Use Of Drones Research Articles

Humanzentrierte Implementierung von (teil-)autonomen Drohnen

Abstract The industrial use of drones is constantly increasing due to the transition from Industry 4.0 to Industry 5.0. A prerequisite for the concrete implementation is the legal and organizational risk assessment of flight robotics. The core of the article is a systematic overview of relevant human-centered risk factors for the adaptation of drones in organizations. Based on the proposed risk taxonomy, design options for human-drone interaction and an overview of key questions for risk assessment are presented.

COMPARATIVE ANALYSIS OF LOW-COST CONSUMER-GRADE AND COMMERCIAL-GRADE DRONES FOR LAND SURVEYING APPLICATIONS

Abstract. This study investigates the feasibility of using low-cost consumer drones, specifically the DJI Mini 2, for land surveys compared to commercial-grade drones like the DJI Phantom 4 Pro V2. The research, conducted within the University of the Philippines - Diliman campus, evaluates the horizontal accuracy of resulting ortho-images and explores the advantages and disadvantages of each platform. Initiated by the recognition of aerial drones' transformative impact on land surveying, particularly acknowledged by the Land Management Bureau in the Philippines, the study addresses concerns about the use of consumer-grade drones for surveying. While some advocate for commercial-grade drones, citing concerns about accuracy, a prior study in India demonstrated the suitability of consumer-grade drones for geomatics applications. The methodology involves creating a flight plan with specified parameters for both drones, establishing Ground Control Points (GCPs), and conducting two flights. The results, processed using UAS Mapping Workflow, indicate comparable errors between the consumer-grade DJI Mini 2 and commercial-grade DJI Phantom 4 Pro V2. Despite slight differences, both drones meet the accuracy standards for land surveys. The study contributes to the discourse on affordable means of secure land tenure, envisioning consumer-grade drones as viable alternatives for cost-effective land surveys. The DJI Mini 2, at a fraction of the cost, demonstrates the capability to achieve accuracy levels comparable to its commercial counterpart. While acknowledging the need for further testing and optimization, the findings suggest the potential of consumer-grade drones in advancing sustainable urban development and poverty reduction through accessible land survey methods.

Drone-Based Bug Detection in Orchards with Nets: A Novel Orienteering Approach

The use of drones for collecting information and detecting bugs in orchards covered by nets is a challenging problem. The nets help in reducing pest damage, but they also constrain the drone’s flight path, making it longer and more complex. To address this issue, we model the orchard as an aisle-graph, a regular data structure that represents consecutive aisles where trees are arranged in straight lines. The drone flies close to the trees and takes pictures at specific positions for monitoring the presence of bugs, but its energy is limited, so it can only visit a subset of positions. To tackle this challenge, we introduce the Single-drone Orienteering Aisle-graph Problem (SOAP), a variant of the orienteering problem, where likely infested locations are prioritized by assigning them a larger profit. Additionally, the drone’s movements have a cost in terms of energy, and the objective is to plan a drone’s route in the most profitable locations under a given drone’s battery. We show that SOAP can be optimally solved in polynomial time, but for larger orchards/instances, we propose faster approximation and heuristic algorithms. Finally, we evaluate the algorithms on synthetic and real datasets to demonstrate their effectiveness and efficiency.

Risk Analysis of the Use of Drones in City Logistics

Drone delivery in city logistics is gaining attention due to road congestion, environmental threats, etc. However, there are risks associated with using drones which can result in hazardous events, such as conflicts in the air, loss of control, and system failures. It is crucial to assess the risks involved in using different types of drones and choose the option with the lowest risk. The existence of different criteria important for this decision imposes the need to apply the multi-criteria decision-making (MCDM) method(s). This paper proposes a new hybrid model that combines the fuzzy Factor Relationship (FARE) method for obtaining the criteria weights and the Axial Distance-based Aggregated Measurement (ADAM) method for obtaining the final ranking of the alternatives. A single-rotor microdrone weighing up to 4.4 lb was chosen as the optimal solution, and after that, the most favorable are also the drones of this size (multi-rotor and fixed-wing microdrones). The establishment of a novel hybrid MCDM model, the identified risks, the set of criteria for evaluating the least risky drones, and the framework for prioritizing the drones are the main novelties and contributions of the paper.

First use of unmanned aerial vehicles to monitor Halyomorpha halys and recognize it using artificial intelligence.

Halyomorpha halys is one of the most damaging invasive agricultural pests in North America and southern Europe. It is commonly monitored using pheromone traps, which are not very effective because few bugs are caught and some escape and/or remain outside the trap on surrounding plants where they feed, increasing the damage. Other monitoring techniques are based on visual sampling, sweep-netting and tree-beating. However, all these methods require several hours of human labor and are difficult to apply to large areas. The aim of this work is to develop an automated monitoring system that integrates image acquisition through the use of drones with H. halys detection through the use of artificial intelligence (AI). The study results allowed the development of an automated flight protocol using a mobile app to capture high-resolution images. The drone caused only low levels of disturbance in both adult and intermediate instars, inducing freezing behavior in adults. Each of the AI models used achieved very good performance, with a detection accuracy of up to 97% and recall of up to 87% for the X-TL model. The first application of this novel monitoring system demonstrated the potential of drones and AI to detect and quantify the presence of H. halys. The ability to capture high-altitude, high-resolution images makes this method potentially suitable for use with a range of crops and pests. © 2024 Society of Chemical Industry.

Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks.

The use of drones has recently gained popularity in a diverse range of applications, such as aerial photography, agriculture, search and rescue operations, the entertainment industry, and more. However, misuse of drone technology can potentially lead to military threats, terrorist acts, as well as privacy and safety breaches. This emphasizes the need for effective and fast remote detection of potentially threatening drones. In this study, we propose a novel approach for automatic drone detection utilizing the usage of both radio frequency communication signals and acoustic signals derived from UAV rotor sounds. In particular, we propose the use of classical and deep machine-learning techniques and the fusion of RF and acoustic features for efficient and accurate drone classification. Distinct types of ML-based classifiers have been examined, including CNN- and RNN-based networks and the classical SVM method. The proposed approach has been evaluated with both frequency and audio features using common drone datasets, demonstrating better accuracy than existing state-of-the-art methods, especially in low SNR scenarios. The results presented in this paper show a classification accuracy of approximately 91% at an SNR ratio of -10 dB using the LSTM network and fused features.

Public Opinion and Drone Exports: Evidence from a Conjoint Experiment in the United States

Abstract Drones are now a mainstay in war. Yet not all countries can manufacture armed and networked drones, heightening global demand. While some drone-manufacturing countries have adopted permissive export policies, U.S. policymakers have been more restrictive, citing international legal commitments and domestic export controls. In this study, I consider drone exports as a question of both political economy and public opinion. I derive and test hypotheses about public support for drone exports through a conjoint survey experiment administered in the United States. I find that perceived alignment between the recipient country and United States, as well as the intended use of drones, are the most important factors shaping public support for drone exports. Export control measures are less relevant to public opinion. These findings offer the first experimental evidence about public opinion for drone exports, which have implications for the political economy of security and policies relating to the export of military technologies.

Analysis of Artificial Intelligence Induced Machines to Improve the Fiscal Advancement of Agronomy

Horticulture became a key player in the development of UT. The total land area of Jammu and Kashmir under horticulture is 332704, among which 214162 in the Kashmir valley and 118542 in the Jammu region. The district Baramulla has achieved first rank in production in apples with 404089 metric tons, and also covers an area of 25231 hectares. Horticulture of UT of Jammu and Kashmir contributes 8-10% contribution to the SGDP (DHK, 2021). In 48 years, the area under horticulture shows a growth rate of 25%.The growth of production of horticulture has also increased 10 thousand metric to 25 Lakh metric tons during 1950-2022.In this paper we will analysis growth in production, productivity, area and State Gross Domestic Product through the Compound Growth Rate from 2012 to 2022. Artificial Intelligence will become a game changer for horticulture sector, which is facing loses consistently from the last few years. Need of the hour to improve the technique like artificial intelligence and procedures for the betterment of horticulture, especially open national, use of drones in farms, highway 44 during the peak season.

Secure malicious node detection in flying ad-hoc networks using enhanced AODV algorithm

In wireless networking, the security of flying ad hoc networks (FANETs) is a major issue, and the use of drones is growing every day. A distributed network is created by a drone network in which nodes can enter and exit the network at any time. Because malicious nodes generate bogus identifiers, FANET is unstable. In this research study, we proposed a threat detection method for detecting malicious nodes in the network. The proposed method is found to be most effective compared to other methods. Malicious nodes fill the network with false information, thereby reducing network performance. The secure ad hoc on-demand distance vector (AODV) that has been suggested algorithm is used for detecting and isolating a malicious node in FANET. In addition, because temporary flying nodes are vulnerable to attacks, trust models based on direct or indirect reliability similar to trusted neighbors have been incorporated to overcome the vulnerability of malicious/selfish harassment. A node belonging to the malicious node class is disconnected from the network and is not used to forward or forward another message. The FANET security performance is measured by throughput, packet loss and routing overhead with the conventional algorithms of AODV (TAODV) and reliable AODV secure AODV power consumption decreased by 16.5%, efficiency increased by 7.4%, and packet delivery rate decreased by 9.1% when compared to the second ranking method. Reduced packet losses and routing expenses by 9.4%. In general, the results demonstrate that, in terms of energy consumption, throughput, delivered packet rate, the number of lost packets, and routing overhead, the proposed secure AODV algorithm performs better than the most recent, cutting-edge algorithms.

A smart filtering-based adaptive optimized link state routing protocol in flying ad hoc networks for traffic monitoring

Nowadays, the use of drones as a fundamental element of smart cities has attracted the attention of many researchers to monitor and control the traffic of vehicles. Because of the high flexibility of multi-drone systems, like flying ad hoc networks (FANETs), they provide various services and improve modern life in smart cities. However, due to the unique features of FANET, especially the high speed of drones and rapid changes in network topology, communication reliability is a serious challenge in this network. Hence, traditional routing protocols, such as optimized link state routing (OLSR) scheme, cannot work well in these networks. In this paper, a smart filtering-based adaptive optimized link state routing (SFA-OLSR) scheme is proposed in FANETs. To increase adaptability to the FANET environment, SFA-OLSR provides a new solution to adjust the hello broadcast period so that each flying node specifies its broadcast period based on a new scale called cosine similarity between real and predicted positions. Furthermore, in SFA-OLSR, each flying node develops a filtering algorithm based on two parameters, namely link lifetime and remaining energy. The purpose of this algorithm is to reduce the size of the single-hop neighboring set of each flying node and minimize the search space when finding multi-point relays (MPRs). This increases the convergence speed of the algorithm. Then, SFA-OLSR exploits the sparrow search algorithm (SSA) to single out the best MPRs. This algorithm introduces a multi-objective function by focusing on three components, including energy, link lifespan, and neighbor degree. Lastly, the simulation process of SFA-OLSR is performed by the NS3 simulator. This process evaluates the performance of the proposed method and three schemes, namely Gangopadhyay et al., P-OLSR, and OLSR-ETX. These evaluations show that SFA-OLSR has a good performance in terms of three scales, namely packet delivery ratio, delay, and throughput, but its overhead is more than other methods.

Flying high for conservation: Opportunities and challenges of operating drones within the oldest National Park in the Alps

Abstract Drones have emerged as an essential tool in various conservation applications. Despite the great potential, drone use by protected area managers is still scarce or accompanied by scepticism. The ongoing debate revolves around whether drones are fancy gadgets or if they can effectively guide management strategies and align with the overarching goals of protected areas. Here, we present a practical overview of how novel drone applications contribute to the goals of the oldest national park in the Alps, along with the associated challenges. To do so, we review our seven‐year experience as park employees flying drones within the park and its surroundings. First, we provide background information receiving limited attention in the existing literature such as our motivation behind a drone purchase, costs and an overview of our flight operations. Second, we show three examples that demonstrate the potential of drones as a valuable tool in addressing the goals of the park: managing the area, researching natural processes and facilitating communication. Third, we reflect on operational challenges and provide valuable lessons for addressing the specific challenges of flying drones in an alpine and protected environment. Practical implication: Our experience supports the benefits of drones for protected area management, but it also highlights the need for certain precautions, increased focus on operational challenges and further research on wildlife‐drone interactions.

Privadome: Delivery Drones and Citizen Privacy

E-commerce companies are actively considering the use of delivery drones for customer fulfillment, leading to growing concerns around citizen privacy. Drones are equipped with cameras, and the video feed from these cameras is often required as part of routine navigation, be it for semi-autonomous or fully-autonomous drones. Footage of ground-based citizens captured in these videos may lead to privacy concerns. This paper presents Privadome, a system that implements the vision of a virtual privacy dome centered around the citizen. Privadome is designed to be integrated with city-scale regulatory authorities that oversee delivery drone operations and realizes this vision through two components, PD-MPC and PD-ROS. PD-MPC allows citizens equipped with a mobile device to identify drones that have captured their footage. It uses secure two-party computation to achieve this goal without compromising the privacy of the citizen’s location. PD-ROS allows the citizen to communicate with such drones and obtain an audit trail showing how the drone uses their footage and determine if privacy-preserving steps are taken to sanitize the footage.

ISSUES OF OPTIMIZATION OF MONITORING OF WOODLANDS USING LOW-FLYING DRONES

The article is devoted to the optimization of the use of low-flying drones for monitoring forests. The adaptive mode of identification of trees in the forest using low-flying drones has been optimized. A target optimization functional has been formed that displays the average integral value of the frequency of alternation of image elements, i.e. black-and-white pixels in the field of view of the imaging radiometer. The adaptive control function is calculated, indicating the relationship between the flight altitude and the height of trees at which the target functional reaches a minimum. A recommendation is made to avoid this mode in the practice of using drones when monitoring forest areas.

Applications of Drone and Unmanned Aerial Vehicle (UAV) Surveying for Planning For Cities

Abstract: The field of town planning is one of the many areas in city life where Unmanned Aerial Vehicles (UAVs) and drones could find use. When compared to traditional surveying options, drones may offer planners a more accurate, precise, flexible, and economical means of obtaining information. Unfortunately, current Indian government restrictions around the use of drones, particularly in the town planning sector, are vague and prioritize safety above advancing technology toward a system that is more efficient. To maximize the public benefit from the use of drone technology in town planning, town planners and surveyors require flexible, practical drone regulations that address the numerous concerns surrounding the use of UAVs in urban areas. This research discusses the present applications of UAVs in many areas of urban planning.

Use of a drone equipped with microthermal sensors to estimate the quality of the atmosphere for astronomical observation

AbstractA drone was used to obtain thermal profiles to estimate atmospheric turbulence for the purposes of astronomical observation. The first 150 metres above ground level were studied in two areas in the south‐east of the Iberian Peninsula. The preliminary results are promising regarding the use of drones as a suitable means of transport for obtaining microthermal measurements. We have also assessed the influence of the planetary boundary layer on the quality of the image for astronomical purposes. In future research, we intend to check these measurements with fixed microthermal sensors installed in meteorological towers.

Analysis of Artificial Intelligence Induced Machines to Improve the Fiscal Advancement of agronomy

Horticulture became a key player in the development of UT. The total land area of Jammu and Kashmir under horticulture is 332704, among which 214162 in the Kashmir valley and 118542 in the Jammu region. The district Baramulla has achieved first rank in production in apples with 404089 metric tons, and also covers an area of 25231 hectares. Horticulture of UT of Jammu and Kashmir contributes 8-10% contribution to the SGDP (DHK, 2021). In 48 years, the area under horticulture shows a growth rate of 25%.The growth of production of horticulture has also increased 10 thousand metric to 25 Lakh metric tons during 1950-2022.In this paper we will analysis growth in production, productivity, area and State Gross Domestic Product through the Compound Growth Rate from 2012 to 2022. Artificial Intelligence will become a game changer for horticulture sector, which is facing loses consistently from the last few years. Need of the hour to improve the technique like artificial intelligence and procedures for the betterment of horticulture, especially open national, use of drones in farms, highway 44 during the peak season.

Research on Vision-Based Servoing and Trajectory Prediction Strategy for Capturing Illegal Drones

A proposed strategy for managing airspace and preventing illegal drones from compromising security involves the use of autonomous drones equipped with three key functionalities. Firstly, the implementation of YOLO-v5 technology allows for the identification of illegal drones and the establishment of a visual-servo system to determine their relative position to the autonomous drone. Secondly, an extended Kalman filter algorithm predicts the flight trajectory of illegal drones, enabling the autonomous drone to compensate in advance and significantly enhance the capture success rate. Lastly, to ensure system robustness and suppress interference from illegal drones, an adaptive fast nonsingular terminal sliding mode technique is employed. This technique achieves finite time convergence of the system state and utilizes delay estimation technology for the real-time compensation of unknown disturbances. The stability of the closed-loop system is confirmed through Lyapunov theory, and a model-based hardware-in-the-loop simulation strategy is adopted to streamline system development and improve efficiency. Experimental results demonstrate that the designed autonomous drone accurately predicts the trajectory of illegal drones, effectively captures them using a robotic arm, and maintains stable flight throughout the process.

Determining the height of cotton plants (Gossypium hirsutum L.) Malvaceae f. with the assistance of a drone

The use of drones in crop assessment has become increasingly common. The use of drones presents benefits for evaluating the height of plants in various crops, including cotton, checking nutritional, phytosanitary, genetic parameters, abiotic and biotic effects. The objective of this study was to evaluate the use of drones to aid in the evaluation of trials in cotton cultivation. The study was carried out at the Goiano Institute of Agriculture, located in the municipality of Montividiu, State of Goiás, Brazil. A Phantom 4 Pro drone was used to carry out the mapping and WebODM was used to carry out photogrammetry and obtain a digital model of the surface area in the cotton field. In the experimental design, 4 randomized blocks with 10 treatments were used. The results demonstrated that there was no difference between the four blocks for vegetative index. The digital surface model proved to be efficient in detecting possible differences between the analyzed blocks. This analysis is essential to understand variations in response to cotton cultivation in different blocks and identify possible factors that contribute to these differences.

Is the Artificial Pollination of Walnut Trees with Drones Able to Minimize the Presence of Xanthomonas arboricola pv. juglandis? A Review

Walnut (Juglans regia L.) is a monoecious species and although it exhibits self-compatibility, it presents incomplete overlap of pollen shed and female receptivity. Thus, cross-pollination is prerequisite for optimal fruit production. Cross-pollination can occur naturally by wind, insects, artificially, or by hand. Pollen has been recognized as one possible pathway for Xanthomonas arboricola pv. juglandis infection, a pathogenic bacterium responsible for walnut blight disease. Other than the well-known cultural and chemical control practices, artificial pollination technologies with the use of drones could be a successful tool for walnut blight disease management in orchards. Drones may carry pollen and release it over crops or mimic the actions of bees and other pollinators. Although this new pollination technology could be regarded as a promising tool, pollen germination and knowledge of pollen as a potential pathway for the dissemination of bacterial diseases remain crucial information for the development and production of aerial pollinator robots for walnut trees. Thus, our purpose was to describe a pollination model with fundamental components, including the identification of the “core” pollen microbiota, the use of drones for artificial pollination as a successful tool for managing walnut blight disease, specifying an appropriate flower pollination algorithm, design of an autonomous precision pollination robot, and minimizing the average errors of flower pollination algorithm parameters through machine learning and meta-heuristic algorithms.

Research on Multi-Sensor Data Collection and Mapping Methods

In today's information society, the importance of collecting data through multiple channels continues to increase. This article focuses on the use of drones for large-area forest information collection and tree analysis. In complex environments, there are a series of problems in obtaining tree mapping information with a single sensor, such as single use scenarios, low accuracy, limited viewing angles, and high requirements for computing power. To address these challenges, this paper proposes a Bayesian-based multi-sensor fusion strategy to overcome the limitations of a single sensor by integrating depth camera and lidar data. Experimental results show that the proposed method significantly improves the accuracy of mapping surrounding trees and the ability to collect information. The multi-sensor fusion strategy effectively reduces the problem of repeated data processing and improves the comprehensiveness of collected data. It meets the requirements of real-time performance and accurate collection of surrounding scenes.