Flight Time Of Unmanned Aerial Vehicle Research Articles

Systematic Research and Application of a 5G Medical Unmanned Aerial Vehicle to Deliver COVID-19 Nucleic Acid Samples.

The purpose of this study was to investigate the feasibility and effectiveness of COVID-19 throat swab samples delivered by medical drones in epidemic prevention and control. This study was carried out in both southern and northern hospital districts of the Affiliated Hospital of Jiangnan University from May to October 2022. The main participants were the Affiliated Hospital of Jiangnan University and Zhejiang Antwork Technology Co., Ltd. We first constructed an urban medical unmanned aerial vehicle (UAV) delivery system and developed a UAV-specific storage box for COVID-19 samples. The UAV system was used to transport COVID-19 throat swab samples from the northern hospital district to the southern hospital district, and the following indexes were obtained: (1) flight time of COVID-19 samples delivered by UAV, (2) real-time temperature of COVID-19 nucleic acid samples during transportation, and (3) the time of distribution of COVID-19 nucleic acid samples by road traffic as measured using the Baidu Maps application, compared with the flight time of UAV. The COVID-19 sample delivery system for urban medical UAV mainly consists of intelligent logistics UAV, low-temperature COVID-19 throat swab sample storage box, unmanned logistics hub, and cloud operation control platform. The flight distance between the northern and southern districts of the Affiliated Hospital of Jiangnan University was 10 km, and the ground distance was 24 km. From May 11 to October 28, 2022, a total of 1,190 UAV flights occurred. The average flight time was 13 minutes, which was 40 to 70 minutes faster than the average road travel time required for manual delivery of COVID-19 throat swab samples. At different time points in the day, UAV delivery efficiency increased by 67.5% to 82%. The use of 5G with the Internet of Things and UAV technology to deliver nucleic acid samples has the characteristics of fast speed, being unaffected by ground traffic conditions, and the ability to ensure the safety of nucleic acid samples in the transportation process, which is worthy of further study.

GREENSKY: A fair energy-aware optimization model for UAVs in next-generation wireless networks

Unmanned Aerial Vehicles (UAVs) offer a strategic solution to address the increasing demand for cellular connectivity in rural, remote, and disaster-hit regions lacking traditional infrastructure. However, UAVs’ limited onboard energy storage necessitates optimized, energy-efficient communication strategies and intelligent energy expenditure to maximize productivity. This work proposes a novel joint optimization model to coordinate charging operations across multiple UAVs functioning as aerial base stations. The model optimizes charging station assignments and trajectories to maximize UAV flight time and minimize overall energy expenditure. By leveraging both static ground base stations and mobile supercharging stations for opportunistic charging while considering battery chemistry constraints, the mixed integer linear programming approach reduces energy usage by 9.1 ​% versus conventional greedy heuristics. The key results provide insights into separating charging strategies based on UAV mobility patterns, fully utilizing all available infrastructure through balanced distribution, and strategically leveraging existing base stations before deploying dedicated charging assets. Compared to myopic localized decisions, the globally optimized solution extends battery life and enhances productivity. Overall, this work marks a significant advance in UAV energy management by consolidating multiple improvements within a unified coordination framework focused on joint charging optimization across UAV fleets. The model lays a critical foundation for energy-efficient aerial network deployments to serve the connectivity needs of the future.

Trajectory optimization of laser-charged UAV to minimize the average age of information for wireless rechargeable sensor network

This paper considers the laser-charged Unmanned Aerial Vehicle (UAV) assisted Wireless Rechargeable Sensor Network (WRSN), where rechargeable sensors are deployed in surveillance environment to monitor information, a UAV is not only used as aerial wireless mobile collector for gathering data from sensors but also used as mobile charger to replenish energy for sensors, Laser Beam Directors (LBDs) are uniformly deployed in the monitoring environment to charge UAV by emitting laser beams. In such network, we study the average Age of Information Optimization (AoIO) problem whose objective is to minimize the average AoI of data collected from sensors such that all data of the network are transported to the base station and the remaining energy of any sensor exceeds a certain threshold. We prove that the AoIO problem is NP-hard. To solve the AoIO problem, we first study the Total Flight Time Minimizing of UAV (TFTM) problem, which aims at finding an optimal charging solution of UAV to minimize the flight time of UAV based on the order of sensors visited by UAV. Then we prove that the TFTM problem is also NP-hard. Afterwards, we propose a heuristic algorithm to solve the TFTM problem by optimizing flight path, data collection, energy power transfer and laser charging of UAV. Based on the solution for the TFTM problem, we propose an approximation algorithm to solve the AoIO problem. Finally, we conduct extensive simulation experiments to verify the effectiveness of the proposed algorithm.

Modulating Retroreflector Based Free Space Optical Link for UAV-to-Ground Communications

Weight reduction and low power consumption are key requirements in the next generation of unmanned aerial vehicle (UAV) networks. Employing modulating retro-reflector (MRR)-based free space optical (FSO) technology is an innovative technique for UAV-to-ground communication in order to reduce the payload weight and power consumption of UAVs which leads to increased maneuverability and flight time of UAV. In this paper, we consider an MRR-based FSO system for UAV-to-ground communication. We will show that the performance of the considered system is very sensitive to tracking errors. Therefore, to assess the benefits of MRR-based UAV deployment for FSO communications, the MRR-based UAV FSO channel is characterized by taking into account tracking system errors along with UAV’s orientation fluctuations, link length, UAV’s height, optical beam divergence angle, effective area of MRR, atmospheric turbulence and optical channel loss in the double-pass channels. To enable effective performance analysis, tractable and closed-form expressions are derived for probability density function of end-to-end signal to noise ratio, outage probability and bit error rate of the considered system under both weak-to-moderate and moderate-to-strong atmospheric turbulence conditions. The accuracy of the analytical expressions is verified by extensive simulations. Analytical results are then used to study the relationship between the optimal system design and tracking system errors.

Estimation of UAV flight time and Battery Consumption for photogrammetric application using multiple machine learning algorithms

ABSTRACTIn recent years, important research has been conducted in Machine Learning (ML), especially on Artificial Neural Networks (ANN). Adaptive-Network Based Fuzzy Inference Systems (ANFIS) and Particle Swarm Optimization-Fuzzy Inference System (PSO-FIS) algorithms are popular ML algorithms like ANN. In terms of their working architecture and results, ANN, ANFIS, and PSO-FIS algorithms can obtain useful solutions for different nonlinear problems. This study evaluated the performance of the ANN, ANFIS, and PSO-FIS algorithms and compared the estimation results. Regarding the application, the test and target data was obtained from the flights performed with Unmanned Aerial Vehicles (UAV), including how long the UAV operates (i.e., Flight Time, FT) and how much battery the UAV consumes during the flight (i.e., Battery Consumption, BC). To obtain FT and BC outputs, sixty-five pre- and post-flight data tables were created. The best iterations for estimating the outputs using the three ML algorithms (considering the minimum/maximum values, RMSE, R, and R2) were determined and discussed based on the training, validation, and test estimations.

SOC Estimation of Lithium-ion Battery Based on RLS-EKF for Unmanned Aerial Vehicle

In recent years, in the civil and military unmanned aerial vehicle(UAV) market, the demand for UAV is increasing day by day, the iteration cycle of UAV products is significantly shortened, and UAV has developed rapidly. Among them, lithium battery has the advantages of high energy density, lightweight and bendability. At present, as the main power source of electric UAV, lithium battery affects the range and flight time of UAV, and is the key factor that directly restricts the development and application of UAV. Therefore, accurate estimation of battery state of charge (SOC) has always been the focus and difficulty in the field of UAV battery research. This paper mainly improves the accuracy of SOC estimation of power battery from the following aspects:First, the significance of researching power batteries is clarified, the advantages of lithium-ion power batteries are pointed out, the definition of SOC and its influencing factors are discussed, and the common methods of battery SOC estimation are more comprehensively introduced and analyzed.Next, a second-order RC network equivalent circuit model that is convenient for engineering realization is established, and its state-space model is deduced. The SOC-OCV relationship was measured.Then, the recursive least squares (RLS) method suitable for real-time calculation is used to identify the parameters of the equivalent circuit model of lithium-ion battery on-line, and the parameters of the equivalent circuit model are identified under UDDS conditions, which proves the correctness and rationality of the identification method.Finally, it is proposed to use the extended Kalman filter (EKF) algorithm to estimate the SOC of the lithium-ion battery, that is, use the EKF algorithm to estimate the open circuit voltage of the battery, and then estimate the battery SOC according to the open circuit voltage method. The accuracy of the joint estimation algorithm to estimate SOC is verified under simulated conditions.

An Integral Data Gathering Framework for Supervisory Control and Data Acquisition Systems in Green IoT

In Green Internet of Things, energy consumption research is a hot topic. Our research focuses on Supervisory Control And Data Acquisition (SCADA) system, and it is a system consisting of a plurality of self-organized sensing networks. Live data gathering from numerous Sub-connected SCADA (S-SCADA) networks to make unified decisions based on collected data is one of pivot problem, which has not been well studied. In this article, an integral data-gathering framework is proposed to prolong network lifetime by using sink rotating and Unmanned Aerial Vehicles (UAV) path planning. In the proposed Sink Rotating joint UAV Data Gathering (SR-UAV-DG) framework, the data gathering process is divided into two organic components: (a) S-SCADA in-network data collection. The energy consumption of the network is balanced by selecting the node with the least energy consumption as the sink node. (b) Use UAV for data collection between S-SCADA networks. The theoretical analysis results show that the SR-UAV-DG framework proposed in this article reduces the maximum energy consumption of nodes in the network by 99.21% after 1000 rounds of data. The flight time of UAV is reduced by 16.83%. In the case of unreliable communication links, the data reception rate is guaranteed to reach 91.94%.

AerialBlocks: Blockchain-Enabled UAV Virtualization for Industrial IoT

Unmanned Aerial Vehicles (UAV) are emerging as a powerful tool in various industrial applications in the context of enabling Industrial IoT. The limited power source and flight time of UAVs along with the need for skilled UAV operators are some of the most daunting challenges to the integration of UAVs in industrial applications such as site inspection, workforce monitoring, logistics, and others. In this article, we propose the unique paradigm of AerialBlocks for blockchain.enabled UAV virtualization to provide virtual UAV-as-a-service for industrial applications. AerialBlocks also aims at providing secure and persistent UAV services to the end-users along with a partially decentralized blockchain model to ensure security, privacy, service quality, and transparency. UAV virtualization allows persistent UAV services, globally, without procuring any physical UAVs. The platform offers virtual UAV services on a pay-per-use basis to the end-user. The UAV owners and virtual UAV service providers gain monetary benefits for their contribution to the UAV service offerings. Finally, we discuss the implications of the proposed platform on the domains of Industrial IoT and the possible challenges in the implementation of this paradigm.

Hydrone: Reconfigurable Energy Storage for UAV Applications

Unmanned aerial vehicles (UAVs) are often used in mission-critical applications, requiring a critical criterion in flight time. Unfortunately, severe power fluctuations, caused by specific flight patterns, degrade the deliverable capacity of the battery and hamper the flight time. A common approach to mitigating power fluctuations is to employ a hybrid energy storage system using a Li-ion battery with an ultracapacitor (UC). However, the conventional scheme poses inherent problems of low-energy density and power leakage due to the use of the UC and the supplementary hardware required for hybrid storage. In this article, we propose Hydrone, a reconfigurable battery architecture that maximizes the flight time of UAVs, overcoming the previous limitations. Hydrone addresses two key challenges that arise when hybrid energy storage is utilized in UAVs: 1) capacity loss and 2) power leakage. First, the proposed scheme compromises the capacity loss of hybrid storage by using a minimal capacity UC for use as a buffer to counteract the power fluctuations. Second, the power leakage of the hybrid battery is minimized by draining power from the UC only when it is necessary. To this end, the Hydrone architecture provides reconfigurability in hardware and offers two modes of battery operation, i.e., a battery-only mode and a hybrid mode. An appropriate operation is then selected at runtime depending on the flight situation and battery status. To switch modes, we employed a reinforcement learning-based switch control, reflecting the power fluctuation adequately on the flight and battery states. We implemented a hardware prototype to demonstrate the efficiency of Hydrone. Our extensive evaluation shows that the flight time of a UAV is prolonged up to 39% in our experiment setup.

Energy Efficiency and Hover Time Optimization in UAV-Based HetNets

In this article, we investigate the downlink performance of a three-tier heterogeneous network (HetNet). The objective is to enhance the edge capacity of a macro cell by deploying unmanned aerial vehicles (UAVs) as flying base stations and small cells (SCs) for improving the capacity of indoor users in scenarios such as temporary hotspot regions or during disaster situations where the terrestrial network is either insufficient or out of service. UAVs are energy-constrained devices with a limited flight time, therefore, we formulate a two layer optimization scheme, where we first optimize the power consumption of each tier for enhancing the system energy efficiency (EE) under a minimum quality-of-service (QoS) requirement, which is followed by optimizing the average hover time of UAVs. We obtain the solution to these nonlinear constrained optimization problems by first utilizing the Lagrange multipliers method and then implementing a sub-gradient approach for obtaining convergence. The results show that through optimal power allocation, the system EE improves significantly in comparison to when maximum power is allocated to users (ground cellular users or connected vehicles). The hover time optimization results in increased flight time of UAVs thus providing service for longer durations.

A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles.

With the development of information technology, Internet-of-Things (IoT) and low-altitude remote-sensing technology represented by Unmanned Aerial Vehicles (UAVs) are widely used in environmental monitoring fields. In agricultural modernization, IoT and UAV can monitor the incidence of crop diseases and pests from the ground micro and air macro perspectives, respectively. IoT technology can collect real-time weather parameters of the crop growth by means of numerous inexpensive sensor nodes. While depending on spectral camera technology, UAVs can capture the images of farmland, and these images can be utilize for analyzing the occurrence of pests and diseases of crops. In this work, we attempt to design an agriculture framework for providing profound insights into the specific relationship between the occurrence of pests/diseases and weather parameters. Firstly, considering that most farms are usually located in remote areas and far away from infrastructure, making it hard to deploy agricultural IoT devices due to limited energy supplement, a sun tracker device is designed to adjust the angle automatically between the solar panel and the sunlight for improving the energy-harvesting rate. Secondly, for resolving the problem of short flight time of UAV, a flight mode is introduced to ensure the maximum utilization of wind force and prolong the fight time. Thirdly, the images captured by UAV are transmitted to the cloud data center for analyzing the degree of damage of pests and diseases based on spectrum analysis technology. Finally, the agriculture framework is deployed in the Yangtze River Zone of China and the results demonstrate that wheat is susceptible to disease when the temperature is between 14 °C and 16 °C, and high rainfall decreases the spread of wheat powdery mildew.

Artificial intelligence based power consumption estimation of two-phase brushless DC motor according to FEA parametric simulation

In this study, Artificial Neural Networks, Extreme Machine Learning and Support Vector Machine (SVM) are used to estimate power consumption of Brushless DC motor in Unmanned Aerial Vehicle (UAV). Durafly 3648 Brushless DC motor of UAV is modelled with the Finite Element Analysis software. Then it is simulated with Ansys-Rmxprt according to pulse degree, speed and battery voltage of the UAV. The training times and Mean Absolute Percentage Errors (MAPE) of the Artificial Intelligence Techniques (AITs) are calculated for motor input power estimation. The best result among the implemented AITs is achieved with the MAPE of 0.269% by the SVM model. Then the graphical user interface (GUI) is developed to easily obtain information about how efficient the battery can be used, and the flight time of UAVs. Thanks to the proposed GUI software, the input power of the motor can be estimated via input parameters without the long-time consuming simulations.

The Comparison of Energy Sources Used in Unmanned Air Vehicles

Unmanned Aerial Vehicles (UAVs) are intelligent systems that have been among the most popular technologies. UAVs have been used in many different areas, such as military, monitoring, entertainment, academic, engineering, search and rescue, border security, surveillance, cargo transportation. UAVs are the air vehicles that can be controlled by a remote control or which can move according to their abilities by means of autonomous software. Because of technological advances and facilitating the supply of used materials, the number of UAVs is increasing day by day. According to the usage area, UAVs have got many advantages and disadvantages. One of these disadvantages is undoubtedly low flight times. The low flight time of UAVs brings problems occur in accomplishing certain tasks. In order to overcome this problem, the studies are being carried out to develop energy sources. Lithium Ion (Li-ion), Lithium Ferrous Phosphate (LiFePO4), Lithium Polymer (LiPo) and Lithium Titanate batteries are used in the UAV flights. In this study, a review is presented about UAV energy sources used in past and present. In addition, the future trends in the energy sources has been examined for UAVs. Keywords : Energy Source, Battery, LiPo, Solar Panel, PEM. DOI : 10.7176/JSTR/5-6-05

Use of aerial thermography to reduce mortality of roe deer fawns before harvest.

In agricultural landscape, there are thousands of young wild animals killed every year. Their deaths are caused mostly by agricultural fieldworks during spring harvest. Among the affected animals there are also fawns of roe deer (Capreolus capreolus), which react to danger by pressing themselves against the ground in order to be protected from predators. There were various methods tested in the past aimed at decreasing roe deer mortality caused by agriculture machinery with varied levels of success. This contribution presents technology that documents the possibility of searching for fawns with a thermal imaging device carried by an unmanned aerial vehicles (UAV). The results are based on field research that estimated the ideal height of flight being ±40 meters above ground. If the climatic conditions are favourable, it is possible to monitor and mark fawn locations using GPS coordinates in an area of about 14 ha in 25 minutes, which is the average flight time of UAV on one battery charge. The thermo-camera is very reliable in finding fawns in early morning hours (4 to 6 a.m.) when there is the highest temperature contrast between the searched object and its surroundings. The main limiting factors are climatic conditions and the short time span in which the thermo-camera can be used. If the basic requirements are met, the rate of successful fawn detection can be even up to 100%. An undisputed advantage of this method is the possibility of involvement of local gamekeepers. Thus the agricultural fieldworks are not interrupted.

Real-Time Optimal Resource Allocation for Embedded UAV Communication Systems

We consider device-to-device (D2D) wireless information and power transfer systems using an unmanned aerial vehicle (UAV) as a relay-assisted node. As the energy capacity and flight time of UAVs is limited, a significant issue in deploying UAV is to manage energy consumption in real-time application, which is proportional to the UAV transmit power. To tackle this important issue, we develop a real-time resource allocation algorithm for maximizing the energy efficiency by jointly optimizing the energy-harvesting time and power control for the considered (D2D) communication embedded with UAV. We demonstrate the effectiveness of the proposed algorithms as running time for solving them can be conducted in milliseconds.

Integrated optimization of unmanned aerial vehicle task allocation and path planning under steady wind.

Wind has a significant effect on the control of fixed-wing unmanned aerial vehicles (UAVs), resulting in changes in their ground speed and direction, which has an important influence on the results of integrated optimization of UAV task allocation and path planning. The objective of this integrated optimization problem changes from minimizing flight distance to minimizing flight time. In this study, the Euclidean distance between any two targets is expanded to the Dubins path length, considering the minimum turning radius of fixed-wing UAVs. According to the vector relationship between wind speed, UAV airspeed, and UAV ground speed, a method is proposed to calculate the flight time of UAV between targets. On this basis, a variable-speed Dubins path vehicle routing problem (VS-DP-VRP) model is established with the purpose of minimizing the time required for UAVs to visit all the targets and return to the starting point. By designing a crossover operator and mutation operator, the genetic algorithm is used to solve the model, the results of which show that an effective UAV task allocation and path planning solution under steady wind can be provided.