Unmanned Arial Vehicles Research Articles

Crowd Monitoring System Based on Unmanned Aerial Vehicles: Secure Key Agreement Scheme

Unmanned Arial Vehicles (UAVs) can be applied to survey or for monitoring huge crowd where conventional monitoring systems fail. Even though UAVs is proven to be effective way for monitoring and surveying there present a threat of data getting leaked when it is being transferred to the user’s device. To lessen these dangers a secure channel amid the user and the UAVs needs to be determined. There are multiple key agreement methods which is already present but they are either heavy authentication type or less secure to the attacks from the unscrupulous parties. In this paper, we provide an approach to mitigate such threats using a public key cryptographic method with a session-based authentication. The above-mentioned method is simulated using NS2 software and it’s efficiency will be recorded at the end.

Military Decision-Making Process Enhanced by Image Detection

This study delves into the vital missions of the armed forces, encompassing the defense of territorial integrity, sovereignty, and support for civil institutions. Commanders grapple with crucial decisions, where accountability underscores the imperative for reliable field intelligence. Harnessing artificial intelligence, specifically, the YOLO version five detection algorithm, ensures a paradigm of efficiency and precision. The presentation of trained models, accompanied by pertinent hyperparameters and dataset specifics derived from public military insignia videos and photos, reveals a nuanced evaluation. Results scrutinized through precision, recall, map@0.5, mAP@0.95, and F1 score metrics, illuminate the supremacy of the model employing Stochastic Gradient Descent at 640 × 640 resolution: 0.966, 0.957, 0.979, 0.830, and 0.961. Conversely, the suboptimal performance of the model using the Adam optimizer registers metrics of 0.818, 0.762, 0.785, 0.430, and 0.789. These outcomes underscore the model’s potential for military object detection across diverse terrains, with future prospects considering the implementation on unmanned arial vehicles to amplify and deploy the model effectively.

Should We Care about how Birds Fly?

Bird flight has intrigued human minds ever since the Stone Age. Paintings in Lascaux cave in France are considered the oldest representation of birds in flight made by early ancestors of humans. The ability to fly has been interpreted as a divine power by some cultures which proceeded to include wings in drawings and sculptures of their deities. In the Renaissance period, Polymaths like Leonardo Da Vinci studied birds and their motion in detail. His codex of birds mentions the objective of this study as to develop a human-powered flying machine. Even, the Wright brothers used warping wings in their flight tests mimicking the flight patterns of birds. The wing and fuselage design of fixed-wing aircraft from the earliest versions to modern jet-powered airliners is directly inspired by the bio-mechanics of large birds. However, with the success of fixed-wing aircraft, the flapping wing concepts saw declining interest as a viable design option for air vehicles. During this time, the aerodynamics of bird wings was only studied in fundamental science to answer the questions on their aerodynamic performance. However, the popularization of Unmanned Arial Vehicles (UAVs) renewed the interest in bird-inspired air vehicles with flapping wings as a possible design.

Public Drone Perception

The use and popularity of unmanned arial vehicles (drones) has split over from the military domain into the civilian space in the past two decades. Its utility has been recognised not only by law enforcement agencies and the security industry at large, but also by private enterprises, leading the proliferation of drone integration in a variety of sectors. However, despite widespread recognition, concerns remain in the public domain over drone deployment. This study, funded by the TSB Technology Strategy Board (Funder Project Reference: 83074), aims to uncover the public's perception of drone use for emergency service response in a civilian context, whilst capturing how exposure to drone operations may influence and alter perception. To do this, two structured surveys were conducted pre- and post-exposure of drone flights, above a 500 m radius from a flight facility in Isleworth, UK (total population of 2363). The results of the study shed light on some statistically important indicators that influence drone perception both pre- and post-treatment; namely, technology acceptance and privacy perception. Those less concerned about privacy who already share personal information with public and private actors perceived drones positively for emergency response. Additionally, open-mindedness about new technology positively impacted public perception of drones. The post-treatment group had an additional three statistically significant predictors: age category, education level, and drone exposure. It was also found that security perception was not statistically significant. These new and novel findings in the field would require further testing to validate the predictors in other areas, in order to facilitate drone integration into civilian spaces.

A comparative study of deep learning and Internet of Things for precision agriculture

Precision farming is made possible by rapid advances in deep learning (DL) and the internet of things (IoT) for agriculture, allowing farmers to upgrade their agriculture operations to sustainably fulfill the future food supply. This paper presents a comprehensive overview of recent research contributions in DL and IoT for precision agriculture. This paper surveys the diverse research on DL applications in agriculture, such as detecting pests, disease, yield, weeds, and soil, including fundamental DL techniques. Also, the work describes the IoT architecture and analyzes sensor categorization, agriculture sensors, and unmanned arial vehicles (UAVs) used in recent research. Besides that, data acquisition, annotation, and augmentation for agriculture datasets were covered, and a few widely used datasets were listed. This work also discusses some challenges and issues that DL and IoT face. Furthermore, the research proposed a bootstrapping approach of Transfer learning where fine-tuned VGG16 is fused with optimized and improved newly built fully connected layers for pest detection. The performance of the proposed model is evaluated and compared with other models, such as custom VGG16 as a classifier; fine-tuned VGG16 is optimized with other optimizers like SGD, RMSProp, and Adam. The results show that the proposed model for pest detection outperforms all other models with an accuracy of 96.58 % and a loss of 0.15%. The review and the proposed work presented in this paper will significantly direct researchers toward DL and IoT for intelligent farming.

Autonomous drone charging station planning through solar energy harnessing for zero-emission operations

• Solar energy from building envelopes can extend UAVs coverage and reliability in last-mile delivery applications. • This integration can omit GHG emissions in parcel delivery while improving building energy efficiency. • A multi-objective optimization model is proposed that maximizes UAV coverage and minimizes the total cost of energy and decarbonization. • A flexible energy use model is applied to a digital twin to generate minimum-energy 3D trajectories. • RI-SHOT multi-objective optimization algorithm is used to solve all the delivery demands. • GHG emissions for the entire UAV charging network are omitted compared to a grid-connected operation. The introduction of Unmanned Arial Vehicles (UAVs) in smart city operations is considered a sustainable technological solution due to the promised significant greenhouse gas emission reductions. This study developed an integrated multi-objective charging infrastructure coverage optimization model that integrates UAV-based operations with solar energy harnessing from building envelopes. This model maximizes UAVs’ coverage range and minimizes the total cost of energy and decarbonization. The model is based on a flexible energy use model for UAVs calibrated to experimental measurements to generate a minimum-energy trajectory. We also developed an origin-destination (O-D) demand geo-referenced in a digital-twin model to replicate real-world operation. Overall, 12,532 simulated daily missions in a large-sized city are modelled. The results show that the proposed system can eliminate GHG emissions. Furthermore, the system can significantly reduce the decarbonization price through associated savings and excess generated electricity. The proposed approach demonstrates avenues to advance smart cities and capitalize on renewable energy.

Design and Simulation of Smart Communication System for Unmanned Arial Vehicles

In recent years, the use of drones has drastically increased as the evolution of drone use in commercial sectors and reduced costs of the hardware. Earlier drone services were mostly used for military operations but nowadays the Unmanned Arial Vehicles (UAV) system is very advanced and its applications are not limited to military operations. The recent years have also witnessed a network evolution of UAVs from single ground to air network to multi-UAV network systems along with usage of wireless public networks like LTE which can act as UAV communication channel. In the proposed project, a communication system used in the UAS system is simulated to analyze the UAV behavior under different conditions with respect to mission planning and the communication networks used. A comprehensive study is done on communication networks used in controlling UAVs. For a safer approach, the proposed model is simulated using available software instead of hardware implementations. ArduPilot SITL, MAVProxy and Mission Planner are used to simulate the UAV system virtually. Whereas network simulations of Wi-Fi and LTE network are done with the help of NS-3 on a separate platform. Various network parameters like network delay, throughput, etc., are graphically represented.

Multi-Emitter Localization via Concurrent Variational Bayesian Inference in UAV-Based WSN

Applying Compressive Sensing (CS) to Received Signal Strength (RSS) based multi-emitter localization using Unmanned Arial Vehicles (UAVs) attracts much attention for its simplicity and efficiency. However, the RSS-based CS approach is vulnerable to the noise in a practical scenario. To mitigate this, we propose a robust localization framework for multiple emitters in UAV-based Wireless Sensor Network (WSN). We first approximate the lognormal noise’s influence on the dictionary by a two-layer hierarchical prior model. Then, by exploiting multi-frequency measurements, the multi-emitter localization is transformed into the joint estimation for multiple sparse vectors and noise level. Finally, the joint estimation problem is solved by a Concurrent Variational Bayesian Inference (CVBI) algorithm, where an adaptive grid pruning mechanism is designed. The merits of the proposed framework are testified by numerical simulations.

Using Information Content to Select Keypoints for UAV Image Matching

Image matching is one of the most important tasks in Unmanned Arial Vehicles (UAV) photogrammetry applications. The number and distribution of extracted keypoints play an essential role in the reliability and accuracy of image matching and orientation results. Conventional detectors generally produce too many redundant keypoints. In this paper, we study the effect of applying various information content criteria to keypoint selection tasks. For this reason, the quality measures of entropy, spatial saliency and texture coefficient are used to select keypoints extracted using SIFT, SURF, MSER and BRISK operators. Experiments are conducted using several synthetic and real UAV image pairs. Results show that the keypoint selection methods perform differently based on the applied detector and scene type, but in most cases, the precision of the matching results is improved by an average of 15%. In general, it can be said that applying proper keypoint selection techniques can improve the accuracy and efficiency of UAV image matching and orientation results. In addition to the evaluation, a new hybrid keypoint selection is proposed that combines all of the information content criteria discussed in this paper. This new screening method was also compared with those of SIFT, which showed 22% to 40% improvement for the bundle adjustment of UAV images.

Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China

Monitoring the water pollution level in real time is the most critical issue for protecting the water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial Vehicles (UAV), four sensitive regions with the area of 1–2 km2 were first selected in this study based on the spatial distribution of total nitrogen (TN) concentration changes estimated by the Landsat remote sensing data. And then twelve machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks (ANN), Bayesian Ridge Regression (BRR), Lasso Regression (Lasso), Elastic Net (EN), Linear Regression (LR), Decision Tree Regression (DTR), K Neighbors Regression (KNR), Random Forest Regression (RFR), Extra Trees Regression (ETR), AdaBoost Regression (ABR) and Gradient Boosting Regression (GBR) were compared to construct a more accurate retrieval model by using the UAV hyper spectral remote sensing and ground monitoring data. And then the TN concentration was estimated after the process of dimensionality reduction and compressed sensing denoing. Finally, spatial heterogeneity of the TN concentration was analyzed in four sensitive areas of the Miyun reservoir. The results demonstrated that among the tested algorithms the Extra Trees Regression was best suitable for the construction of a TN concentration retrieval model on the basis of UAV hyper spectral data, and its absolute squared error was 0.000065. The spatial distribution of the TN showed that the concentration was highest within the water area of the Bulaotun village and the Houbajiazhuang village, while it was relatively low for the Chao river dam and Bai river dam. Additionally, no significant differences regarding the concentrations were shown in the single UAV flight area except the Houbajia village, which indicated that the water quality in Miyun reservoir was relatively stable and changed in a small interval. These conclusions can provide scientific references for water quality monitoring and management in the water reservoir.

Novel Architecture for Wireless Power Transfer in Unmanned Arial Vehicles

There has been a rapid growth in research towards developing smart, low-cost, and high-performance unmanned aerial vehicles (UAVs) for enabling wireless data transfer and communication. The practical applications of UAVs in healthcare, surveillance, agriculture and other domains is increasing at a faster pace. Mass production of these UAVs is essential to meet the growing communication requirements. Plans towards standardization and global deployment of Internet-of-Things (IoT) technologies and fifth generation (5G) wireless communication are in progress. Functional diversity is established by the UAVs in enabling wireless communication with IoT and 5G technologies. The different UAVs used in wireless communication is studied and a hierarchical architecture is proposed to facilitate integrating these technologies into next-generation networks. The principles of aerodynamics, wireless communication, power transfer, area of coverage, delay, meteorological impacts and other design parameters and their tradeoffs is analyzed.

3D Location Oriented Routing in Flying Ad-Hoc Networks for Information Dissemination

A Flying Ad-hoc Networks (FANETs) is an autonomous technology that creates a self-organized wireless network via Unmanned Arial Vehicles (UAVs). In this network, all UAVs can communicate within a restricted range of wireless communication in the absence of fixed infrastructure. As a result of high mobility, the limited energy, and the communication range of UAVs, network forming and deformation between them are very frequent that causes packet delivery failure. Therefore, a stable route is always needed to ensure effective data dissemination between source and destination in FANETs. Since it has drastically changing network topology, therefore, to maintain the stable route during packet transmission, there is a need for a suitable routing protocol. This paper proposes an Optimized Location-Aided Routing (O-LAR) protocol which is the modified version of Location-Aided Routing (LAR) protocol. Our protocol’s novelty comes from the fact that it established an optimal route between UAVs for information dissemination towards their respective destination UAV by considering weight function. A weighted function is used to decide the best next-hop node selection based on the parameters like residual energy, distance, and UAV movement direction. The performance of the O-LAR is evaluated mathematically and simulated through the NS-2 simulator. The empirical results attest that O-LAR improves the link duration, network lifetime, packet delivery ratio, and average throughput compared with the state-of-the-art protocols: LEPR, D-LAR, and LAR. Further, the proposed scheme reduces the number of next-hops, routing overhead and end-to-end delay compared to the state-of-the-art protocols.

Predicting Phosphorus and Potato Yield Using Active and Passive Sensors

Applications of remote sensing are important in improving potato production through the broader adoption of precision agriculture. This technology could be useful in decreasing the potential contamination of soil and water due to the over-fertilization of agriculture crops. The objective of this study was to assess the utility of active sensors (Crop Circle™, Holland Scientific, Inc., Lincoln, NE, USA and GreenSeeker™, Trimble Navigation Limited, Sunnyvale, CA, USA) and passive sensors (multispectral imaging with Unmanned Arial Vehicles (UAVs)) to predict total potato yield and phosphorus (P) uptake. The experimental design was a randomized complete block with four replications and six P treatments, ranging from 0 to 280 kg P ha−1, as triple superphosphate (46% P2O5). Vegetation indices (VIs) and plant pigment levels were calculated at various time points during the potato growth cycle, correlated with total potato yields and P uptake by the stepwise fitting of multiple linear regression models. Data generated by Crop Circle™ and GreenSeeker™ had a low predictive value of potato yields, especially early in the season. Crop Circle™ performed better than GreenSeeker™ in predicting plant P uptake. In contrast, the passive sensor data provided good estimates of total yields early in the season but had a poor correlation with P uptake. The combined use of active and passive sensors presents an opportunity for better P management in potatoes.

Nonlinear estimation of quadcopter states using unscented Kalman filter

In recent years, using Unmanned Arial Vehicles (UAV) like quadcopter in civilian and military fields are increased dramatically. Performance and robustness are the most important specifications required for most applications. Different sensors are usually used for a quadcopter to provide the necessary measured states (attitude and position) for control. The white noise generated by physical sensors is one of the important issues that affect the quality of states measurements. The available solutions are still have limited performance for a wide range of nonlinearity. In this paper, Unscented Kalman Filter (UKF) is proposed as a robust estimator that has the ability to work efficiently with high nonlinear systems. Modified PID (PI-D) controller which has better properties than traditional PID controller is used with proposed filter in order to get better performance of quadcopter. The obtained results are compared with that of Extended Kalman Filter (EKF) and proved to be more reliable. Moreover, the results show that the proposed filter largely decreases the error generated by noise and improves the performance of quadcopter better than the EKF.

Adaptive robust nonsingular terminal sliding mode design controller for quadrotor aerial manipulator

In this paper, a novel adaptive control approach for Unmanned Aerial Manipulators (UAMs) is proposed. The UAMs are a new configuration of the Unmanned Arial Vehicles (UAVs) which are characterized by several inhered nonlinearities, uncertainties and coupling. The studied UAM is a Quadrotor endowed with two degrees of freedom robotic arm. The main objectives of our contribution are to achieve both a tracking error convergence by avoiding any singularity problem and also the chattering amplitude attenuation in the presence of perturbations. Therefore, the proposed Adaptive Nonsingular Terminal Super-Twisting controller (ANTSTW) consists of the hybridization of a Nonsingular Terminal Sliding Mode Control and an Adaptive Super Twisting. The adaptive law, which adjust the Super-Twisting’s parameters, is obtained by using stability Lyapunov theorem. Simulation experiments in trajectory tracking mode were realized and compared with Nonsingular Terminal Super-twisting control to prove the superiority and the effectiveness of the proposed approach.

DEVELOPMENT OF ENGINE-AUTOPILOT INTERFACE CIRCUITS FOR UAVS

ABSTRACTThe problem of interfacing Engine Control Units (ECUs) with autopilots in UnmannedArial Vehicles (UAVs), in the presence of modular communication protocols, is aconsiderable concern in the UAV industry. This paper presents a developmentalgorithm for interface circuits between ECUs and autopilots for UAVs. The algorithmdeveloped was based on testing and analysing the communication protocols for boththe autopilot and the ECU. The engine selected for the analysis was the Zanzottera498H engine and the autopilot was a generic one that produces PWM commands forthe actuators. For off-line testing of the developed interface circuit communicationperformance with the ECU, a hardware PWM generator circuit was developed tomimic the generated autopilot protocol signal. The present work illustrates the stepsto analyse both protocols and the procedures to develop such an interface circuit.Finally, the interface circuit was tested experimentally and showed good performancein communication between the autopilot and the ECU.

Underwater noise assessment for energy extraction and production systems using unmanned arial vehicles (UAVs)

Traditional operations such as oil and gas exploration and production have long undergone sound field and environmental impact assessments of underwater acoustic noise. More recently emerging industries such as renewables (wind, wave, and tidal energy production) have also required scrutiny in terms of underwater noise sound fields. To make these assessments, sound fields are typically measured using hydrophones deployed from boats, drifting systems or moored acoustic data loggers. These measurements are often complex and expensive requiring complicated equipment deployments, boat operations and personnel in often-dangerous or hazardous environments. Unmanned Ariel Vehicles (UAV) or drone based technologies offer the opportunity for rapid deployment of smart hydrophone systems arrays over a large spatial area with significantly lower operator and boat interaction improving deployment flexibility, cost and minimising safety concerns for boat based deployments. Results presented are from tests of a prototyp...

Autonomous Formation Flight Algorithm and Platform for Quadrotor UAVs

There are many advantages for having collaborative flight of multiple Unmanned Arial Vehicles (UAVs) rather than a single UAV. In this paper, a proposed formation flight algorithm is tested and a Graphical User Interface (GUI) is implemented on the ground station to allow for path planning and flight control. The proposed algorithm is based on a Hierarchal Leader-Follower formation, where the leader has more flexibility and independence, while the follower is completely dependent on the path taken by the leader. The path planning and the proposed formation flight algorithm are implemented on one axis and tested on two quadrotors. The proposed algorithm is implemented on real quadrotors and its ability to track the given path will be shown.

Studies of Mechanical Properties of Multiwall Nanotube Based Polymer Composites

The two phase polymer composites have been extensively used in various structural applications; however, there is need to further enhance the strength and stiffness of these polymer composites. Carbon nanotubes (CNTs) can be effectively used as secondary reinforcement material in polymer based composites due to their superlative mechanical properties. In this paper, effects of multiwall nanotubes (MWNTs) reinforcement on epoxy–carbon polymer composites are investigated using experiments. MWNTs synthesized by chemical vapor deposition (CVD) technique and amino-functionalization are achieved through acid-thionyl chloride route. Diglycidyl ether of bisphenol-A (DGEBA) epoxy resin with diethyl toluene diamine (DETDA) hardener has been used as matrix. T-300 carbon fabric is used as the primary reinforcement. Three types of test specimen of epoxy–carbon composite are prepared with MWNT reinforcement as 0%, 1%, and 2% MWNT (by weight). The resultant three phase nanocomposites are subjected to tensile test. It has been found that both tensile strength and strain at failure are substantially enhanced with the small addition of MWNT. The analytical results obtained from rule of mixture theory (ROM) shows good agreement with the experimental results. The proposed three phase polymer nanocomposites can find applications in composite structures, ballistic missiles, unmanned arial vehicles, helicopters, and aircrafts.

A Service-Oriented Middleware for Building Collaborative UAVs

For a while, Unmanned Arial Vehicles (UAVs) use was limited to military applications, however recently UAVs are also used for a wide range of civilian applications. Some of these UAV applications m...