Board Of Unmanned Aerial Vehicle Research Articles

A Fixed-Wing UAV Formation Algorithm Based on Vector Field Guidance

The vector field method was originally proposed to guide a single fixed-wing Unmanned Aerial Vehicle (UAV) towards a desired path. In this work, a non-uniform vector field method is proposed that changes in both magnitude and direction, for the purpose of achieving formations of UAVs. As compared to related work in the literature, the proposed formation control law does not need to assume absence of wind. That is, due to the effect of the wind on the UAV, one can handle the UAV air speed being different from its ground speed, and the UAV heading angle being different from its course angle. Stability of the proposed formation method is analyzed via Lyapunov stability theory, and validations are carried out in software-in-the-loop and hardware-in-the-loop comparative experiments. Note to Practitioners—The software-in-the-loop and hardware-in-the-loop experiments, which are done with PX4 autopilot software and hardware, show that the proposed method can be implemented on board of UAVs and integrated with the control architecture of existing autopilot suites. Comparisons with standard formation algorithms show that the proposed method is effective in achieving formation in different path scenarios.

ОБОСНОВАНИЕ ЭЛЕКТРОННОЙ КОМПОНЕНТНОЙ БАЗЫ ДЛЯ АВТОГЕНЕРАТОРОВ ПРИ НАВИГАЦИОННОМ ОБЕСПЕЧЕНИИ БЕСПИЛОТНЫХ ЛЕТАТЕЛЬНЫХ АППАРАТОВ

The study analyzes the tasks of air objects navigation support. The authors conducted a synthesis of a flight route of an unmanned aerial vehicle with the choice of heading angles of flight at turning points using the direction finding signals received from a stationary ground source. The root-mean-square errors are estimated based on the flying tests. These errors determine a location of emitters during multiple estimation of angular coordinates at points separated in space by direction-finding receivers placed on board of unmanned aerial vehicles “Orlan-10”, “Phoenix”, and “Grant-M”. Methods for correcting autonomous flight with increasing and decreasing accuracy in estimating signal arrival directions are studied, which make it possible to minimize positioning errors for a fixed observation time. The study proposes a scheme of a self-excited oscillator on a field-effect transistor for backup command control with automatic heading angle control. A rule is substantiated for constructing a model of a selfexcited oscillator that meets the requirements for a minimum standard deviation calculated using an electrical circuit and a given heading angle with a restriction on the physical feasibility of the layout parameters of a given structure. Recommendations for the implementation of the electronic circuit are formulated and the modes of self-excitation of the oscillator are determined.

High spatio-temporal monitoring of century-old biochar effects on evapotranspiration through the ETLook model: a case study with UAV and satellite image fusion based on additive wavelet transform (AWT)

ABSTRACT It can be challenging to fuse remotely-sensed images with large differences in spatial resolutions. In this paper, we used additive wavelet transform (AWT) to fuse Landsat-8 (30 m) and unmanned aerial vehicle (UAV) images (7 cm and 3.7 cm for thermal and multispectral images, respectively) as one of the primary studies. AWT image fusion generated sharpened Landsat-8 (L-8) images which were significantly correlated with coarse resolution images, while also well preserving the spatial details. Surface albedo (α0), normalized difference vegetation index (NDVI), and surface temperature (ST) were computed from multispectral and thermal sensors on board of UAV and L-8 platforms. High-resolution UAV and AWT sharpened L-8 images were then used in ETLook model to estimate evapotranspiration (ET) across an agricultural farm enriched with century-old biochar. High spatio-temporal analysis demonstrated a significant decrease in α0 across the biochar patches during the early development stages of winter wheat. Moreover, biochar significantly stimulated the development of wheat canopies towards the middle of the cropping season. There were however no impacts at the end of the season due to dense wheat canopies covering the aggravated dark colour soil across the biochar patches. ST was not affected by biochar either at the beginning or towards the end of the season. Neither was there any impact of biochar on actual ET over the season. Our approach can help to develop robust techniques for fusion of UAV and satellite images in light of climate-smart agriculture, and is also applicable to other farms with any specific precision agricultural treatments.

Automatic detection and tracking the moving objects observed by an unmanned aerial vehicles video camera

An algorithm of automatic detection and tracking the moving objects for the use in equipment on board of unmanned aerial vehicles is considered. The developed algorithm is based on a tracking specially selected points for a certain period. Tracked points are selected from the areas on the current frame, where the pixel intensity differs from the intensities of the same pixels in previous frames, aligned with the current frame using projective transformation. If the displacement of the tracked points is not fixed on several adjacent frames, they are being deleted, and new points from the areas presumably belonging to moving objects in the current frame are added instead. On each frame the points similar by the location and shape of trajectories of movement are combined into groups that presumably correspond to moving objects. Objects are tracked by comparing the groups of moving points with the points of neighboring frames. Groups of moving points from neighboring frames are matched if they contain a large number of common tracked points. The algorithm allows simultaneous tracking of more than 20 objects in real time. The indication of objects as moving occurs only if during the time of its tracking it has shifted a considerable distance. The algorithm has a low percentage of false detections of moving objects, it detects well small objects and is capable reliably to accompany moving objects.

Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data

The use of remote sensing data for tree species classification in tropical forests is still a challenging task, due to their high floristic and spectral diversity. In this sense, novel sensors on board of unmanned aerial vehicle (UAV) platforms are a rapidly evolving technology that provides new possibilities for tropical tree species mapping. Besides the acquisition of high spatial and spectral resolution images, UAV-hyperspectral cameras operating in frame format enable to produce 3D hyperspectral point clouds. This study investigated the use of UAV-acquired hyperspectral images and UAV-photogrammetric point cloud (PPC) for classification of 12 major tree species in a subtropical forest fragment in Southern Brazil. Different datasets containing hyperspectral visible/near-infrared (VNIR) bands, PPC features, canopy height model (CHM), and other features extracted from hyperspectral data (i.e., texture, vegetation indices-VIs, and minimum noise fraction-MNF) were tested using a support vector machine (SVM) classifier. The results showed that the use of VNIR hyperspectral bands alone reached an overall accuracy (OA) of 57% (Kappa index of 0.53). Adding PPC features to the VNIR hyperspectral bands increased the OA by 11%. The best result was achieved combining VNIR bands, PPC features, CHM, and VIs (OA of 72.4% and Kappa index of 0.70). When only the CHM was added to VNIR bands, the OA increased by 4.2%. Among the hyperspectral features, besides all the VNIR bands and the two VIs (NDVI and PSSR), the first four MNF features and the textural mean of 565 and 679 nm spectral bands were pointed out as more important to discriminate the tree species according to Jeffries–Matusita (JM) distance. The SVM method proved to be a good classifier for the tree species recognition task, even in the presence of a high number of classes and a small dataset.

Real-Time Model-Free Minimum-Seeking Autotuning Method for Unmanned Aerial Vehicle Controllers Based on Fibonacci-Search Algorithm.

The paper presents a novel autotuning approach for finding locally-best parameters of controllers on board of unmanned aerial vehicles (UAVs). The controller tuning is performed fully autonomously during flight on the basis of predefined ranges of controller parameters. Required controller properties may be simply interpreted by a cost function, which is involved in the optimization process. For example, the sum of absolute values of the tracking error samples or performance indices, including weighed functions of control signal samples, can be penalized to achieve very precise position control, if required. The proposed method relies on an optimization procedure using Fibonacci-search technique fitted into bootstrap sequences, enabling one to obtain a global minimizer for a unimodal cost function. The approach is characterized by low computational complexity and does not require any UAV dynamics model (just periodical measurements from basic onboard sensors) to obtain proper tuning of a controller. In addition to the theoretical background of the method, an experimental verification in real-world outdoor conditions is provided. The experiments have demonstrated a high robustness of the method to in-environment disturbances, such as wind, and its easy deployability.

RADIOMETRIC CORRECTION OF MULTITEMPORAL HYPERSPECTRAL UAS IMAGE MOSAICS OF SEEDLING STANDS

Abstract. Novel miniaturized multi- and hyperspectral imaging sensors on board of unmanned aerial vehicles have recently shown great potential in various environmental monitoring and measuring tasks such as precision agriculture and forest management. These systems can be used to collect dense 3D point clouds and spectral information over small areas such as single forest stands or sample plots. Accurate radiometric processing and atmospheric correction is required when data sets from different dates and sensors, collected in varying illumination conditions, are combined. Performance of novel radiometric block adjustment method, developed at Finnish Geospatial Research Institute, is evaluated with multitemporal hyperspectral data set of seedling stands collected during spring and summer 2016. Illumination conditions during campaigns varied from bright to overcast. We use two different methods to produce homogenous image mosaics and hyperspectral point clouds: image-wise relative correction and image-wise relative correction with BRDF. Radiometric datasets are converted to reflectance using reference panels and changes in reflectance spectra is analysed. Tested methods improved image mosaic homogeneity by 5 % to 25 %. Results show that the evaluated method can produce consistent reflectance mosaics and reflectance spectra shape between different areas and dates.

APPLICATION OF COMPUTER INTELLIGENCE BASED ON GENETIC ALGORITHM IN PROBLEMS OF ROUTING OF UNMANNED AERIAL VEHICLES

The article deals with a possibility of using a genetic algorithm in solving the tasks of optimal selection of routing for unmanned aerial vehicle (UAV) in autonomous flight. Complexity of resolving this task is the need to evaluate the situation at observed objects, to take a decision to change and recalculate the flight path in real time on board of UAV by means of onboard computers. The article reflects the relationship between the application task of monitoring of extended graph on terrain and the mathematical model of optimization by means of genetic algorithm.

Inventory measurements based on non-metric aerial photos from the board of unmanned aerial vehicle (UAV)

Inventory measurements based on non-metric aerial photos from the board of unmanned aerial vehicle (UAV)

THE CONDITIONS OF PREDICTION OF THE PARAMETRIC COMPONENTS OF THE TERMINATION CONTROL SYSTEM OF THE UNMANNED AERIAL VEHICLE FLIGHT PATH

Abstract. According to the results of measurements on board of Unmanned Aerial Vehicle the distancevector D is formed in the inertial coordinate system, and in mathematical modeling - in the Greenwichsystem of coordinates. The velocity vector k V is formed by the navigation system in the trajectory coordinatesystem. Defined by this way the initial conditions gives the possibility to predict the terminal parameters offlight (descent) of Unmanned Aerial Vehicle.Keywords: control system of the unmanned aerial vehicle flight path and landing, motion parameters,termination control.