Cooperative localization based on the azimuth angles among multiple UAVs

Unmanned aerial vehicles (UAVs) are an emerging technology for 3D Earth modelling and mapping. UAVs collect aerial images that are used to generate orthophotos with the assistance of navigation systems such as the Global Positioning System (GPS) and telemetry monitors at the Ground Control Station (GSC) for monitoring and controlling the UAV in autonomous flight. However, precision is critical in UAV photogrammetry mapping (PMg) to present acceptable precision measurements of the captured images that closely related to Ground Sample Distance (GSD) as most survey works are gradually shifting away from conventional surveying equipment such as total station and theodolite which has good precision measurement. This study focuses on the application of UAVs at close-range topography and mapping of land slope. Flight through the slope will vary in terms of flying altitude, affecting the GSD’s value for each slope height. The accuracy of PMg for land slope is subsequently assessed by evaluating the accuracy of slope mapping, particularly the slope height derived from Digital Elevation Model (DEM), Digital Surface Model (DSM) of orthophotos and Global Navigation Satellite System (GNSS) survey. This is due to the fact that most PMg is done on almost exclusively horizontal areas, and the GSD change value acquired remains the same. The purpose of this study is to analyze the accuracy of PMg of land slope by land slope mapping at 100m flight high above ground level. the RMSE recorded for x and y coordinate were (RMSEx = 0.654198cm) and (RMSEy = 0.668516cm) while for elevation error was (RMSEz = 0.512062cm). This study’s output is to help and assist authorities in construction planning, particularly during the cutting and filling process; to determine the precise amount of earth required for construction; and control the cost of earthwork.

Abstract. The rapid development of unmanned aerial vehicles is a challenge for applied research. Many technologies are developed and then researcher are looking up for their application in different sectors. Therefore, we decided to verify the use of the unmanned aerial vehicle for wood chips pile monitoring. We compared the use of GNSS device and unmanned aerial vehicle for volume estimation of four wood chips piles. We used DJI Phantom 3 Professional with the built-in camera and GNSS device (geoexplorer 6000). We used Agisoft photoscan for processing photos and ArcGIS for processing points. Volumes calculated from pictures were not statistically significantly different from amounts calculated from GNSS data and high correlation between them was found (p = 0.9993). We conclude that the use of unmanned aerial vehicle instead of the GNSS device does not lead to significantly different results. Tthe data collection consumed from almost 12 to 20 times less time with the use of UAV. Additionally, UAV provides documentation trough orthomosaic.

The use of unmanned aerial vehicles (UAVs) is becoming increasingly widespread in many areas of human life every year. Not only military, but also civilian use of UAVs in various fields of science, industry, forestry, agronomy, cartography, ecology, and space research is hard to overestimate. For this reason, the use and development of UAVs in various fields remains extremely promising in the near future. Compared to manned vehicles, UAVs have the following advantages: no crew and no need for life support systems, large airfields; lower cost and low expenses for their development, production and operation; better weight and dimensions; high reliability and maneuverability; a wide range of specialized equipment for placement on board. UAVs are being introduced into all spheres of human life, even those where UAVs have no manned alternative. Some models of multicopters are already being used to deliver goods and extinguish fires. UAVs of new designs continue to be developed.In all areas of UAV application, the task of increasing autonomy remains relevant and important. The duration of con-tinuous operation and flight range directly affect the capabilities of the UAV, its ability to perform tasks and efficiency.UAV power consumption depends on the weight of the payload, weather conditions and drone speed. Energy consumption is a critical criterion for a drone to perform its missions and needs to be further studied. Many studies have been conducted to increase flight time by improving energy efficiency or reducing the energy consumed by the drone.Study the influence of battery parameters (capacity and weight) and propeller group (number of propellers and their diameter) on the maximum possible flight duration of UAVs of different masses.The main factors affecting the maximum flight duration of a UAV are considered. A model for calculating the power consumed by the device in the hover mode is presented. The maximum flight duration of a UAV is calculated depending on the battery capacity and weight, the number of propellers, and the diameter of the propellers for vehicles of different masses.It is shown that there is no point in increasing the battery capacity indefinitely, and there is an optimum point that depends on the ratio of the battery mass to the mass of the vehicle. It is also shown that increasing the diameter of the propellers significantly increases the flight time, while increasing their number is not as effective.

In the developing world, the trend of using Unmanned Aerial Vehicles (UAVs) is being increased in many fields. These UAVs are now available in many different versions which ranges from children toy to military level combat equipment. Manufacturing companies are mostly mainly paying attention on reducing power consumption, high data transfer rate, less weight and more speed. However, by keeping focus on these features, most of the UAVs manufacturing companies overlook the factor of security and the risk in case of exploitation is ignored. This fact cannot be ignored that an exploited/hacked UAV with malicious intent can cause serious harm to the target in many ways. In the UAVs development, focus on the less computation power, restricted correspondence data transfer capacity, light weight data carrying limit and robustness captivity in UAVs has become a challenge for security practitioners and researchers. UAVs use numerous communication protocols and one among these protocols is Micro Air Vehicle (MAV) link protocol which is reliable as well as efficient. Currently, there are two versions of MAVlink communication protocol called MAVlink1 and MAVlink2. This protocol is used to establish a communication between Ground Control Station and UAV. The second version of this protocol (i.e. MAVlink2) is recent and there is not much data available about the potential exploitable vulnerabilities present in this protocol. This research paper has explored and identified security vulnerabilities in MAVlink2, specifically linked to the communication of UAV and their impact on UAV. The paper has further analyzed MAVlink2 communication protocol in order to exploit the vulnerabilities in the MAVlink2 protocol for hijacking the UAV.

Floating covers used in waste water treatment plants are one of the many structures formed with membrane materials. These structures are usually large and can spread over an area measuring 470 m × 170 m. The aim of this paper is to describe recent work to develop an innovative and effective approach for structural health monitoring (SHM) of such large membrane-like infrastructure. This paper will propose a potentially cost-effective non-contact approach for full-field strain and stress mapping using an unmanned aerial vehicle (UAV) mounted with a digital camera and a global positioning system (GPS) tracker. The aim is to use the images acquired by the UAV to define the geometry of the floating cover using photogrammetry. In this manner, any changes in the geometry of the floating cover due to forces acting beneath resulting from its deployment and usage can be determined. The time-scale for these changes is in terms of weeks and months. The change in the geometry can be implemented as input conditions to a finite element model (FEM) for stress prediction. This will facilitate the determination of the state of distress of the floating cover. This paper investigates the possibility of using data recorded from a UAV to predict the strain level and assess the health of such structures. An investigation was first conducted on a laboratory sized membrane structure instrumented with strain gauges for comparison against strains, which were computed from 3D scans of the membrane geometry. Upon validating the technique in the laboratory, it was applied to a more realistic scenario: an outdoor test membrane structure and capable UAV were constructed to see if the shape of the membrane could be computed. The membrane displacements were then used to calculate the membrane stress and strain, state demonstrating a new way to perform structural health monitoring on membrane structures.

In this paper, the effective use of flight-time constrained unmanned aerial vehicles (UAVs) as flying base stations that provide wireless service to ground users is investigated. In particular, a novel framework for optimizing the performance of such UAV-based wireless systems in terms of the average number of bits (data service) transmitted to users as well as the UAVs' hover duration (i.e. flight time) is proposed. In the considered model, UAVs hover over a given geographical area to serve ground users that are distributed within the area based on an arbitrary spatial distribution function. In this case, two practical scenarios are considered. In the first scenario, based on the maximum possible hover times of UAVs, the average data service delivered to the users under a fair resource allocation scheme is maximized by finding the optimal cell partitions associated to the UAVs. Using the powerful mathematical framework of optimal transport theory, this cell partitioning problem is proved to be equivalent to a convex optimization problem. Subsequently, a gradient-based algorithm is proposed for optimally partitioning the geographical area based on the users' distribution, hover times, and locations of the UAVs. In the second scenario, given the load requirements of ground users, the minimum average hover time that the UAVs need for completely servicing their ground users is derived. To this end, first, an optimal bandwidth allocation scheme for serving the users is proposed. Then, given this optimal bandwidth allocation, optimal cell partitions associated with the UAVs are derived by exploiting the optimal transport theory. Simulation results show that our proposed cell partitioning approach leads to a significantly higher fairness among the users compared with the classical weighted Voronoi diagram. Furthermore, the results demonstrate that the average hover time of the UAVs can be reduced by 64% by adopting the proposed optimal bandwidth allocation scheme as well as the optimal cell partitioning approach. In addition, our results reveal an inherent tradeoff between the hover time of UAVs and bandwidth efficiency while serving the ground users.

Pesticide spray drift and risk assessment using unmanned aerial vehicle (UAV) sprayer and traditional electric knapsack sprayer (EKS).

The paper discusses the ways to increase the effectiveness and efficiency of green health care by using Unmanned Aerial Vehicle (UAV) technology. Using this technology at the center of care can significantly reduce costs and increase the effectiveness of a modern hospital’s services while enhancing efficiency and emphasizing environmental sustainability. This paper introduces UAVs as the future efficient method of real-time data transmission and trains them according to the circumstances to work more efficiently than manpower with better security. This can be the most significant method in the case of emergency handling. Where manually can’t be reached UAVs can solve the problem of bandwidth and transmitting data from lower to higher bandwidth. UAVs can be pre-trained and show acceptable accuracy to ensure green health care. By using the MobileNet model, the paper has implemented a mask detection process. It suggests a method in which UAVs are used to transport biological material from smaller hospitals and clinics to larger hospitals without any physical movement. The paper has discussed the UAV workflow and the UAV training process and specifically aims to show how technology can reduce inefficiencies caused by the under-utilization of professional resources and assets, as well as transportation.

This paper proposes a transmission method using unmanned aerial vehicle (UAV) with distributed Space-Time Block Code (STBC) for multi-hop wireless relay networks in search and rescue operations. First, an UAV is considered to add to the hop with the minimum output signal-noise-ratio (SNR) and operates as a relay node to maintain the links between adjacency nodes in network, expand the transmission coverage area and improve the transmission performance. In addition, in order to overcome the difficulty in assigning the STBC patterns to the distributed relays and also alleviate the complexity of system design and implementation, the original STBC pattern is modified while keeping the same cooperative diversity gain. Finally, an algorithm is proposed to find out the optimal location of the added UAV in the hop, where the UAV has the best contribution to the data transmission performance between the transmitter and the receiver. It can be seen from the simulation results, the optimal location of the added UAV depends on not only the environment of real scenarios but also the distributed cooperative diversity gain. We can confirm that the proposed method achieves the significant performance improvement while keeping the simple operation of system for UAV communications in search and rescue operations.

The use of intelligent transportation systems (ITSs) is expanding, and vehicle-to-vehicle communication (V2V) and vehicle-to-infrastructure communication (V2I) have attracted considerable attention. During disasters, it becomes difficult to share information with vehicles in sparse areas and distant places. Therefore, we considered using unmanned aerial vehicle (UAVs) in ITSs during disasters. We developed a method to control the flight speed and modulation and coding scheme (MCS) of a UAV and maximize the number of vehicles that receive data in one transmission. A model to derive the optimal UAV's flight speed and MCS was constructed. Then, we confirmed that an optimal UAV's flight speed and MCS exist that maximize the number of vehicles receiving data in one transmission by the UAV by numerical analysis. In the evaluation using the flight speed and MCS derived from the model, the expected number of vehicles that received data in one transmission at each data size held by the UAV was derived and compared with other methods. From the evaluation, we confirmed that the proposed method maximizes the number of vehicles receiving data in one transmission by the UAV for each data size maintained by the UAV.

In scenarios such as environmental data collection and traffic monitoring, timely responses to real-time situations are facilitated by persistently accessing nodes with revisiting constraints using unmanned aerial vehicles (UAVs). However, imbalanced task allocation may pose risks to the safety of UAVs and potentially lead to failures in monitoring tasks. For instance, continuous visits to nodes without replenishment may damage UAV batteries, while delays in recharging could result in missing task deadlines, ultimately causing task failures. Therefore, this study investigates the problem of achieving balanced multi-UAV path planning for persistent monitoring tasks, which has not been previously researched according to the authors’ knowledge. The main contribution of this study is the proposal of two novel indicators to assist in balancing task allocation regarding multi-UAV path planning for persistent monitoring. One of the indicators is namely the waiting factor, which reflects the urgency of a task node waiting to be accessed, and the other is the difficulty level which is introduced to measure the difficulty of tasks undertaken by a UAV. By minimizing differences in difficulty level among UAVs, we can ensure equilibrium in task allocation. For a single UAV, the ant colony initialized genetic algorithm (ACIGA) has been proposed to plan its path and obtain its difficulty level. For multiple UAVs, the K-means clustering algorithm has been improved based on difficulty levels to achieve balanced task allocation. Simulation experiments demonstrated that the difficulty level could effectively reflect the difficulty of tasks and that the proposed algorithms could enable UAVs to achieve balanced task allocation.

Automated Papaya Farm Monitoring system using Unmanned Aerial Vehicle (UAV)

Historic buildings have been damaged by natural events and human hands for centuries. For this reason, the importance of studies on the preservation, documentation and restoration of historical buildings is increasing worldwide. In addition, technological opportunities are also becoming necessary for carrying out more comprehensive studies on the sustainability of historical buildings. Studies on modelling many cultural heritage (CH) works worldwide continue. Precise data about the structure must be obtained to evaluate these models in reconstruction studies. In parallel with technological developments, the measurement sensitivity of devices in this field has also increased. In addition, different technological tools that will assist in measurement activities are entering our lives daily. Unmanned Aerial Vehicle (UAV) systems are one of them. 3D data from UAVs and photogrammetric systems are sensitive data obtained quickly and cheaply. This data provides the opportunity to be used in interdisciplinary studies. This study investigates the use of UAVs in the digital documentation study of the Ancient City of Kilistra in Konya/Gökyurt. The study showed that UAVs are more practical and effective than traditional measurement techniques. It has been revealed that, if appropriate conditions are provided, UAVs can be a suitable alternative in terms of time and cost in documenting CH sites. Finally, it has been determined that the high-resolution digital maps obtained not only speed up the process of documenting large areas but also provide advantages in accuracy and precision.

With changing climate and farmland ecological conditions, pest outbreaks in agricultural landscapes are becoming more frequent, increasing the need for improved crop production tools and methods. UAV-based agricultural spraying is anticipated to be an important new technology for providing efficient and effective applications of crop protection products. This paper reviews and summarizes the status of the current research and progress on UAV application technologies for plant protection, and it discusses the characteristics of atomization by unmanned aircraft application systems with a focus on spray applications of agrichemicals. Additionally, the factors influencing the spraying performance including downwash airflow field and operating parameters are analyzed, and a number of key technologies for reducing drift and enhancing the application efficiency such as remote sensing, variable-rate technologies, and spray drift models are considered. Based on the reviewed literature, future developments and the impacts of these UAV technologies are projected. This review may inspire the innovation of the combined use of big data analytics and UAV technology, precision agricultural spraying technology, drift reduction technology, swarm UAV cooperative technology, and other supporting technologies for UAV-based aerial spraying for scientific research in the world. Keywords: UAV, plant protection, spraying technology, drift reduction, pesticide efficacy, spraying model, big data analytics DOI: 10.25165/j.ijabe.20211401.5714 Citation: Chen H B, Lan Y B, Fritz B K, Hoffmann W C, Liu S B. Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle (UAV). Int J Agric & Biol Eng, 2021; 14(1): 38–49.

PAveMENT: a framework for the intelligent and safe navigation of unmanned aerial vehicles over optimal PAths in MobilE NeTworks