Real-time video exploitation system for small UAVs

In recent years, networks for unmanned aircraft vehicle (UAV)-assisted device-to-device (D2D) communication have attracted attention, as they can be employed for emergency communication in the event of a large-scale disaster. However, many existing UAV-assisted D2D communication technology strategies neglect the UAV energy consumption during flight. Further, only the links between the UAV and ground devices are evaluated, without assumption of a wide information diffusion range. Herein, we propose a novel multihop D2D communication method that exploits UAV technology and uniquely designed virtual cells to achieve information diffusion to remote areas. Moreover, we establish a model to evaluate the trade-off between the UAV flight energy consumption and the communication performance between the UAV and mobile devices. By employing the constructed model and demonstrating the energy efficiency trend, we confirm the effectiveness of the proposed method combining multihop D2D communication and UAV technology.

In this contribution two different types of broadband antennas namely a logarithmic-periodic dipole antenna (LPDA) and a transversal electromagnetic (TEM) horn antenna for ground penetrating synthetic aperture radar sensors are presented. These antennas are designed to be mounted on an unmanned aircraft vehicle (UAV). The antennas are evaluated in terms of their matching, radiation pattern pulse response and weight. Finally, an integration example of one of these antennas is shown on an UAV.

In the last few years, radio technologies for unmanned aircraft vehicle (UAV) have advanced very rapidly. The increasing need to fly unmanned aircraft systems (UAS) in the national airspace system (NAS) to perform missions of vital importance to national security, defense, and science has pushed ahead the design and implementation of new radio platforms. However, a lot still has to be done to improve those radios in terms of performance and capabilities. In addition, an important aspect to account for is hardware cost and the feasibility to implement these radios using commercial off-the-shelf (COTS) components. UAV radios come with numerous technical challenges and their development involves contributions at different levels of the design. Cognitive algorithms need to be developed in order to perform agile communications using appropriate frequency allocation while maintaining safe and efficient operations in the NAS and, digital reconfigurable architectures have to be designed in order to ensure a prompt response to environmental changes. Command and control (C2) communications have to be preserved during “standard” operations while crew operations have to be minimized. It is clear that UAV radios have to be software-defined systems, where size, weight and power consumption (SWaP) are critical parameters. This paper provides preliminary results of the efforts performed to design a fully digital radio architecture as part of a NASA Phase I STTR. In this paper, we will explain the basic idea and technical principles behind our dynamic/adaptive frequency hopping radio for UAVs. We will present our Simulink model of the dynamic FH radio transmitter design for UAV communications and show simulation results and FPGA system analysis.

Recent developments in Unmanned Aircraft Vehicles (UAVs), thermal imaging, and Auto-machine learning (AutoML) have shown high potential for precise wildlife surveys but have rarely been studied for habitat assessment. Here, we propose a framework that leverages these advanced techniques to achieve cost-effective habitat quality assessment from the perspective of actual wildlife community usage. The framework exploits vision intelligence hidden in the UAV thermal images and AutoML methods to achieve cost-effective wildlife distribution mapping, and then derives wildlife use indicators to imply habitat quality variance. We conducted UAV-based thermal wildlife surveys at three wetlands in the Rainwater Basin, Nebraska. Experiments were set to examine the optimal protocols, including various flight designs (61 and 122 m), feature types, and AutoML. The results showed that UAV images collected at 61 m with a spatial resolution of 7.5 cm, combined with Faster R-CNN, returned the optimal wildlife mapping (more than 90% accuracy). Results also indicated that the vision intelligence exploited can effectively transfer the redundant AutoML adaptation cycles into a fully automatic process (with around 33 times efficiency improvement for data labeling), facilitating cost-effective AutoML adaptation. Eventually, the derived ecological indicators can explain the wildlife use status well, reflecting potential within- and between-habitat quality variance.

This paper presents a new evolutionary planner for optimizing the input commands of multiple Unmanned Aircraft Vehicles (UAVs) in target search missions. This planner minimizes the target detection time and maximizes the UAVs performance, given 1) the uncertainty in the target location and sensor information, and 2) the UAV motion and sensorial payload models. On one hand, it calculates the detection time related criteria using Bayesian theory to handle the uncertainty of the problem. On the other hand, it measures the UAVs performance against a real kinematic model that takes into account some environmental effects. Besides, it exploits the typical versatility and good performance of evolutionary algorithms to tackle this search problem as a multi-objective and multi-stepped receding horizon controller, capable of providing acceptable long term (less-myopic) decisions due to a novel optimization criterium that weights the future expected observations with the UAV manoeuvrability constraints. All these properties let it handle successfully the minimum time target detection task in real world scenarios, as the results analyzed in this paper, obtained over different setups, show.

A novel battery thermal management system for an unmanned aerial vehicle using the graphene directional heat transfer structure

Cooperative and non-cooperative Sense-and-Avoid (SAA) capabilities are key enablers for Unmanned Aircraft Vehicle (UAV) to safely and routinely access all classes of airspace. In this paper state-of-the-art cooperative and non-cooperative SAA sensor/system technologies for small-to-medium size UAV are identified and the associated multi-sensor data fusion techniques are introduced. A reference SAA system architecture is presented based on Boolean Decision Logics (BDL) for selecting and sorting non-cooperative and cooperative sensors/systems including both passive and active Forward Looking Sensors (FLS), Traffic Collision Avoidance System (TCAS) and Automatic Dependent Surveillance - Broadcast (ADS-B). After elaborating the SAA system processes, the key mathematical models associated with both non-cooperative and cooperative SAA functions are presented. The analytical models adopted to compute the overall uncertainty volume in the airspace surrounding an intruder are described. Based on these mathematical models, the SAA Unified Method (SUM) for cooperative and non-cooperative SAA is presented. In this unified approach, navigation and tracking errors affecting the measurements are considered and translated to unified range and bearing uncertainty descriptors, which apply both to cooperative and non-cooperative scenarios. Simulation case studies are carried out to evaluate the performance of the proposed SAA approach on a representative host platform (AEROSONDE UAV) and various intruder platforms. Results corroborate the validity of the proposed approach and demonstrate the impact of SUM towards providing a cohesive logical framework for the development of an airworthy SAA capability, which provides a pathway for manned/unmanned aircraft coexistence in all classes of airspace.

The development technology of Unmanned Aircraft Vehicles (UAV) allows the improvement of new applications and it may be used to obtain geo-spatial information with high precision. In this study we present the evaluation of an ortho-mosaic and a digital surface model (DSM), which both have been generated from information received by UAV flights. The horizontal control points were obtained by precision positioning with GPS and they were referred at SIRGAS system, epoch 2014.0. The vertical points were obtained by spirit leveling referred at vertical datum “La Libertad” in Ecuador. These results indicate that it is possible to realize cartography scales 1:500 scales, and digital elevation model a 1:500 scales with contour line of 1 m. Such high resolution of the UAV has been used to evaluate the damage provoked by the 7.8 Mw earthquake, which occurred at the 16th of April 2016 in the coastal part of Ecuador. The quick application of the UAV and the generation of such images and data obtained, supported the rescue teams in the recovery of victims and helped to improve a fast evaluation and quantification of the collapses of the infrastructure.KeywordsUAVGPSSpirit levelingOrtho-mosaicDSMEcuador

<strong class="journal-contentHeaderColor">Abstract.</strong> We developed an innovative tool to quantify CO₂ and CH₄ emissions at the scale of an industrial site, based on a mass balance approach relying on a newly developed light-weight (1.4 kg) open path laser absorption spectrometer operable on-board Unmanned Aircraft Vehicles (UAVs). This spectrometer simultaneously records in situ CO₂ and CH₄ concentrations at high frequency (24 Hz in this study) with precisions of 10 ppb for CH₄ and 1 ppm for CO₂ averaged at 1 Hz. The large range of measurable concentrations, up to 1000 ppm for CO₂ and 200 ppm for CH₄, makes this analyzer suitable for operation on industrial sites at a short distance from the emission sources, therefore avoiding many logistical and legal limits associated with most long-range airborne observations. To quantify the emissions, high spatial resolution atmospheric concentration measurements obtained throughout a plume cross-section downwind of a source within the limited UAV flight period are exploited by calculations using a mass balance approach. This high spatial resolution, allowed by the high acquisition frequency, limits the use of horizontal interpolation, thus gaining in precision compared to current airborne alternative quantification techniques. A field validation campaign, conducted on the TotalEnergies TADI test platform at Lacq, France, consisted in controlled CO₂ and CH₄ leak experiments to which several institutes participated with various measurement systems (gas LiDAR, multispectral camera, infrared camera including concentrations and emissions quantification system, acoustic sensors, ground mobile and fixed Cavity RingDown Spectrometers). Our method was proved suitable to detect leaks during controlled release experiments with emission fluxes down to 0.01 g s^&minus;1, with 24 % of estimated CH₄ fluxes within the &minus;20 % to +20 % error range, 80 % of quantifications within the &minus;50 % to +100 % error range and all of our results within the &minus;69 % to +150 % error range. Such precision levels are better ranked than current top-down alternative techniques to quantify CH₄ at comparable spatial scales. Observations across the plume of two offshore oil and gas platforms operated by TotalEnergies in the North Sea were used to quantify the instantaneous greenhouse gases emissions of these facilities and are coherent with reference emissions for these platforms estimated by mass balance and combustion calculations for CO₂. The operational deployment of such instruments and quantification methods, on a large scale and on a regular basis, potentially with fully autonomous UAVs, will allow the quantification of the time dependent greenhouse gases emissions of numerous oil and gas facilities.

The sustainable intensification of forage production in Mediterranean climates requires technological solutions that optimize the use of agricultural inputs. This study aimed to evaluate the performance of proximal optical sensors in recommending and monitoring variable rate nitrogen fertilization in winter forage crops cultivated under Mediterranean conditions. A handheld multispectral active sensor (HMA), a multispectral camera on an unmanned aircraft vehicle (UAV), and one passive on-the-go sensor (OTG) were used to generate real-time nitrogen (N) application prescriptions. The sensors were assessed for their correlation with agronomic parameters such as plant fresh matter (PFM), plant dry matter (PDM), plant N content (PNC), crude protein (CP) in%, crude protein yield (CPyield) per unit of area, and N uptake (NUp). The real-time N fertilization stood out by promoting a 15.23% reduction in the total N fertilizer applied compared to a usual farmer-fixed dose of 150 kg ha−1, saving 22.90 kg ha−1 without compromising crop productivity. Additionally, NDVI_OTG showed moderate simple linear correlation with PFM (R2 = 0.52), confirming its effectiveness in prescription based on vegetative vigor. UAV_II (NDVI after fertilization) showed even stronger correlations with CP (R2 = 0.58), CPyield (R2 = 0.53), and NUp (R2 = 0.53), highlighting its sensitivity to physiological responses induced by N fertilization. Although the HMA sensor operates via point readings, it also proved effective, with significant correlations to NUp (R2 = 0.55) and CPyield (R2 = 0.53). It is concluded that integrating sensors enables both precise input prescription and efficient monitoring of plant physiological responses, fostering cost-effectiveness, sustainability, and improved agronomic efficiency.

Antennas on changing platforms without hindering the device performance is a fairly novel concept in antenna technologies. Reconfigurability in terms of the real estate can open new avenues for antenna designs that haven't been explored before. This paper investigates a `peel off' antenna which can be mounted on any desirable surface and can be made operational. The suggested antenna is reusable and can be made to stick on multiple surfaces based on the operational requirements at a certain point in time. This research is primarily focused on antennas for small unmanned aircraft vehicles (sUAV) and hence both conformal and planar designs are investigated.

GPS receiver is widely integrated into the autopilot for Small Unmanned Aircraft Vehicle (SUAV). The flight control loop within the autopilot uses the position and velocity information provided by GPS receiver to control the SUVA to flight through the desired path. So for tightly tracking a desired flight path, it is essential that GPS receiver can provide precise-enough position and velocity information. After selecting the RCB-4H GPS receiver, it is of great significant that the receiver undertakes as many necessary tests as possible before a real flight test. By using the facility of GSS6560 GPS simulator, a series of test were designed and conducted on the ground. Here three tests of them which focus on the evaluation of specifications of position accuracy and acquisition time are described in detail, which includes the test design, test data processing and analysis. The results show that the RCB-4H GPS receiver meets some specifications under certain environment.

This paper proposed fuzzy PID controller which prevents compressor surge and reduces the acceleration time of the fuel flow control system for turbo-jet engine. The fuzzy PID controller is to stabilize the unmanned aircraft vehicle upon occurring unexpected engine surge. This controller is designed by applying fuzzy PID control algorithm, inferred by applying Mamdani’s inference method and defuzzified by using the center of gravity method. Fuzzy inference results are used as the fuel flow control inputs to prevent compressor surge and flame-out for turbo-jet engine. The controller is designed to converge to the desired speed quickly and safely. The performance of the proposed controller is verified by performing computer simulations with MATLAB.KeywordsTurbojet engineFuzzy PID controlUAVFuel flow controlSurge control

This paper presents a platform for airborne sensor applications using low-cost, open-source components carried by an easy-to-fly unmanned aircraft vehicle (UAV). The system, available in open-source , is designed for researchers, students and makers for a broad range of exploration and data-collection needs. The main contribution is the extensible architecture for modularized airborne sensor deployment and real-time data visualisation. Our open-source Android application provides data collection, flight path definition and map tools. Total cost of the system is below 800 dollars. The flexibility of the system is illustrated by mapping the location of Bluetooth beacons (iBeacons) on a ground field and by measuring water temperature in a lake.

In this study, we present a class of nonlinear analytical solutions for the dynamics of a fixed wing unmanned aircraft vehicle (UAV). These solutions are needed for the integration and fusion of sensor data for input to guidance and control algorithms. Derivation and integration of the 3-rd order vector differential equation of motion, and its applications to various dynamical models are presented. It is assumed that (a) acceleration due to aerodynamic lift, and the difference between the propulsive thrust and aerodynamic drag accelerations are not changed; (b) the bank angle is zero; (c) the sideslip angle is zero. The general integral and the corresponding analytical solutions for a class of flight trajectories consist of six independent integrals for heading angle, magnitude of velocity vector, time, altitude, and two components of the position vector. This explicit expression with respect to the governing parameters facilitates its direct incorporation into the development and design of trajectories, targeting, guidance and control schemes. It is shown that the first integrals which have been shown valid for a variety of aircraft platforms, re-entry vehicles and missiles, can specifically be applied to UAVs in which such control solutions are needed for sense and avoid situations. An illustrative example highlights the applicability of the general integral for range of trajectories and conditions pertinent to UAV flight patterns.