as the one shown in our study are carried out for different ecosystems. Full article
(This article belongs to the Collection Feature Papers of Drones)
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Article
Comparison of Sentinel-2 and UAV Multispectral Data for Use in Precision Agriculture: An Application from Northern Greece
Drones 2021, 5(2), 35; https://doi.org/10.3390/drones5020035 - 07 May 2021
Viewed by 517
Abstract
The scope of this work is to compare Sentinel-2 and unmanned aerial vehicles (UAV) imagery from northern Greece for use in precision agriculture by implementing statistical analysis and 2D visualization. Surveys took place on five dates with a difference between the sensing dates [...] Read more.
The scope of this work is to compare Sentinel-2 and unmanned aerial vehicles (UAV) imagery from northern Greece for use in precision agriculture by implementing statistical analysis and 2D visualization. Surveys took place on five dates with a difference between the sensing dates for the two techniques ranging from 1 to 4 days. Using the acquired images, we initially computed the maps of the Normalized Difference Vegetation Index (NDVI), then the values of this index for fifteen points and four polygons (areas). The UAV images were not resampled, aiming to compare both techniques based on their initial standards, as they are used by the farmers. Similarities between the two techniques are depicted on the trend of the NDVI means for both satellite and UAV techniques, considering the points and the polygons. The differences are in the a) mean NDVI values of the points and b) range of the NDVI values of the polygons probably because of the difference in the spatial resolution of the two techniques. The correlation coefficient of the NDVI values, considering both points and polygons, ranges between 83.5% and 98.26%. In conclusion, both techniques provide important information in precision agriculture depending on the spatial extent, resolution, and cost, as well as the requirements of the survey. Full article
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Article
UAV-Based Classification of Cercospora Leaf Spot Using RGB Images
Drones 2021, 5(2), 34; https://doi.org/10.3390/drones5020034 - 05 May 2021
Viewed by 505
Abstract
Plant diseases can impact crop yield. Thus, the detection of plant diseases using sensors that can be mounted on aerial vehicles is in the interest of farmers to support decision-making in integrated pest management and to breeders for selecting tolerant or resistant genotypes. [...] Read more.
Plant diseases can impact crop yield. Thus, the detection of plant diseases using sensors that can be mounted on aerial vehicles is in the interest of farmers to support decision-making in integrated pest management and to breeders for selecting tolerant or resistant genotypes. This paper investigated the detection of Cercospora leaf spot (CLS), caused by Cercospora beticola in sugar beet using RGB imagery. We proposed an approach to tackle the CLS detection problem using fully convolutional neural networks, which operate directly on RGB images captured by a UAV. This efficient approach does not require complex multi- or hyper-spectral sensors, but provides reliable results and high sensitivity. We provided a detection pipeline for pixel-wise semantic segmentation of CLS symptoms, healthy vegetation, and background so that our approach can automatically quantify the grade of infestation. We thoroughly evaluated our system using multiple UAV datasets recorded from different sugar beet trial fields. The dataset consisted of a training and a test dataset and originated from different fields. We used it to evaluate our approach under realistic conditions and analyzed its generalization capabilities to unseen environments. The obtained results correlated to visual estimation by human experts significantly. The presented study underlined the potential of high-resolution RGB imaging and convolutional neural networks for plant disease detection under field conditions. The demonstrated procedure is particularly interesting for applications under practical conditions, as no complex and cost-intensive measuring system is required. Full article
(This article belongs to the Collection Feature Papers of Drones)
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Article
Communication Aware UAV Swarm Surveillance Based on Hierarchical Architecture
Drones 2021, 5(2), 33; https://doi.org/10.3390/drones5020033 - 30 Apr 2021
Viewed by 637
Abstract
Multi-agent unmanned aerial vehicle (UAV) teaming becomes an essential part in science mission, modern warfare surveillance, and disaster rescuing. This paper proposes a decentralized UAV swarm persistent monitoring strategy in realizing continuous sensing coverage and network service. A two-layer (high altitude and low [...] Read more.
Multi-agent unmanned aerial vehicle (UAV) teaming becomes an essential part in science mission, modern warfare surveillance, and disaster rescuing. This paper proposes a decentralized UAV swarm persistent monitoring strategy in realizing continuous sensing coverage and network service. A two-layer (high altitude and low altitude) UAV teaming hierarchical structure is adopted in realizing the accurate object tracking in the area of interest (AOI). By introducing the UAV communication channel model in its path planning, both centralized and decentralized control schemes would be evaluated in the waypoint tracking simulation. The UAV swarm network service and object tracking are measured by metrics of communication link quality and waypoints tracking accuracy. UAV swarm network connectivity are evaluated over different aspects, such as stability and volatility. The comparison of proposed algorithms is presented with simulations. The result shows that the decentralized scheme outperforms the centralized scheme in the mission of persistent surveillance, especially on maintaining the stability of inner UAV swarm network while tracking moving objects. Full article
(This article belongs to the Collection Feature Papers of Drones)
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Article
MIMO Relaying UAVs Operating in Public Safety Scenarios
Drones 2021, 5(2), 32; https://doi.org/10.3390/drones5020032 - 26 Apr 2021
Viewed by 592
Abstract
Methods to implement communication in natural and humanmade disasters have been widely discussed in the scientific community. Scientists believe that unmanned aerial vehicles (UAVs) relays will play a critical role in 5G public safety communications (PSC) due to their technical superiority. They have [...] Read more.
Methods to implement communication in natural and humanmade disasters have been widely discussed in the scientific community. Scientists believe that unmanned aerial vehicles (UAVs) relays will play a critical role in 5G public safety communications (PSC) due to their technical superiority. They have several significant advantages: a high degree of mobility, flexibility, exceptional line of sight, and real-time adaptative planning. For instance, cell edge coverage could be extended using relay UAVs. This paper summarizes the sidelink evolution in the 3GPP standardization associated with the usage of the device to device (D2D) techniques that use long term evolution (LTE) communication systems, potential extensions for 5G, and a study on the impact of circular mobility on relay UAVs using the software network simulator 3 (NS3). In this simulation, the transmitted packet percentage was evaluated where the speed of the UAV for users was changed. This paper also examines the multi-input multi-output (MIMO) communication applied to drones and proposes a new trajectory to assist users experiencing unfortunate circumstances. The overall communication is highly dependent on the drone speed and the use of MIMO and suitable antennas may influence overall transmission between users and the UAVs relay. When the UAVs relaying speed was configured at 108 km/h the total transmission rate was reduced to 55% in the group with 6 users allocated to each drone. Full article
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Article
Comparison of Outdoor Compost Pile Detection Using Unmanned Aerial Vehicle Images and Various Machine Learning Techniques
Drones 2021, 5(2), 31; https://doi.org/10.3390/drones5020031 - 26 Apr 2021
Viewed by 494
Abstract
Since outdoor compost piles (OCPs) contain large amounts of nitrogen and phosphorus, they act as a major pollutant that deteriorates water quality, such as eutrophication and green algae, when the OCPs enter the river during rainfall. In South Korea, OCPs are frequently used, [...] Read more.
Since outdoor compost piles (OCPs) contain large amounts of nitrogen and phosphorus, they act as a major pollutant that deteriorates water quality, such as eutrophication and green algae, when the OCPs enter the river during rainfall. In South Korea, OCPs are frequently used, but there is a limitation that a lot of manpower and budget are consumed to investigate the current situation, so it is necessary to efficiently investigate the OCPs. This study compared the accuracy of various machine learning techniques for the efficient detection and management of outdoor compost piles (OCPs), a non-point pollution source in agricultural areas in South Korea, using unmanned aerial vehicle (UAV) images. RGB, multispectral, and thermal infrared UAV images were taken in August and October 2019. Additionally, vegetation indices (NDVI, NDRE, ENDVI, and GNDVI) and surface temperature were also considered. Four machine learning techniques, including support vector machine (SVM), decision tree (DT), random forest (RF), and k-NN, were implemented, and the machine learning technique with the highest accuracy was identified by adjusting several variables. The accuracy of all machine learning techniques was very high, reaching values of up to 0.96. Particularly, the accuracy of the RF method with the number of estimators set to 10 was highest, reaching 0.989 in August and 0.987 in October. The proposed method allows for the prediction of OCP location and area over large regions, thereby foregoing the need for OCP field measurements. Therefore, our findings provide highly useful data for the improvement of OCP management strategies and water quality. Full article
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Article
Drones, Gulls and Urbanity: Interaction between New Technologies and Human Subsidized Species in Coastal Areas
Drones 2021, 5(2), 30; https://doi.org/10.3390/drones5020030 - 22 Apr 2021
Viewed by 512
Abstract
The use of drones has expanded the boundaries of several activities, which is expected to be utilized intensively in the near future. Interactions between urbanity and naturalness have been increasing while urban expansion amplifies the proximity between urban and natural areas. In this [...] Read more.
The use of drones has expanded the boundaries of several activities, which is expected to be utilized intensively in the near future. Interactions between urbanity and naturalness have been increasing while urban expansion amplifies the proximity between urban and natural areas. In this scenario, the interactions between drones and fauna could be augmented. Therefore, the aim of this study was to depict and evaluate the responses of the opportunistic and territorial seagull Larus livens to a small-sized drone during the non-breeding stage in urban areas and natural surroundings. The results evidenced that gulls do not react to drone sounds, coloration, or distance between them and the drone take-off spot. Clearly, the take-off vertical movement triggers an agonistic behavior that is more frequent in groups conformed by two adults, evidencing some kind of territorial response against the device, expressed as characteristic mobbing behavior. Thus, adult settled gulls in touristic and non-urbanized areas displayed agonistic behavior more frequently against the drone. Despite the coastal urban area being a free interaction environment, it evidences a low risk between drone management and territorial seabirds. Full article
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Article
Assessing the Potential of Remotely-Sensed Drone Spectroscopy to Determine Live Coral Cover on Heron Reef
Drones 2021, 5(2), 29; https://doi.org/10.3390/drones5020029 - 17 Apr 2021
Viewed by 616
Abstract
Coral reefs, as biologically diverse ecosystems, hold significant ecological and economic value. With increased threats imposed on them, it is increasingly important to monitor reef health by developing accessible methods to quantify coral cover. Discriminating between substrate types has previously been achieved with [...] Read more.
Coral reefs, as biologically diverse ecosystems, hold significant ecological and economic value. With increased threats imposed on them, it is increasingly important to monitor reef health by developing accessible methods to quantify coral cover. Discriminating between substrate types has previously been achieved with in situ spectroscopy but has not been tested using drones. In this study, we test the ability of using point-based drone spectroscopy to determine substrate cover through spectral unmixing on a portion of Heron Reef, Australia. A spectral mixture analysis was conducted to separate the components contributing to spectral signatures obtained across the reef. The pure spectra used to unmix measured data include live coral, algae, sand, and rock, obtained from a public spectral library. These were able to account for over 82% of the spectral mixing captured in each spectroscopy measurement, highlighting the benefits of using a public database. The unmixing results were then compared to a categorical classification on an overlapping mosaicked drone image but yielded inconclusive results due to challenges in co-registration. This study uniquely showcases the potential of using commercial-grade drones and point spectroscopy in mapping complex environments. This can pave the way for future research, by increasing access to repeatable, effective, and affordable technology. Full article
(This article belongs to the Collection Feature Papers of Drones)
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Article
SeeCucumbers: Using Deep Learning and Drone Imagery to Detect Sea Cucumbers on Coral Reef Flats
Drones 2021, 5(2), 28; https://doi.org/10.3390/drones5020028 - 16 Apr 2021
Viewed by 1170
Abstract
Sea cucumbers (Holothuroidea or holothurians) are a valuable fishery and are also crucial nutrient recyclers, bioturbation agents, and hosts for many biotic associates. Their ecological impacts could be substantial given their high abundance in some reef locations and thus monitoring their populations [...] Read more.
Sea cucumbers (Holothuroidea or holothurians) are a valuable fishery and are also crucial nutrient recyclers, bioturbation agents, and hosts for many biotic associates. Their ecological impacts could be substantial given their high abundance in some reef locations and thus monitoring their populations and spatial distribution is of research interest. Traditional in situ surveys are laborious and only cover small areas but drones offer an opportunity to scale observations more broadly, especially if the holothurians can be automatically detected in drone imagery using deep learning algorithms. We adapted the object detection algorithm YOLOv3 to detect holothurians from drone imagery at Hideaway Bay, Queensland, Australia. We successfully detected 11,462 of 12,956 individuals over 2.7ha with an average density of 0.5 individual/m2. We tested a range of hyperparameters to determine the optimal detector performance and achieved 0.855 mAP, 0.82 precision, 0.83 recall, and 0.82 F1 score. We found as few as ten labelled drone images was sufficient to train an acceptable detection model (0.799 mAP). Our results illustrate the potential of using small, affordable drones with direct implementation of open-source object detection models to survey holothurians and other shallow water sessile species. Full article
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Article
UAVs Trajectory Optimization for Data Pick Up and Delivery with Time Window
Drones 2021, 5(2), 27; https://doi.org/10.3390/drones5020027 - 16 Apr 2021
Viewed by 558
Abstract
Unmanned Aerial Vehicles (UAVs), also known as drones, are a class of aircraft without the presence of pilots on board. UAVs have the ability to reduce the time and cost of deliveries and to respond to emergency situations. Currently, UAVs are extensively used [...] Read more.
Unmanned Aerial Vehicles (UAVs), also known as drones, are a class of aircraft without the presence of pilots on board. UAVs have the ability to reduce the time and cost of deliveries and to respond to emergency situations. Currently, UAVs are extensively used for data delivery and/or collection to/from dangerous or inaccessible sites. However, trajectory planning is one of the major UAV issues that needs to be solved. To address this question, we focus in this paper on determining the optimized routes to be followed by the drones for data pickup and delivery with a time window with an intermittent connectivity network, while also having the possibility to recharge the drones’ batteries on the way to their destinations. To do so, we formulated the problem as a multi-objective optimization problem, and we showed how to use the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve this problem. Several experiments were conducted to validate the proposed algorithm by considering different scenarios. Full article
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Article
Hybrid LoRa-IEEE 802.11s Opportunistic Mesh Networking for Flexible UAV Swarming
Drones 2021, 5(2), 26; https://doi.org/10.3390/drones5020026 - 15 Apr 2021
Viewed by 736
Abstract
Unmanned Aerial Vehicles (UAVs) and small drones are nowadays being widely used in heterogeneous use cases: aerial photography, precise agriculture, inspections, environmental data collection, search-and-rescue operations, surveillance applications, and more. When designing UAV swarm-based applications, a key “ingredient” to make them effective is [...] Read more.
Unmanned Aerial Vehicles (UAVs) and small drones are nowadays being widely used in heterogeneous use cases: aerial photography, precise agriculture, inspections, environmental data collection, search-and-rescue operations, surveillance applications, and more. When designing UAV swarm-based applications, a key “ingredient” to make them effective is the communication system (possible involving multiple protocols) shared by flying drones and terrestrial base stations. When compared to ground communication systems for swarms of terrestrial vehicles, one of the main advantages of UAV-based communications is the presence of direct Line-of-Sight (LOS) links between flying UAVs operating at an altitude of tens of meters, often ensuring direct visibility among themselves and even with some ground Base Transceiver Stations (BTSs). Therefore, the adoption of proper networking strategies for UAV swarms allows users to exchange data at distances (significantly) longer than in ground applications. In this paper, we propose a hybrid communication architecture for UAV swarms, leveraging heterogeneous radio mesh networking based on long-range communication protocols—such as LoRa and LoRaWAN—and IEEE 802.11s protocols. We then discuss its strengths, constraints, viable implementation, and relevant reference use cases. Full article
(This article belongs to the Special Issue Mobile Fog and Edge Computing in Drone Swarms)
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Article
A New Method for High Resolution Surface Change Detection: Data Collection and Validation of Measurements from UAS at the Nevada National Security Site, Nevada, USA
Drones 2021, 5(2), 25; https://doi.org/10.3390/drones5020025 - 14 Apr 2021
Viewed by 549
Abstract
The use of uncrewed aerial systems (UAS) increases the opportunities for detecting surface changes in remote areas and in challenging terrain. Detecting surface topographic changes offers an important constraint for understanding earthquake damage, groundwater depletion, effects of mining, and other events. For these [...] Read more.
The use of uncrewed aerial systems (UAS) increases the opportunities for detecting surface changes in remote areas and in challenging terrain. Detecting surface topographic changes offers an important constraint for understanding earthquake damage, groundwater depletion, effects of mining, and other events. For these purposes, changes on the order of 5–10 cm are readily detected, but sometimes it is necessary to detect smaller changes. An example is the surface changes that result from underground explosions, which can be as small as 3 cm. Previous studies that described change detection methodologies were generally not aimed at detecting sub-5-cm changes. Additionally, studies focused on high-fidelity accuracy were either computationally modeled or did not fully provide the necessary examples to highlight the usability of these workflows. Detecting changes at this threshold may be critical in certain applications, such as global security research and monitoring for high-consequence natural hazards, including landslides. Here we provide a detailed description of the methodology we used to detect 2–3 cm changes in an important applied research setting—surface changes related to underground explosions. This methodology improves the accuracy of change detection data collection and analysis through the optimization of pre-field planning, surveying, flight operations, and post-processing the collected data, all of which are critical to obtaining the highest output data resolution possible. We applied this methodology to a field study location, collecting 1.4 Tb of images over the course of 30 flights, and location data for 239 ground control points (GCPs). We independently verified changes with orthoimagery, and found that structure-from-motion, software-reported root mean square errors (RMSEs) for both control and check points underestimated the actual error. We found that 3 cm changes are detectable with this methodology, thereby improving our knowledge of a rock’s response to underground explosions. Full article
(This article belongs to the Special Issue Drone-Based Photogrammetric Mapping for Change Detection)
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Article
Biomimetic Drones Inspired by Dragonflies Will Require a Systems Based Approach and Insights from Biology
Drones 2021, 5(2), 24; https://doi.org/10.3390/drones5020024 - 27 Mar 2021
Viewed by 1580
Abstract
Many drone platforms have matured to become nearly optimal flying machines with only modest improvements in efficiency possible. “Chimera” craft combine fixed wing and rotary wing characteristics while being substantially less efficient than both. The increasing presence of chimeras suggests that their mix [...] Read more.
Many drone platforms have matured to become nearly optimal flying machines with only modest improvements in efficiency possible. “Chimera” craft combine fixed wing and rotary wing characteristics while being substantially less efficient than both. The increasing presence of chimeras suggests that their mix of vertical takeoff, hover, and more efficient cruise is invaluable to many end users. We discuss the opportunity for flapping wing drones inspired by large insects to perform these mixed missions. Dragonflies particularly are capable of efficiency in all modes of flight. We will explore the fundamental principles of dragonfly flight to allow for a comparison between proposed flapping wing technological solutions and a flapping wing organism. We chart one approach to achieving the next step in drone technology through systems theory and an appreciation of how biomimetics can be applied. New findings in dynamics of flapping, practical actuation technology, wing design, and flight control are presented and connected. We show that a theoretical understanding of flight systems and an appreciation of the detail of biological implementations may be key to achieving an outcome that matches the performance of natural systems. We assert that an optimal flapping wing drone, capable of efficiency in all modes of flight with high performance upon demand, might look somewhat like an abstract dragonfly. Full article
(This article belongs to the Collection Feature Papers of Drones)
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