A method to interpret fracture aperture of rock slope using adaptive shape and unmanned aerial vehicle multi-angle nap-of-the-object photogrammetry

The aperture of natural rock fractures significantly affects the deformation and strength properties of rock masses,as well as the hydrodynamic properties of fractured rock masses.The conventional measurement methods are inadequate for collecting data on high-steep rock slopes in complex mountainous regions.This study establishes a high-resolution three-dimensional model of a rock slope using unmanned aerial vehicle(UAV)multi-angle nap-of-the-object photogrammetry to obtain edge feature points of fractures.Fracture opening morphology is characterized using coordinate projection and transformation.Fracture central axis is determined using vertical measuring lines,allowing for the interpretation of aperture of adaptive fracture shape.The feasibility and reliability of the new method are verified at a construction site of a railway in southeast Tibet,China.The study shows that the fracture aperture has a significant interval effect and size effect.The optimal sampling length for fractures is approximately 0.5e1 m,and the optimal aperture interpretation results can be achieved when the measuring line spacing is 1%of the sampling length.Tensile fractures in the study area generally have larger apertures than shear fractures,and their tendency to increase with slope height is also greater than that of shear fractures.The aperture of tensile fractures is generally positively correlated with their trace length,while the correlation between the aperture of shear fractures and their trace length appears to be weak.Fractures of different orientations exhibit certain differences in their distribution of aperture,but generally follow the forms of normal,log-normal,and gamma distributions.This study provides essential data support for rock and slope stability evaluation,which is of significant practical importance.

A landslide monitoring method using data from unmanned aerial vehicle and terrestrial laser scanning with insufficient and inaccurate ground control points

Non-contact remote sensing techniques,such as terrestrial laser scanning(TLS)and unmanned aerial vehicle(UAV)photogrammetry,have been globally applied for landslide monitoring in high and steep mountainous areas.These techniques acquire terrain data and enable ground deformation monitoring.However,practical application of these technologies still faces many difficulties due to complex terrain,limited access and dense vegetation.For instance,monitoring high and steep slopes can obstruct the TLS sightline,and the accuracy of the UAV model may be compromised by absence of ground control points(GCPs).This paper proposes a TLS-and UAV-based method for monitoring landslide deformation in high mountain valleys using traditional real-time kinematics(RTK)-based control points(RCPs),low-precision TLS-based control points(TCPs)and assumed control points(ACPs)to achieve high-precision surface deformation analysis under obstructed vision and impassable conditions.The effects of GCP accuracy,GCP quantity and automatic tie point(ATP)quantity on the accuracy of UAV modeling and surface deformation analysis were comprehensively analyzed.The results show that,the proposed method allows for the monitoring accuracy of landslides to exceed the accuracy of the GCPs themselves by adding additional low-accuracy GCPs.The proposed method was implemented for monitoring the Xinhua landslide in Baoxing County,China,and was validated against data from multiple sources.

Underdetermined direction of arrival estimation with nonuniform linear motion sampling based on a small unmanned aerial vehicle platform

Uniform linear array(ULA)radars are widely used in the collision-avoidance radar systems of small unmanned aerial vehicles(UAVs).In practice,a ULA's multi-target direction of arrival(DOA)estimation performance suffers from significant performance degradation owing to the limited number of physical elements.To improve the underdetermined DOA estimation performance of a ULA radar mounted on a small UAV platform,we propose a nonuniform linear motion sampling underdetermined DOA estimation method.Using the motion of the UAV platform,the echo signal is sampled at different positions.Then,according to the concept of difference co-array,a virtual ULA with multiple array elements and a large aperture is synthesized to increase the degrees of freedom(DOFs).Through position analysis of the original and motion arrays,we propose a nonuniform linear motion sampling method based on ULA for determining the optimal DOFs.Under the condition of no increase in the aperture of the physical array,the proposed method obtains a high DOF with fewer sampling runs and greatly improves the underdetermined DOA estimation performance of ULA.The results of numerical simulations conducted herein verify the superior performance of the proposed method.

Ground target localization of unmanned aerial vehicle based on scene matching

In order to improve target localization precision,accuracy,execution efficiency,and application range of the unmanned aerial vehicle(UAV)based on scene matching,a ground target localization method for unmanned aerial vehicle based on scene matching(GTLUAVSM)is proposed.The sugges-ted approach entails completing scene matching through a feature matching algorithm.Then,multi-sensor registration is optimized by robust estimation based on homologous registration.Finally,basemap generation and model solution are utilized to improve basemap correspondence and accom-plish aerial image positioning.Theoretical evidence and experimental verification demonstrate that GTLUAVSM can improve localization accuracy,speed,and precision while minimizing reliance on task equipment.

Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

This paper presents a machine-learning method for detecting jamming UAVs and classifying nodes during jamming attacks onWireless Sensor Networks(WSNs).Jamming is a type of Denial of Service(DoS)attack and intentional interference where a malicious node transmits a high-power signal to increase noise on the receiver side to disrupt the communication channel and reduce performance significantly.To defend and prevent such attacks,the first step is to detect them.The current detection approaches use centralized techniques to detect jamming,where each node collects information and forwards it to the base station.As a result,overhead and communication costs increased.In this work,we present a jamming attack and classify nodes into different categories based on their location to the jammer by employing a single node observer.As a result,we introduced a machine learning model that uses distance ratios and power received as features to detect such attacks.Furthermore,we considered several types of jammers transmitting at different power levels to evaluate the proposed metrics using MATLAB.With a detection accuracy of 99.7%for the k-nearest neighbors(KNN)algorithm and average testing accuracy of 99.9%,the presented solution is capable of efficiently and accurately detecting jamming attacks in wireless sensor networks.

Review of Effective Vegetation Mapping Using the UAV (Unmanned Aerial Vehicle) Method

We tried more precise mapping of vegetation using UAV?(unmanned aerial vehicle), as a new method of creating vegetation maps, and we?objected to be clearly the efficient mapping of vegetation using the UAV method by comparing vegetation maps created by analysing aerial photographs taken by a UAV and an aircraft (manned flight). The aerial photography using UAV was conducted in the Niida River estuary (the secondary river flowing into Minamisoma City in Fukushima Prefecture, Japan). The photography period was in August 2013. We analysed the aerial photographs using ArcGis 9 (Esri Japan Corporation, Tokyo, Japan). The aerial photographs of the main plant communities (Phragmites australis,?Typha domingensis, and?Miscanthus sacchariflorus) taken by the UAV could clearly discriminate each plant community at the 1/50 scale. Moreover, it could clearly discriminate the shape of a plant at the 1/10 scale. We compared the vegetation maps by analysing the aerial photos taken by a UAV (2013 shooting) and an aircraft (2011 shooting). As a result, the vegetation map created by the UAV method could clearly discriminate community distributions. We conclude that vegetation surveys using UAV are possible and are capable of a highly precise community division in places where field reconnaissance is difficult. The UAV method is effective and will contribute to the improvement of research methods in the future;this method may reduce research costs associated with a reduction in field survey days and man-power.

A new approach to study terrestrial yardang geomorphology based on high-resolution data acquired by unmanned aerial vehicles(UAVs): A showcase of whaleback yardangs in Qaidam Basin, NW China

Yardangs are wind-eroded ridges usually observed in arid regions on Earth and other planets. Previous geomorphology studies of terrestrial yardang fields depended on satellite data and limited fieldwork. The geometry measurements of those yardangs based on satellite data are limited to the length, the width, and the spacing between the yardangs; elevations could not be studied due to the relatively low resolution of the satellite acquired elevation data, e.g. digital elevation models(DEMs). However, the elevation information(e.g. heights of the yardang surfaces) and related information(e.g. slope) of the yardangs are critical to understanding the characteristics and evolution of these aeolian features. Here we report a novel approach, using unmanned aerial vehicles(UAVs) to generate centimeterresolution orthomosaics and DEMs for the study of whaleback yardangs in Qaidam Basin, NW China. The ultra-high-resolution data provide new insights into the geomorphology characteristics and evolution of the whaleback yardangs in Qaidam Basin. These centimeter-resolution datasets also have important potential in:(1) high accuracy estimation of erosion volume;(2) modeling in very fine scale of wind dynamics related to yardang formation;(3) detailed comparative planetary geomorphology study for Mars, Venus, and Titan.

High Spatial Resolution Mapping of Dykes Using Unmanned Aerial Vehicle(UAV) Photogrammetry: New Insights On Emplacement Processes

Remote sensing has played a pivotal role in our understanding of the geometry of dykes and dyke swarms on Earth,Venus and Mars(West and Ernst,1991;Mege and Masson,1995;Ernst et al.,2005).Since the 1970’s

Contour Based Path Planning with B-Spline Trajectory Generation for Unmanned Aerial Vehicles (UAVs) over Hostile Terrain

This research focuses on trajectory generation algorithms that take into account the stealthiness of autonomous UAVs;generating stealthy paths through a region laden with enemy radars. The algorithm is employed to estimate the risk cost of the navigational space and generate an optimized path based on the user-specified threshold altitude value. Thus the generated path is represented with a set of low-radar risk waypoints being the coordinates of its control points. The radar-aware path planner is then approximated using cubic B-splines by considering the least radar risk to the destination. Simulated results are presented, illustrating the potential benefits of such algorithms.

Technical Specification for Application of Unmanned Aerial Vehicle( UAV) Monitoring of Rocky Desertification in Karst Area

Taking the opportunity of the third monitoring of rocky desertification in the karst area of China,Zigui County of Hubei Province applied Unmanned Aerial Vehicle( UAV) for the first time for monitoring. Through repeated trials and studies,it established technical requirements including the UAV monitoring technology for the rocky desertification,the feature point photographing,UAV video judgment of rocky desertification degree,UAV video correction misclassification subcompartment,and UAV video observation of rocky desertification control. It completed the third rocky desertification monitoring task of karst area in Zigui County.

Rotary unmanned aerial vehicles path planning in rough terrain based on multi-objective particle swarm optimization

This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,length and angle variable rate.First,a three-dimensional(3D)modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs.Considering the length,height and tuning angle of a path,the path planning of R-UAVs is described as a tri-objective optimization problem.Then,an improved multi-objective particle swarm optimization algorithm is developed.To render the algorithm more effective in dealing with this problem,a vibration function is introduced into the collided solutions to improve the algorithm efficiency.Meanwhile,the selection of the global best position is taken into account by the reference point method.Finally,the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine.Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.

Multi-objective Optimization Design of Vented Cylindrical Airbag Cushioning System for Unmanned Aerial Vehicle

Multi-objective optimization design of the gas-filled bag cushion landing system is investigated.Firstly,the landing process of airbag is decomposed into a adiabatic compression and a release of landing shock energy,and the differential equation of cylindrical gas-filled bag is presented from a theoretical perspective based on the ideal gas state equation and dynamic equation.Then,the effects of exhaust areas and blasting pressure on buffer characteristics are studied,taking those parameters as design variable for the multiobjective optimization problem,and the solution can be determined by comparing Pareto set,which is gained by NSGA-Ⅱ.Finally,the feasibility of the design scheme is verified by experimental results of the ground test.

Outage Performance of Non-Orthogonal Multiple Access Based Unmanned Aerial Vehicles Satellite Networks

With rapid development of unmanned aerial vehicles(UAVs), more and more UAVs access satellite networks for data transmission. To improve the spectral efficiency, non-orthogonal multiple access(NOMA) is adopted to integrate UAVs into the satellite network, where multiple satellites cooperatively serve the UAVs and mobile terminal using the Ku-band and above. Taking into account the rain fading and the fading correlation, the outage performance is first analytically obtained for fixed power allocation and then efficiently calculated by the proposed power allocation algorithm to guarantee the user fairness. Simulation results verify the outage performance analysis and show the performance improvement of the proposed power allocation scheme.

Flight Control System of Unmanned Aerial Vehicle

To date unmanned aerial system(UAS)technologies have attracted more and more attention from countries in the world.Unmanned aerial vehicles(UAVs)play an important role in reconnaissance,surveillance,and target tracking within military and civil fields.Here one briefly introduces the development of UAVs,and reviews its various subsystems including autopilot,ground station,mission planning and management subsystem,navigation system and so on.Furthermore,an overview is provided for advanced design methods of UAVs control system,including the linear feedback control,adaptive and nonlinear control,and intelligent control techniques.Finally,the future of UAVs flight control techniques is forecasted.

Tree species classification using deep learning and RGB optical images obtained by an unmanned aerial vehicle

The diversity of tree species and the complexity of land use in cities create challenging issues for tree species classification.The combination of deep learning methods and RGB optical images obtained by unmanned aerial vehicles(UAVs) provides a new research direction for urban tree species classification.We proposed an RGB optical image dataset with 10 urban tree species,termed TCC10,which is a benchmark for tree canopy classification(TCC).TCC10 dataset contains two types of data:tree canopy images with simple backgrounds and those with complex backgrounds.The objective was to examine the possibility of using deep learning methods(AlexNet,VGG-16,and ResNet-50) for individual tree species classification.The results of convolutional neural networks(CNNs) were compared with those of K-nearest neighbor(KNN) and BP neural network.Our results demonstrated:(1) ResNet-50 achieved an overall accuracy(OA) of 92.6% and a kappa coefficient of 0.91 for tree species classification on TCC10 and outperformed AlexNet and VGG-16.(2) The classification accuracy of KNN and BP neural network was less than70%,while the accuracy of CNNs was relatively higher.(3)The classification accuracy of tree canopy images with complex backgrounds was lower than that for images with simple backgrounds.For the deciduous tree species in TCC10,the classification accuracy of ResNet-50 was higher in summer than that in autumn.Therefore,the deep learning is effective for urban tree species classification using RGB optical images.

Circular Formation Flight Control for Unmanned Aerial Vehicles With Directed Network and External Disturbance

This paper proposes a new distributed formation flight protocol for unmanned aerial vehicles(UAVs)to perform coordinated circular tracking around a set of circles on a target sphere.Different from the previous results limited in bidirectional networks and disturbance-free motions,this paper handles the circular formation flight control problem with both directed network and spatiotemporal disturbance with the knowledge of its upper bound.Distinguishing from the design of a common Lyapunov fiunction for bidirectional cases,we separately design the control for the circular tracking subsystem and the formation keeping subsystem with the circular tracking error as input.Then the whole control system is regarded as a cascade connection of these two subsystems,which is proved to be stable by input-tostate stability(ISS)theory.For the purpose of encountering the external disturbance,the backstepping technology is introduced to design the control inputs of each UAV pointing to North and Down along the special sphere(say,the circular tracking control algorithm)with the help of the switching function.Meanwhile,the distributed linear consensus protocol integrated with anther switching anti-interference item is developed to construct the control input of each UAV pointing to east along the special sphere(say,the formation keeping control law)for formation keeping.The validity of the proposed control law is proved both in the rigorous theory and through numerical simulations.

Three-dimensional multi-constraint route planning of unmanned aerial vehicle low-altitude penetration based on coevolutionary multi-agent genetic algorithm

To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration,a novel route planning method was proposed.First and foremost,a coevolutionary multi-agent genetic algorithm (CE-MAGA) was formed by introducing coevolutionary mechanism to multi-agent genetic algorithm (MAGA),an efficient global optimization algorithm.A dynamic route representation form was also adopted to improve the flight route accuracy.Moreover,an efficient constraint handling method was used to simplify the treatment of multi-constraint and reduce the time-cost of planning computation.Simulation and corresponding analysis show that the planning results of CE-MAGA have better performance on terrain following,terrain avoidance,threat avoidance (TF/TA2) and lower route costs than other existing algorithms.In addition,feasible flight routes can be acquired within 2 s,and the convergence rate of the whole evolutionary process is very fast.

Risk Assessment of Unmanned Aerial Vehicle Flight Based on Kmeans Clustering Algorithm

To quantify unmanned aerial vehicle(UAV)flight risks in low-altitude airspace,we analyze the factors of UAV flight risks from three aspects:flight conflict,flight environment,and traffic characteristics.The aerial risk index and ground risk index of the UAV are constructed,the index screening model and the UAV flight risk assessment model are established,and a UAV flight risk assessment model based on K-means clustering has been proposed.Meanwhile,numerical simulations show the proposed method can not only evaluate the UAV flight risks effectively,but also provide technical support for UAV risk management and control.

Analysis of unmanned aerial vehicle navigation and height control system based on GPS

According to the characteristic of global positioning system（GPS） reflection signals,a GPS delay mapping receiver system scheme is put forward,which not only satisfies the unmanned aerial vehicle（UAV） guidance localization but also realizes height measurement.A code delay algorithm is put forward,which processes the direct and land reflected signal and outputs the navigation data and specular point.The GPS terrain reflected echo signal mathematical equation is inferred.The reflecting signal area,when the GPS signal passes the land,is analyzed.The height survey model reflected land surface characteristic is established.A simulation system which carries guidance localization of the UAV and the height measuring control through the GPS direct signal and the land reflected signal is designed,taken the GPS satellite as the illumination source,the receiver is put on the UAV.Then the UAV guidance signal,the GPS reflection signal and receiver＇s parallel processing are realized.The parallel processing reduces UAV＇s payload and raises system＇s operating efficiency.The simulation results confirms the validity of the model and also provides the basis for the UAV＇s optimization design.

Placement of unmanned aerial vehicles as communication relays in two-tiered multi-agent system:clustering based methods

The network performance and the unmanned aerial vehicle(UAV)number are important objectives when UAVs are placed as communication relays to enhance the multi-agent information exchange.The problem is a non-deterministic polynomial hard(NP-hard)multi-objective optimization problem,instead of generating a Pareto solution,this work focuses on considering both objectives at the same level so as to achieve a balanced solution between them.Based on the property that agents connected to the same UAV are a cluster,two clustering-based algorithms,M-K-means(MKM)and modified fast search and find density of peaks(MFSFDP)methods,are first proposed.Since the former algorithm requires too much computational time and the latter one requires too many relays,an algorithm for the balanced network performance and relay number(BPN)is proposed by discretizing the area to avoid missing the optimal relay positions and defining a new local density function to reflect the network performance metric.Simulation results demonstrate that the proposed algorithms are feasible and effective.Comparisons between these algorithms show that the BPN algorithm uses fewer relay UAVs than the MFSFDP and classic set-covering based algorithm,and its computational time is far less than the MKM algorithm.