Types Of Drones Research Articles

Intelligent Swarm: Concept, Design and Validation of Self-Organized UAVs Based on Leader–Followers Paradigm for Autonomous Mission Planning

Unmanned Aerial Vehicles (UAVs), commonly known as drones, are omnipresent and have grown in popularity due to their wide potential use in many civilian sectors. Equipped with sophisticated sensors and communication devices, drones can potentially form a multi-UAV system, also called an autonomous swarm, in which UAVs work together with little or no operator control. According to the complexity of the mission and coverage area, swarm operations require important considerations regarding the intelligence and self-organization of the UAVs. Factors including the types of drones, the communication protocol and architecture, task planning, consensus control, and many other swarm mobility considerations must be investigated. While several papers highlight the use cases for UAV swarms, there is a lack of research that addresses in depth the challenges posed by deploying an intelligent UAV swarm. Against this backdrop, we propose a computation framework of a self-organized swarm for autonomous and collaborative missions. The proposed approach is based on the Leader–Followers paradigm, which involves the distribution of ROS nodes among follower UAVs, while leaders perform supervision. Additionally, we have integrated background services that autonomously manage the complexities relating to task coordination, control policy, and failure management. In comparison with several research efforts, the proposed multi-UAV system is more autonomous and resilient since it can recover swiftly from system failure. It is also reliable and has been deployed on real UAVs for outdoor survey missions. This validates the applicability of the theoretical underpinnings of the proposed swarming concept. Experimental tests carried out as part of an area coverage mission with 6 quadcopters (2 leaders and 4 followers) reveal that the proposed swarming concept is very promising and inspiring for aerial vehicle technology. Compared with the conventional planning approach, the results are highly satisfactory, highlighting a significant gain in terms of flight time, and enabling missions to be achieved rapidly while optimizing energy consumption. This gives the advantage of exploring large areas without having to make frequent downtime to recharge and/or charge the batteries. This manuscript has the potential to be extremely useful for future research into the application of unmanned swarms for autonomous missions.

Recent Developments and Future Prospects in Collision Avoidance Control for Unmanned Aerial Vehicles (UAVS): A Review

The industry has been significantly enhanced by recent developments in UAV collision avoidance systems. They made collision avoidance controllers for self-driving drones both affordable and hazardous. These low-maintenance, portable devices provide continuous monitoring in near-real time. It is inaccurate due to the fact that collision avoidance controllers necessitate trade-offs regarding data reliability. Collision avoidance control research is expanding significantly and is disseminated through publications, initiatives, and grey literature. This paper provides a concise overview of the most recent research on the development of autonomous vehicle collision avoidance systems from 2017 to 2024. In this paper, the state-of-the-art collision avoidance system used in drone systems, the capabilities of the sensors used, and the distinctions between each type of drone are discussed. The pros and cons of current approaches are analyzed using seven metrics: complexity, communication dependency, pre-mission planning, resilience, 3D compatibility, real-time performance, and escape trajectories.

A Novel Approach to Detect Drones Using Deep Convolutional Neural Network Architecture.

Over the past decades, drones have become more attainable by the public due to their widespread availability at affordable prices. Nevertheless, this situation sparks serious concerns in both the cyber and physical security domains, as drones can be employed for malicious activities with public safety threats. However, detecting drones instantly and efficiently is a very difficult task due to their tiny size and swift flights. This paper presents a novel drone detection method using deep convolutional learning and deep transfer learning. The proposed algorithm employs a new feature extraction network, which is added to the modified YOU ONLY LOOK ONCE version2 (YOLOv2) network. The feature extraction model uses bypass connections to learn features from the training sets and solves the "vanishing gradient" problem caused by the increasing depth of the network. The structure of YOLOv2 is modified by replacing the rectified linear unit (relu) with a leaky-relu activation function and adding an extra convolutional layer with a stride of 2 to improve the small object detection accuracy. Using leaky-relu solves the "dying relu" problem. The additional convolution layer with a stride of 2 reduces the spatial dimensions of the feature maps and helps the network to focus on larger contextual information while still preserving the ability to detect small objects. The model is trained with a custom dataset that contains various types of drones, airplanes, birds, and helicopters under various weather conditions. The proposed model demonstrates a notable performance, achieving an accuracy of 77% on the test images with only 5 million learnable parameters in contrast to the Darknet53 + YOLOv3 model, which exhibits a 54% accuracy on the same test set despite employing 62 million learnable parameters.

Enhancing Healthcare Access: Drone-based Delivery of Medicines and Vaccines in hard-to-Reach Terrains of Northeastern India

Background: Ensuring efficient healthcare access in a geographically diverse country like India poses significant challenges, particularly in challenging terrains. Conventional transportation methods often encounter difficulties due to inadequate infrastructure and adverse climatic conditions, resulting in delays in medical supplies. This study aimed to assess the viability of drones for transporting various types of medical supplies across diverse geographical landscapes, also evaluating their impact on transportation time, covered distance, weight carrying capacities and safety of medical supplies. Materials and Methods: The study spanned 4 months across 19 healthcare centres across Manipur and Nagaland. Different types of drones were used for the transportation of vaccines, medications and surgical items across these centres covering terrains such as flatlands, valleys, foothills, islands and hills. Results: Drones were able to successfully transport the medical supplies maintaining the essential temperatures and integrity of the samples. Further, drone-based supplies significantly reduce the delivery time compared to conventional road-based delivery mechanisms. Conclusion: The present study demonstrates the potential application of drones for the delivery of healthcare supplies in remote areas. Despite the logistical challenges, drones offer a time-efficient alternative for medical supply delivery. However, there is a need for further research and logistical infrastructure development in the current field.

The Use of Drones in Tactical Military Operations in the Integrated and Cybernetic Battlefield

Abstract Drones have become an essential component of modern military arsenals, representing unmanned aerial vehicles capable of performing a variety of tasks, from surveillance and reconnaissance to precision strikes and assistance in military operations. This paper examines the classification and use of drones in the integrated and cybernetic battlefield, analyzing the existing types of drones and the advantages and disadvantages of their use. Various types of drones, their characteristics, capabilities, advantages, and limitations depending on the type and purpose of the operation are examined. The high flight autonomy, surveillance and reconnaissance capabilities, striking potential, precision, and reduced manufacturing and operating costs make drones a highly topical subject in military scientific research. The vulnerabilities and specific limitations of these new weapons, such as vulnerability to radar interference, adverse weather conditions, technological novelty, and doctrinal action limitations, create problems, restrict use, and decrease target effectiveness. Additionally, the paper analyzes the role and impact of Kamikaze drones in military operations in the current integrated and cybernetic battlefield, highlighting their ability to precisely target and surprise the enemy.

Vertical Takeoff and Landing for Distribution of Parcels to Hospitals: A Case Study about Industry 5.0 Application in Israel’s Healthcare Arena

To gain a sustained competitive advantage, organizations such as UPS, Fedex, Amazon, etc., began to seek for industry 5.0 innovative autonomous delivery options for the last mile. Autonomous unmanned aerial vehicles are a promising alternative for the logistics industry. The fact that drones are propelled by green renewable energy source fits the companies’ need to become sustainable, replacing their fuel truck fleets, especially for traveling to remote rural locations to deliver small packages, but a major obstacle is the necessity for charging stations which is well documented in the literature. Therefore, the current research embarks on devising a novel yet practical piece of technology adopting the simplicity approach of direct flights to destinations. The analysis showcases the application for a network of warehouses and hospitals in Israel while controlling costs. Given the products in the case study are medical, direct flight has the potential to save lives when every moment counts. Hydrogen cell technology allows long-range flying without refueling, and it is both vibration-free which is essential for sensitive medical equipment and environmentally friendly in terms of air pollution and silence in urban areas. Importantly, hydrogen cells are lighter, with higher energy density than batteries, which makes them ideal for drone usage to reduce weight, maintain a longer life, and enable faster charging, all of which minimize downtime. Also, hydrogen sourcing is low-cost and unlimited compared to lithium-ion material which needs to be mined. The case study investigates an Israeli entrepreneurial company, Gadfin, which builds a vertical takeoff-and-landing-type of drone with folded wings that enable higher speed for the delivery of refrigerated medical cargo, blood, organs for transplant, and more to hospitals in partnership with the Israeli medical logistic conglomerate, SAREL. An analysis of shipping optimization (concerning the number and type of drone) is conducted using a mixed-integer linear programming technique based on various types of constraints such as traveling distance, parcel weight, the amount of flight controllers and daily number of flights allowed in order to not overcrowd the airspace. Importantly, the discussion assesses the ecosystem’s variety of risks and commensurate safety mechanisms for advancing a newly shaped landscape of drones in an Israeli tight airspace to establish a network of national routes for drone traffic. The conclusion of this research cautions limitations to overcome as the utilization of drones expand and offers future research avenues.

Real-time lightweight drone detection model: Fine-grained Identification of four types of drones based on an improved Yolov7 model

Recently, the rapid progress of artificial intelligence has enhanced the human-robot relationship through the development of several autonomous robots; such as drones. The overwhelming rise of drones has brought both relevant advantages and threatening risks into our daily lives. They are strongly deployed to achieve hard tasks and reach critical areas within a short period of time. In the past few years, drones' anarchic and malicious use has generated many incidents and accidents, causing far-reaching impacts. Due to their dual-edged nature, they are regarded as potential threats. Recognizing the type of flying drones represents a challenging task for anti-drone systems to reinforce airspace safety and security. To this end, we propose a novel lightweight model able to differentiate between the main types of drones in real-time using appropriate modules. To find the optimal compromise between size, performance and speed, a lightweight model is proposed based on Yolov7 with the incorporation of CNeB and C3C2 modules as well as the Re- Parameterization Decoupled (RePD) head structure. Further, a CNeB module is used to reduce the model’s size and improve the model’s performance, a C3C2 module to enhance the feature extraction and fusion and a Re- Parameterization Decoupled RePD to improve both the efficacy and accuracy of the prediction part while reducing the inference time. The experimental results show that the Yolov7-CNeB-C3C2-RePD meets the anti-drone requirements by improving the detection performance, minimizing the inference speed and reducing the model size. In comparison to other models, the developed has provided high results reaching 91.9% precision, 90.5% recall, 95% mAP@0.5, and 20.3 ms during the inference stage, which outperforms the other algorithms in the benchmark. Furthermore, the model has an average detection speed of 0.003 ms per image and a capacity to process 27.88 frames per second, thus satisfying the real-time requirements.

A Review on the State of the Art in Copter Drones and Flight Control Systems.

This paper presents an overview on the state of the art in copter drones and their components. It starts by providing an introduction to unmanned aerial vehicles in general, describing their main types, and then shifts its focus mostly to multirotor drones as the most attractive for individual and research use. This paper analyzes various multirotor drone types, their construction, typical areas of implementation, and technology used underneath their construction. Finally, it looks at current challenges and future directions in drone system development, emerging technologies, and future research topics in the area. This paper concludes by highlighting some key challenges that need to be addressed before widespread adoption of drone technologies in everyday life can occur. By summarizing an up-to-date survey on the state of the art in copter drone technology, this paper will provide valuable insights into where this field is heading in terms of progress and innovation.

Drone-Assisted Particulate Matter Measurement in Air Monitoring: A Patent Review

Air pollution is caused by the presence of polluting elements. Ozone (O3), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM) are the most controlled gasses because they can be released into the atmosphere naturally or as a result of human activity, which affects air quality and causes disease and premature death in exposed people. Depending on the substance being measured, ambient air monitors have different types of air quality sensors. In recent years, there has been a growing interest in designing drones as mobile sensors for monitoring air pollution. Therefore, the objective of this paper is to provide a comprehensive patent review to gain insight into the proprietary technologies currently used in drones used to monitor outdoor air pollution. Patent searches were conducted using three different patent search engines: Google Patents, WIPO’s Patentscope, and the United States Patent and Trademark Office (USPTO). The analysis of each patent consists of extracting data that supply information regarding the type of drone, sensor, or equipment for measuring PM, the lack or presence of a cyclone separator, and the ability to process the turbulence generated by the drone’s propellers. A total of 1473 patent documents were retrieved using the search engine. However, only 13 met the inclusion criteria, including patent documents reporting drone designs for outdoor air pollution monitoring. Therefore, was found that most patents fall under class G01N (measurement; testing) according to the International Patents Classification, where the most common sensors and devices are infrared or visible light cameras, cleaning devices, and GPS tracking devices. The most common tasks performed by drones are air pollution monitoring, assessment, and control. These categories cover different aspects of the air pollution management cycle and are essential to effectively address this environmental problem.

Fuzzy control allocation of a positionable rotor quadrotor based on log‐barrier optimization and propulsion system fault toleration

AbstractHerein, a control system and a fault tolerance method for the rotor positionable quadrotor are proposed. Quadrotors that have a variable structure are made for different purposes. The rotor‐positionable quadrotor studied here, is a type of drone with a variable structure that has the ability to change the position of its rotors linearly along the axis of each arm. It can be seen that this capability can improve the drone robustness against disturbances and faults in comparison with regular quadcopters. Due to the over‐actuated dynamics of this type of quadrotor, the control allocation scheme based on log‐barrier optimization is employed to obtain the position and speed of each rotor. In this study, it is experimentally shown that rotor positioning not only reduces power consumption but also increases roll and pitch control inputs magnitude. Furthermore, when a fault occurs as a decrease in rotor speed, a fuzzy method is proposed to position the rotors which tolerates the fault. Finally, numerical simulations and experimental tests verified that rotor positioning can bring more robustness, reduction in power consumption, and fault tolerance in some rotor faults capabilities for quadrotors.

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.

Facility location decisions for drone delivery with riding: A literature review

This study presents a comprehensive literature survey on facility location problems for drone (uncrewed vehicle) delivery in situations where drones can ride in or on other vehicles. This includes facilities visited by only one type of vehicle, as well as facilities visited by both drones and other vehicles. Unlike traditional facility location problems for delivery systems with one vehicle type, hybrid vehicle-drone delivery systems usually require determining locations where the two vehicle types meet and separate. The main goals of this paper are to review the large volume of drone delivery literature with riding from a facility location perspective to provide a connection between the studies from different research areas that cover similar problems, and to highlight future research directions in this area. We first review the functions of drones, including aerial and ground drones, and the different types of facilities used for hybrid vehicle-drone delivery systems. The literature is categorized based on the presence of resupply operations, the locations of drone launch and retrieval points, the types of drones (aerial or ground) and the location space (discrete or continuous). Each category is analyzed in terms of the modeling approach, decision(s), objective function(s), constraints and additional features. The paper concludes with promising future research directions.

A heuristic-based benders decomposition for the hybrid relief distribution network minimizing deprivation cost of vulnerable areas

PurposeThe study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology.Design/methodology/approachResearch has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques.FindingsIncorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate.Originality/valueCurrent research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.

Drone detection with radio frequency signals and deep learning models

The widespread use of drones raises security, environmental, privacy, and ethical issues; therefore, effective detection by drones is important. There are several methods for detecting drones, such as wireless signal detection, photoelectric detection, radar detection, and sound detection. However, these detection methods are not accurate enough to identify drones for use. To solve this question, more robust drone detection method are needed. In addition, for different types of drones and application scenarios, different technical means need to be used for detection and identification. Based on 2-class ,4-class and 10-class problems on an open ratio frequency (RF) signal dataset, we compared the drone detection and classification performances of different machine learning with deep learning models and multi-task models which is proposed by combining different RF methods with Convolutional neural networks (CNNs). Our experimental results show that the XGBoost model achieved the latest results on this groundbreaking dataset, with 99.96% accuracy for 2-class problem, 92.31% accuracy for 4-class problem, and 74.81% accuracy for 10-class problem, which exhibits the best performance for drone detection and classification.

Malicious UAV detection using various modalities

Unmanned Aerial Vehicles, also known as drones, have seen increasing interest in recent years. This surge of interest is based on technological advancements, enhanced performance, affordability, and their large array of applications. Despite their utility in various applications, drones could also be used for malicious intent. The increasing concern regarding malicious drones triggers the use of various technologies and countermeasures, including drone detection and counter-drone systems. In this paper, we raise concerns about the malicious use of drone systems by providing a brief description of the major security threats. We also present the architecture of Unmanned Aerial Systems, drone types and their communication methods. We provide an in-depth overview of various drone detection techniques including vision-based, radio-frequency-based, and audio-based techniques, and discuss recent studies that address this issue using machine learning and deep learning models. We highlight the importance of using a hybrid approach for more accurate results.

Hybrid Mode: Routinization of the Transition Mode as the Third Common Mode for Compound VTOL Drones

Fixed-wing Vertical Takeoff and Landing (VTOL) drones have been widely researched and applied because they combine the advantages of both rotorcraft and fixed-wing drones. However, the research on the transition mode of this type of drone has mainly focused on completing the process quickly and stably, and the application potential of this mode has not been given much attention. The objective of this paper is to routinize the transition mode of compound VTOL drones, i.e., this mode works continuously for a longer period of time as a third commonly used mode besides multi-rotor and fixed-wing modes, which is referred to as the hybrid mode. For this purpose, we perform detailed dynamics modeling of the drone in this mode and use saturated PID controllers to control the altitude, velocity, and attitude of the drone. In addition, for more stable altitude control in hybrid mode, we identify the relevant parameters for the lift of the fixed-wings and the thrust of the actuators. Simulation and experimental results show that the designed control method can effectively control the compound VTOL drone in hybrid mode. Moreover, it is proven that flight in hybrid mode can reduce the flight energy consumption to some extent.

Investigation of the Use of Drones by Construction Professionals in Addressing Built Environment Tasks

An unmanned aerial vehicle (UAV), also called a drone, has been gaining attention globally to solve different tasks. The aim of this study is to assess the awareness and level of adoption of these types of drones by building professionals and the factors limiting their use in the built environment. The study was carried out in Lagos State, in the southwestern part of Nigeria. Data for this research were collected from primary and secondary data sources. Primary data was obtained by administering a questionnaire to the selected building industry professionals, and the collected data were analyzed using both descriptive and inferential statistical techniques. The results showed that structure inspection and photography are the most rated features of drones in addressing built environment tasks, with a mean score (MS) of 4.50, and security surveillance (MS = 3.86) is the least important feature. The level of adoption of drones in addressing built environment tasks was assessed among the professionals, and the results showed that the practice of an emergency response plan and a health and safety plan to reduce the risk of harm and ensure safety on site is considered to be the most important task. The study found that the fixed-wing hybrid drone with an MS of 1.97 is the most commonly used drone type. Lastly, the study showed that lack of public awareness (MS = 4.81) is rated as the most significant factor that limits its use. Few professionals have practically used drones to solve tasks faced in the building industry. Thus, there is a need to further sensitize built environment professionals to the benefits associated with its wider adoption and applications. Also, the importance of drones and their associated managerial and practical implications in solving relevant built environment tasks can be initiated by deepening their relevance in the curriculum of building industry programs at the professional and academic levels.

Front and Back Covers, Volume 40, Number 1. February 2024

Front and back cover caption, volume 40 issue 1EYE IN THE SKY: ANTHROPOLOGY IN THE DRONE ERAA civilian contractor launches a V‐BAT surveillance and reconnaissance drone from the USS Rushmore in the Philippine Sea. Manufactured by the California‐based startup Shield AI, the V‐BAT takes off vertically and operates autonomously without operator guidance or GPS (global positioning system). Its ‘swarming’ capability allows for potential combat use.Drones have had a profound impact in theatres of war for decades, causing lethal outcomes for combatants and non‐combatants alike. The US‐led ‘war on terror’ in regions like Afghanistan, Pakistan and Yemen has seen drone strikes causing tens of thousands of deaths, including many civilians. Beyond the direct, destructive impact, American drone pilots report significant mental health issues like anxiety, depression and PTSD due to the nature of remote‐controlled warfare.Recent conflicts in Ukraine and Gaza have seen an expansion in the types of drones used, ranging from inexpensive commercial drones with explosives to advanced armed drones. While traditionally operated remotely, there is an increasing shift towards autonomous functioning.This escalation in drone warfare technology, particularly the development of lethal autonomous weapon systems, has led to warnings of a global AI arms race involving powers like the US, China and Russia. The concept of ‘sensor‐to‐shooter’ machine learning infrastructures by military planners underscores the ethical, moral and sociopolitical dilemmas in a world increasingly leaning towards algorithmic warfare.In this issue, Roberto González argues that anthropologists need to illuminate how drone warfare impacts societies, the effects on both operators and targeted populations and the broader sociopolitical implications of autonomous weapons. This critical, publicly oriented anthropological perspective is essential to navigating the complex interplay of technology, ethics and human values in modern warfare, offering a deeper understanding of the societal impacts of these rapidly evolving military technologies.UNRAVELLING ‘REAL’ KINSHIPThis thought‐provoking Illustration shows the contrast between the idealized concept of unilineal descent and the intricate realities of kinship in everyday life. The left side of the image displays a simplified, stylized, unilineal family tree. With its clear, vertical lines, this depiction symbolizes kinship as a straightforward, linear progression. It embodies an idealized perspective on lineage, where relationships are traced through a single ancestral line, mirroring classical kinship theory.Conversely, the right side of the image presents a more complex and interwoven family tree. This part challenges the simplistic notions of unilineal descent, revealing kinship's rich, multifaceted nature. Here, the interconnected lines represent a spectrum of relationships extending beyond mere descent, encompassing marriage, adoption, communal ties and other social bonds. These aspects are frequently marginalized in conventional kinship models but, as anthropologists know, they are essential to understanding the full scope of human connections.Sabina Cveček's insightful analysis in this issue shows how archaeologists’ interpretation of unilineal kinship systems tend to be incomplete. Her work underscores the disparity between theoretical models and the nuanced realities of kinship, prompting a call for a more dynamic interpretation of kinship as a socially constructed and evolving network. This illustration invites introspection on the diversity and complexity of kinship and advocates a more inclusive and holistic approach to studying human relationships.Cveček argues that the dichotomy between idealized models and actual kinship dynamics is a recurring theme in anthropological literature. Anthropology remains crucial in understanding the depths and dimensions of kinship systems, even in today's era of scientific advancements like ancient DNA analysis.

An audio‐based risky flight detection framework for quadrotors

AbstractDrones have increasingly collaborated with human workers in some workspaces, such as warehouses. The failure of a drone flight may bring potential risks to human beings' life safety during some aerial tasks. One of the most common flight failures is triggered by damaged propellers. To quickly detect physical damage to propellers, recognise risky flights, and provide early warnings to surrounding human workers, a new and comprehensive fault diagnosis framework is presented that uses only the audio caused by propeller rotation without accessing any flight data. The diagnosis framework includes three components: leverage convolutional neural networks, transfer learning, and Bayesian optimisation. Particularly, the audio signal from an actual flight is collected and transferred into time–frequency spectrograms. First, a convolutional neural network‐based diagnosis model that utilises these spectrograms is developed to identify whether there is any broken propeller involved in a specific drone flight. Additionally, the authors employ Monte Carlo dropout sampling to obtain the inconsistency of diagnostic results and compute the mean probability score vector's entropy (uncertainty) as another factor to diagnose the drone flight. Next, to reduce data dependence on different drone types, the convolutional neural network‐based diagnosis model is further augmented by transfer learning. That is, the knowledge of a well‐trained diagnosis model is refined by using a small set of data from a different drone. The modified diagnosis model has the ability to detect the broken propeller of the second drone. Thirdly, to reduce the hyperparameters' tuning efforts and reinforce the robustness of the network, Bayesian optimisation takes advantage of the observed diagnosis model performances to construct a Gaussian process model that allows the acquisition function to choose the optimal network hyperparameters. The proposed diagnosis framework is validated via real experimental flight tests and has a reasonably high diagnosis accuracy.

Pregled mogućnosti primene nekih tehnologija nano dronova u savremenoj poljoprivredi

The use of different types of drones in almost all sectors of the global economy is growing rapidly, but the use of drones in agriculture has suddenly increased. According to some data from the literature, the market for different types of drones in agriculture alone is expected to grow from USD 1.2 billion in 2019 to USD 5.5 billion in 2024. A particularly interesting phenomenon is the significant increase in the use of drones (especially various nano-types) in the world and the possibility of some of them being used in agriculture in the Republic of Serbia. The world of drone technology has taken a huge leap forward with the introduction of nano drones. For example, some modern nano drone solutions have dimensions of less than 2 x 2 cm. Nano drones are ultra-small remote-controlled aircraft that can perform a variety of tasks. They are equipped with advanced sensors and functions such as obstacle avoidance and high-speed maneuverability. Some models are even capable of taking aerial photographs, staying in the air for long periods of time and flying autonomously. Nano drones are now more affordable than ever before. Prices range from a few hundred dollars to several thousand, depending on the model and features. Nowadays, nano drones are affordable for everyday users in various fields. This paper introduces nano drone technology (e.g. the type of nano drones and equipment) as a new application for greenhouses: There are some stages that greenhouse growers can consider for the use of nano drones; Safe inspection of the structural components of greenhouses; Pollination processes (e.g. the role of RobotBee); Application of shading composite glasshouses; Crop monitoring/inventory of greenhouses.