UAV System Research Articles

Automatic bridge crack detection device based on quadrotor UAV

For the bridge located in deep valley, with high tower and long span structure, the traditional artificial crack detection can not meet the needs of today. In order to solve this problem, a new bridge crack detection equipment is designed based on quadrocopter unmanned aerial vehicle. According to this equipment, the developed system contains: reconnaissance unmanned aerial vehicle system, acquisition and transmission system, crack positioning system, and graphics operations system. Reconnaissance UAV system is improved based on quadrocoper UAV to meet the requirements of flight power, flight control, flight safety and signal stability. In the integrated transmission system, the designed UAV equipped with Hawkeye 5S motion camera, and 4G communication is used to complete the real-time image transmission; The crack location system uses BDS / GPS dual-mode combined single-point positioning module to realize the accurate location of bridge surface cracks; Graphics operations system, based on C++ and python language, designed a superpotent actual-time video streaming graphics operations method, and used median filter, image grayscale, histogram equalization, threshold segmentation and other methods to process the crack image. The designed bridge crack detection equipment can effectively solve the problem of low crack detection efficiency, and realize the intelligence and automation of crack identification. It has practical engineering application value and promotes the development of bridge crack detection technology.

An Algorithm for Local Dynamic Map Generation for Safe UAV Navigation

For safe UAV navigation and to avoid collision, it is essential to have accurate and real-time perception of the environment surrounding the UAV, such as free area detection and recognition of dynamic and static obstacles. The perception system of the UAV needs to recognize information such as the position and velocity of all objects in the surrounding local area regardless of the type of object. At the same time, a probability based representation taking into account the noise of the sensor is also essential. In addition, a software design with efficient memory usage and operation time is required in consideration of the hardware limitations of the UAVs. In this paper, we propose a 3D Local Dynamic Map (LDM) generation algorithm for a perception system for UAVs. The proposed LDM uses a circular buffer as a data structure to ensure low memory usage and fast operation speed. A probability based occupancy map is created using sensor data and the position and velocity of each object are calculated through clustering between grid voxels using the occupancy map and velocity estimation based on a particle filter. The objects are predicted using the position and velocity of each object and this is reflected in the occupancy map. This process is continuously repeated and the flying environment of the UAV can be expressed in a three-dimensional grid map and the state of each object. For the evaluation of the proposed LDM, we constructed simulation environments and the UAV for outdoor flying. As an evaluation factor, the occupancy grid is accuracy evaluated and the ground truth velocity and the estimated velocity are compared.

Hazardous flight region prediction for a small UAV operated in an urban area using a deep neural network

With an increase of UAVs in logistics and transportation, the safety of UAVs operated in the urban wind environment becomes an important issue. Small UAVs are more sensitive to the wind environment because of their small size, slow cruising speed, and limited endurance. In the unmanned aircraft system traffic management (UTM), a safety risk assessment under bad weather conditions is an important component. In this study, a hazardous flight region prediction system for small UAVs operated in urban areas is developed using a deep neural network (DNN) to support a risk assessment and safe trajectory planning. A large eddy simulation (LES) is applied to reflect the terrain-driven wind environment in the urban area. The result of a weather research and forecasting (WRF) model is used as an initial and boundary condition of the LES to generate a realistic complicated wind environment in an urban area. Furthermore, an iterative nesting algorithm is applied to the LES to obtain a sufficient resolution of the wind environment, which is suitable for the small UAV scale. The deviation distance from the original flight path due to the wind environment is considered as a flight hazard criterion in this study. The proposed system is able to predict deviation distance due to the wind environment over the entire flight space over time by using the DNN model. The training data for the DNN is obtained using the multicopter flight dynamics simulator, which can take into account the influence of a specific wind environment. With the indexes considering this deviation distance and the local topography (distribution of buildings) in the urban area, the hazardous flight region is predicted. The information supplied by the proposed hazardous flight region prediction model can be used for the flight risk assessment and safe flight trajectory planning to increase the flight safety of small UAVs.

Determination of optimal flight altitude to minimise acoustic drone disturbance to wildlife using species audiograms

Abstract Unmanned aerial vehicles (UAVs) are increasingly important in wildlife data collection but concern over wildlife disturbance has led several countries to ban their use in National Parks. Disturbance is an animal welfare concern and impedes scientific data collection through provoking aberrant behaviour. Dealing with the issue of disturbance will enable wildlife researchers to use UAV technology more effectively and ethically. Here we present a novel method to determine optimal flight altitude for minimising drone disturbance for wildlife using species audiograms. We recorded sound profiles of seven common UAV systems in the horizontal and vertical planes at 5‐m increments up to 120 m. To understand how mammals perceive UAV sound, we used audiograms of 20 species to calculate the loudness of each UAV for each species across the measured distances. These calculations filter the UAV noise based on the sensitivity of species’ hearing over the relevant frequency spectrum. We have devised a method to optimise the trade‐off between image spatial resolution and flight altitude. We calculated the lowest point at which either the UAV sound level decreases below an acceptable threshold, here chosen as 40 dB, weighted according to species’ hearing sensitivity, or disturbance cannot be significantly further minimised by flying higher. The latter is quantified as the point above which each additional 5 m of flight altitude causes on average less than 0.05 dB decrease in sound pressure level. Reliable data on appropriate flight altitudes can guide policy regulations on flying UAVs over wildlife, thus enabling increased use of this technology for scientific data collection and for wildlife conservation purposes. The methodology is readily applicable to other species and UAV systems for which sound recordings and audiograms are available.

Evaluation of the soil profile quality of subsided land in a coal mining area backfilled with river sediment based on monitoring wheat growth biomass with UAV systems.

Underground coal mining leads to land subsidence, and the situation is particularly serious in the Coal-Grain Complex in eastern China, causing the crop production to be reduced or to be taken out. Backfilling with Yellow River sediment is one of the effective methods to solve the land subsidence in this area, but a key issue is how to select the optimal soil reconstruction profile so that the crop yield after backfilling and reclamation is unaffected. The main purpose of this study is to verify the feasibility of selecting the optimal soil reconstruction profile by rapid monitoring of crop growth and judging soil quality with the aid of unmanned aerial vehicle systems (UAVs). A control treatment and 13 experimental treatments were established for the study area. The control treatment consisted of using 30cm topsoil and 90cm subsoil and the topsoil is a proxy for native (undisturbed) soil from the study sites. All other treatments consisted of using varying combinations of subsoil and sediment overlain by 30cm of topsoil. The vegetation indices from the UAV multispectral images, and the plant height and vegetation coverage from the UAV RGB images were used for estimation of the winter wheat biomass in a random forest regression. The results showed that the random forest regression model yielded accurate estimation of the aboveground biomass. Furthermore, knowledge of plant height and vegetation coverage improved the accuracy of prediction such that crop growth was well characterized. The optimal soil profile consisted of 0.3m topsoil + 0.2m subsoil + 0.2m sediment + 0.2m subsoil + 0.3m sediment. A fast and effective airborne monitoring method for soil quality was established, thus providing greatly improved monitoring efficiency.

Development and Implementation of a Laser–Camera–UAV System to Measure Total Dynamic Transverse Displacement

AbstractRailroad bridge inspectors are interested in measuring the maximum total transverse displacement of railroad bridges under trains, but these values are generally not easy to obtain in the f...

The Status Quo of US Coast Guard Aviation Force Construction and the Development and Application of UAV System

The Status Quo of US Coast Guard Aviation Force Construction and the Development and Application of UAV System

Robust Model Reference Adaptive Control for Tail-Sitter VTOL Aircraft

This study presents a control design of roll motion for a vertical take-off and landing unmanned air vehicle (VTOL-UAV) design based on the Model Reference Adaptive Control (MRAC) scheme in the hovering flight phase. The adaptive laws are developed for the UAV system under nonparametric uncertainty (gust and wind disturbance). Lyapunov-based stability analysis of the adaptive controlled UAV system under roll motion has been conducted and the adaptive laws have been accordingly developed. The Uniform Ultimate Boundness (UUB) of tracking error has been proven and the stability analysis showed that the incorporation of dead-zone modification in adaptive laws could guarantee the uniform boundness of all signals. The computer simulation has been conducted based on a proposed controller for tracking control of the roll motion. The results show that the drift, which appears in estimated gain behaviors due to the application of gust and wind disturbance, could be stopped by introducing dead-zone modification in adaptive laws, which leads to better robustness characteristics of the adaptive controller.

Research on UAV Route Optimization Method Based on Double Target of Confidence and Ambiguity.

In recent years, with the continuous development of drone technology, UAVs are used as unmanned and flightable devices, UAV plays an important role in remote sensing and GIS disciplines. During the flight, no one directly participates in flight-related decisions such as flight routes, path planning, and flight control. In this case, it is necessary to use the computing power of the onboard computer of the UAV system, the computing power of the ground station computer, and related technologies such as detecting sensing, image vision, real-time wireless communication, etc., to develop target planning, decision-making and control algorithms for specific problems, and to solve the problem. Flight planning and flight control issues in machine applications. The UAV route optimization method based on the double target of confidence and ambiguity has positive significance for route optimization and wide application of UAV. In this context, this paper aims to analyze and study the UAV route optimization method based on the two goals of confidence and ambiguity, and optimized the method of drone route. The calculation results show that, compared with other methods, this method can make the UAV not rely on human control, but realize the use of fuzzy control method to identify the target and track the moving target.

Unmanned Aerial Vehicle system layout based on two-layer interconnection model

Based on the Australian wildfire in 2019, this paper obtains the grid risk coefficient through the cellular automata model, and linear model and uses the cluster gravity method to select the location of EOC. Finally it gives the two-layer interconnection model constructed by SSA UAV and radio repeater UAV and the UAV system model of UAV two-layer interconnection coverage planning. It aims to create a safe and economic disaster monitoring system. The security, that is, the connectivity coverage rate of UAV system and the economy, that is, the total number of UAVs, are taken as the objective functions. The constraints are the minimum standard limit of coverage rate and connectivity rate, the limit of divided area, the limit of divided area inspection cycle, and the limit of the relationship between the built-in battery consumption and the number of on-the-job UAVs. According to the PSO-ACO fusion algorithm, the optimal number and combination of the two types of UAVs are obtained. The adaptability of the model to extreme fire events in the next ten years is verified by grey weight Markov prediction.

Forest Wildfire Monitoring and Communication UAV System Based on Particle Swarm Optimization

Forest fire is one of the major disasters faced by the society at present. During the drought every year, devastating wildfires often occur all over the world, causing serious economic losses and life injuries. The effective layout of unmanned aerial vehicles in the monitoring system is the key to effectively prevent forest wildfires. Wildfires occur suddenly and rapidly, and most of them occur in dense forest areas which are difficult for firefighters to reach. Considering the factors of economy, security and capability, an efficient and accurate integrated optimization layout model of UAV is proposed. The UAV monitoring system based on this model can make the emergency operation centre best guide the active crew through two-way radio communication, so as to achieve the best effect and maximum safety. We optimized the number and combination of drones by using optimization algorithm, and got the highest scoring drone procurement plan in different types of areas. For example, when meeting low terrain without obstacles, the optimal number of drones for SSA is 20 and for Radio Repeater is 1 in high risk. Finally, we analysed the influence of global climate change, fire scale, UAV density and terrain on the optimal location of UAV.

Conversion control of a tilt tri-rotor unmanned aerial vehicle with modeling uncertainty

The tilt-rotor unmanned aerial vehicle (TRUAV) has vertical take-off and landing and high-speed flight capabilities through conversion and reconversion modes, thereby presenting wide application prospects. This article presents a compact tilt tri-rotor UAV. For flying in the conversion mode, the TRUAV needs to realize the transition of two control logic and two kinds of actuators in both of the rotor and fixed-wing modes, achieving the control of the multi-input multioutput, input nonaffine, and nonlinear multichannel cross-coupling UAV system. Furthermore, the lateral dynamics of a tilt tri-rotor UAV is more unstable for unpaired rotors. These system characteristics present a great challenge to conversion control. To solve these problems, the nonlinear dynamic model of the vehicle in the conversion mode is provided. Furthermore, a cascade control system consisting of position control, velocity control, angle control, angular velocity control, and control mixer is proposed. The simulation result of the control system shows steady flight and a fast transition in the conversion mode with modeling uncertainty in case of no wind.

A nonlinear optimal control approach for the UAV and suspended payload system

The article proposes a nonlinear optimal control approach for the UAV and suspended load system. The dynamic model of the UAV and payload system undergoes approximate linearization with the use of Taylor series expansion around a temporary operating point which recomputed at each iteration of the control method. For the approximately linearized model an H-infinity feedback controller is designed. The linearization procedure relies on the computation of the Jacobian matrices of the state-space model of the system. The proposed control method stands for the solution of the optimal control problem for the nonlinear and multivariable dynamics of the UAV and payload system, under model uncertainties and external perturbations. For the computation of the controller’s feedback gains an algebraic Riccati equation is solved at each time-step of the control method. The new nonlinear optimal control approach achieves fast and accurate tracking for all state variables of the UAV and payload system, under moderate variations of the control inputs. The stability properties of the control scheme are proven through Lyapunov analysis.

Design of UAV Autonomous Charging Pad for Surveillance

The conventional unmanned aerial vehicles have limited supply of power due to which they are grounded after designated hours of flight. In this paper, the autonomous charging of UAVs is proposed, developed, implemented to increase the battery charging and operational life. The development of automated charging system for UAVs is achieved by utilizing the solar based charging system by means of charging stations marked throughout the route of a UAV. The drone, according to the predefined programming, evaluates its charging level and when the level drops below the set threshold, it lands on a landing site. The UAV can charge itself through contact-based charging functionality without a human’s interference. This allows the UAVs to stay in flight for longer periods of time and does not requires intervention of humans to change the battery pack. This will increase the efficiency in terms of surveillance and allows the legal agencies carrying out surveillance to maintain their cover and achieve footage which might have been lost while landing the UAV to change the battery pack. This system can perform surveillance to be carried out by a single UAV as it does not rely upon human’s support for the supply of power. The improvement in the battery life allows UAVs to hover for longer hours thus avoiding the risk of crash landings and damage to the UAVs.

Under-Canopy UAV Laser Scanning Providing Canopy Height and Stem Volume Accurately

The automation of forest field reference data collection has been an intensive research objective for laser scanning scientists ever since the invention of terrestrial laser scanning more than two decades ago. In this study, we demonstrated that an under-canopy UAV laser scanning system utilizing a rotating laser scanner can alone provide accurate estimates of canopy height and stem volume for the majority of trees in a boreal forest. We mounted a rotating laser scanner based on a Velodyne VLP-16 sensor onboard a manually piloted UAV. The UAV was commanded with the help of a live video feed from the onboard camera. Since the system was based on a rotating laser scanner providing varying view angles, all important elements such as treetops, branches, trunks, and ground could be recorded with laser hits. In an experiment including two different forest structures, namely sparse and obstructed canopy, we showed that our system can measure the heights of individual trees with a bias of −20 cm and a standard error of 40 cm in the sparse forest and with a bias of −65 cm and a standard error of 1 m in the obstructed forest. The accuracy of the obtained tree height estimates was equivalent to airborne above-canopy UAV surveys conducted in similar forest conditions or even at the same sites. The higher underestimation and higher inaccuracy in the obstructed site can be attributed to three trees with a height exceeding 25 m and the reduced point density of these tree tops due to occlusion and the limited ranging capacity of the scanner. Additionally, we used our system to estimate the stem volumes of individual trees with a standard error at the level of 10%. This level of error is equivalent to the error obtained when merging above-canopy UAV laser scanner data with terrestrial point cloud data. The results show that we do not necessarily need a combination of terrestrial point clouds and point clouds collected using above-canopy UAV systems in order to accurately estimate the heights and the volumes of individual trees in reference data collection.

A UAV-based machine vision method for bridge crack recognition and width quantification through hybrid feature learning

Bridge crack width is an important indicator to assess and evaluate the health condition of the bridge. In this paper, we have proposed a UAV-based machine vision method for bridge crack recognition and width quantification through hybrid feature learning. Firstly, we have configured a UAV system that can obtain bridge crack image (effective pixel 7952x5304) and GPS position, calculate image resolution, and correct measured plane simultaneously. Then, the crack recognition method combining the R-FCN network and Haar-AdaBoost suited for UAV imagery recognition is proposed, which can make full use of advanced features, shape features and gray features of bridge cracks. The time cost of our method is about 0.2 s for crack detection per one 7952 × 5304 pixels images and 4.5 s in pixel-level segmentation per one 1000 × 1000 pixels bounding box. Additionally, the real bridge crack widths are calculated and quantified by the ranging method using corresponding object distance data. Finally, a case study of the Xiangjiang-River bridge inspection is carried out to demonstrate the effectiveness of the proposed method, achieving above 90% precision in the real bridge crack width quantification.

МЕТОД ОЦЕНКИ КООРДИНАТ БЛА ПО ИЗМЕРЕННЫМ ЛОКАЛЬНЫМ РАССТОЯНИЯМ МЕЖДУ ЭЛЕМЕНТАМИ ГРУППЫ

Important problems in the development of mobile robotics are the task of autonomous navigation,automatic movement control and providing a reliable communication channel. For navigation,an unmanned aerial vehicle can use its own inertial navigation system and a satellite navigationsystem. The purpose of this article is to develop a method for reducing errors in the operationof the inertial navigation system of UAVs caused by the presence of random and systematic errors.In this case, we consider the situation of a monotonous increase in the systematic error over time.Usually, navigation data obtained from the satellite does not contain a significant systematic errorin determining the coordinates. However, the satellite signal may be lost for a time significantlylonger than the period of transmission of navigation data from the satellite in normal mode.As a result, there is a problem of increasing the accuracy of the data received from the inertialnavigation system. This problem is particularly relevant for group application of UAVs. Whensolving group control tasks, it becomes necessary to prevent vehicle collisions and possible collisionsalready at the stage of traffic planning. In addition, to solve a number of group tasks, such asmonitoring the terrain, conducting rescue operations, searching for objects in a given area, andjoint cargo transportation, individual objects of the group must move smoothly in space with greataccuracy. This imposes more stringent restrictions on the accuracy of the inertial navigation systemsprocessing and the frequency of information exchange. In this paper, we propose a methodthat allows, based on data obtained from local systems that measure the mutual distances betweenobjects in a group. This information allows correct the estimates of their own coordinates in sucha way as to reduce the standard deviation of the corrected set of points from the true positions ofobjects at a given time. The method also reduces the maximum value of the corresponding deviationin comparison with the original set of estimates obtained from the navigation data of the INS.The method is demonstrated by the example of increasing the accuracy of determining global coordinatesin a group of UAVs.

Impact of the Electromagnetic Environment on UAV’s Datalink

The deployment of drones for any mission relies heavily on a ground control system. The UAV must therefore maintain a stable datalink with the ground control station to allow it to be controlled in real time by the ground operator. The evolution of drones in specific electromagnetic environments can seriously compromise data linkage, giving rise to electromagnetic susceptibility (EMS) issues. This article presents a review of the exploratory literature of electromagnetic interferences that the datalinks between UAVs and the ground station can face from the point of view of electromagnetic compatibility (EMC). The goal is to understand the effects of a given EM environment on the datalinks of an UAV system in order to predict its behaviour and perform the necessary precautions and improvements.

Distributed event triggering control for six-rotor UAV systems with asymmetric time-varying output constraints

Distributed event triggering control for six-rotor UAV systems with asymmetric time-varying output constraints

Research on the application of computer track planning algorithm in uav power line patrol system

Unmanned aerial vehicle (UAV) has the advantages of high safety, accurate detection results and long time continuous operation in power grid patrol inspection. Article several kinds of commonly used in electric power inspection for unmanned aerial vehicle flight mode is analyzed, and the planning track of unmanned aerial vehicle (uav) in power patrol autonomous optimization, power patrol scheme based on path planning was proposed, at the same time on the ground calibration start and end position of the workstation USES route planning algorithm for flight points, of course, so as to realize the tracking of on line.