Low-cost Multi-UAV Technologies for Contour Mapping of Nuclear Radiation Field

Low cost UAVs are becoming more and more popular in both research and practical applications, and it leads to a new, potentially significant service product known as UAV-based personal remote sensing (PRS). Multi-UAV system with advanced cooperative control algorithms has advantages over single UAV system, especially in time urgent tasks such as detecting nuclear radiation before deploying the salvage. This paper considers two scenarios for nuclear radiation detection using multiple UAVs, of which contour mapping of the nuclear radiation is simulated. Then, for real applications, this paper presents a low-cost UAV platform with built-in formation flight control architecture together with a formulated standard flight test routine. Three experimental formation flight scenarios that imitate the nuclear detection missions are prepared for contour mapping of nuclear radiation field in 3D space.     

Safety,Security, and Rescue Missions with an Unmanned Aerial Vehicle (UAV)

Several missions with an Unmanned Aerial Vehicle (UAV) in different realistic safety, security, and rescue field tests are presented. First, results from two safety and security missions at the 2009 European Land Robot Trials (ELROB) are presented. A UAV in form of an Airrobot AR100-B is used in a reconnaissance and in a camp security scenario. The UAV is capable of autonomous waypoint navigation using onboard GPS processing. A digital video stream from the vehicle is used to create photo maps—also known as mosaicking—in real time at the operator station. This mapping is done using an enhanced version of Fourier Mellin based registration, which turns out to be very fast and robust. Furthermore, results from a rescue oriented scenario at the 2010 Response Robot Evaluation Exercises (RREE) at Disaster City, Texas are presented. The registration for the aerial mosaicking is supplemented by an uncertainty metric and embedded into Simultaneous Localization and Mapping (SLAM), which further enhances the photo maps as main mission deliveries.     

Design,control, and visual navigation of the DelftaCopter VTOL tail‐sitter UAV

To participate in the Outback Medical Express UAV Challenge 2016, a vehicle was designed and tested that can autonomously hover precisely, takeoff and land vertically, fly fast forward efficiently, and use computer vision to locate a person and a suitable landing location. The vehicle is a novel hybrid tail‐sitter combining a delta‐shaped biplane fixed‐wing and a conventional helicopter rotor. The rotor and wing are mounted perpendicularly to each other,and the entire vehicle pitches down to transition from hover to fast forward flight where the rotor serves as propulsion. To deliver sufficient thrust in hover while still being efficient in fast forward flight, a custom rotor system was designed. The theoretical design was validated with energy measurements, wind tunnel tests, and application in real‐world missions. A rotor‐head and corresponding control algorithm were developed to allow transitioning flight with the nonconventional rotor dynamics that are caused by the fuselage rotor interaction. Dedicated electronics were designed that meet vehicle needs and comply with regulations to allow safe flight beyond visual line of sight. Vision‐based search and guidance algorithms running on a stereo‐vision fish‐eye camera were developed and tested to locate a person in cluttered terrain never seen before. Flight tests and a competition participation illustrate the applicability of the DelftaCopter concept.     

A Framework for Simulation and Testing of UAVs in Cooperative Scenarios

Today, Unmanned Aerial Vehicles (UAVs) have deeply modified the concepts of surveillance, Search&Rescue, aerial photogrammetry, mapping, etc. The kinds of missions grow continuously; missions are in most cases performed by a fleet of cooperating autonomous and heterogeneous vehicles. These systems are really complex and it becomes fundamental to simulate any mission stage to exploit benefits of simulations like repeatability, modularity and low cost. In this paper a framework for simulation and testing of UAVs in cooperative scenarios is presented. The framework, based on modularity and stratification in different specialized layers, allows an easy switching from simulated to real environments, thus reducing testing and debugging times, especially in a training context. Results obtained using the proposed framework on some test cases are also reported.     

LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR CONTROL

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.     

GPS‐denied Indoor and Outdoor Monocular Vision Aided Navigation and Control of Unmanned Aircraft

GPS‐denied closed‐loop autonomous control of unstable Unmanned Aerial Vehicles (UAVs) such as rotorcraft using information from a monocular camera has been an open problem. Most proposed Vision aided Inertial Navigation Systems (V‐INSs) have been too computationally intensive or do not have sufficient integrity for closed‐loop flight. We provide an affirmative answer to the question of whether V‐INSs can be used to sustain prolonged real‐world GPS‐denied flight by presenting a V‐INS that is validated through autonomous flight‐tests over prolonged closed‐loop dynamic operation in both indoor and outdoor GPS‐denied environments with two rotorcraft unmanned aircraft systems (UASs). The architecture efficiently combines visual feature information from a monocular camera with measurements from inertial sensors. Inertial measurements are used to predict frame‐to‐frame transition of online selected feature locations, and the difference between predicted and observed feature locations is used to bind in real‐time the inertial measurement unit drift, estimate its bias, and account for initial misalignment errors. A novel algorithm to manage a library of features online is presented that can add or remove features based on a measure of relative confidence in each feature location. The resulting V‐INS is sufficiently efficient and reliable to enable real‐time implementation on resource‐constrained aerial vehicles. The presented algorithms are validated on multiple platforms in real‐world conditions: through a 16‐min flight test, including an autonomous landing, of a 66 kg rotorcraft UAV operating in an unconctrolled outdoor environment without using GPS and through a Micro‐UAV operating in a cluttered, unmapped, and gusty indoor environment. © 2013 Wiley Periodicals, Inc.     

UAV Localization in Row Crops

High‐flying unmanned aerial vehicles (UAVs) are transforming industrial and research agriculture by delivering high spatiotemporal resolution data on a field environment. While current UAVs fly high above fields collecting aerial imagery, future low‐flying aircraft will directly interact with the environment and will utilize a wider variety of sensors. Safely and reliably operating close to unstructured environments requires improving UAVs' sensing, localization, and control algorithms. To this end, we investigate localizing a micro‐UAV in corn phenotyping trials using a laser scanner and IMU to control the altitude and position of the vehicle relative to the plant rows. In this process, the laser scanner is not only a means of localization, but also a scientific instrument for measuring plant properties. Experimental evaluations demonstrate that the is capable of safely and reliably operating in real‐world phenotyping trials. We experimentally validate the system in both low and high wind conditions in fully mature corn fields. Using test data from 18 test flights, we show that the UAV is capable of localizing its position to within one field row of the true position.     

某大机动无人机基于飞行任务的敏捷性评估飞行科目设计与仿真

由于现代空战的特点，根据时间量级分类的单维度的敏捷性评估已经不能满足空战的需求。为了优化控制系统的设计，最大限度地发挥飞机的飞行性能，需要对大机动无人机进行基于飞行任务的敏捷性评估。针对大机动无人机敏捷性评估的特点，从20个标准评估机动任务集中选取了3种与某无人机实际使用相关性较强的飞行任务作为其评估机动，展开了无人机基于飞行任务的敏捷性评估和仿真验证。对选取的3种飞行任务进行了详细的敏捷性评估方法描述，在通用的无人机敏捷性评估仿真环境中对加入非线性飞行控制律的某大机动无人机进行了基于3种飞行任务的敏捷性评估仿真验证，根据评估结果对控制律进行调参，为控制律设计提供指南和优化依据。     

无人机飞行控制系统软件测试策略的研究

无人机飞行控制系统是无人机机载设备中最重要的部分之一;对无人机飞行控制系统软件进行严格测试是保证其质量的重要手段;首先介绍软件测试的通用的基本理论和基本方法;然后以某型无人机飞行控制系统软件测试为案例,研究了无人机飞行控制系统软件的特点;提出了一种基于经验反馈的软件测试模型,并分析了该模型的特点;最后提出了一套适用于无人机飞行控制系统软件测试的策略。     

高实时性的无人机飞控软件测试系统设计

机载计算机作为无人机系统的控制核心,在设计过程中对其进行性能监测及功能验证是保证其正常工作的基本途径;运行于机载计算机内的飞控软件已成为无人机飞控系统设计中最重要的因素之一,采用Labview和RTX搭配的建模方式,模拟无人机机载计算机的真实硬件环境,在Windows操作系统下构建一套高实时性的测试系统方案,完成对机载计算机飞控软件的实时监测评估和性能测试;经实际测试,该系统能够实现对无人机飞控软件的实时监测和测试,实时性带来的实时误差不超过1％.     

A binocular vision-based UAVs autonomous aerial refueling platform

Unmanned aerial vehicles (UAVs) are highly focused and widely used in various domains, and the capability of autonomous aerial refueling (AAR) becomes increasingly important. Most of the research in this area concerns the verification of the algorithms while the experiments are conducted on the ground. In this work, in order to verify the vision system designed for boom approach AAR, an integrated platform is built and tested. The platform consists of a tanker UAV, a receiver UAV and a ground station. The pictures of the marker on the receiver UAV are captured by the binocular vision system on the tanker UAV and then used for flight control and boom control. Performance and feasibility of the platform are demonstrated by the real out-door flight tests, and the experimental results verified the feasibility and effectiveness of our developed binocular vision-based UAVs AAR.     

EKF Based Attitude Estimation and Stabilization of a Quadrotor UAV Using Vanishing Points in Catadioptric Images

In recent years, Unmanned Air Vehicles (UAVs) have become more and more important. These vehicles are employed in many applications from military operations to civilian tasks. Under situations where global positioning system (GPS) and inertial navigation system (INS) do not function, or as an additional sensor, computer vision can be used. Having 360° view, catadioptric cameras might be very useful as they can be used as measurement units, obstacle avoidance sensors or navigation planners. Although many innovative research has been done about this camera, employment of such cameras in UAVs is very new. In this paper, we present the use of catadioptric systems in UAVs to estimate vehicle attitude using parallel lines that exist on many structures in an urban environment. After explanation of the algorithm, the UAV modeling and control will be presented. In order to increase the estimation and control speed an Extended Kalman Filter (EKF) and multi-threading are used and speeds up to 40 fps are obtained. Various simulations have been done to present the effectiveness of the estimation algorithms as well as the UAV controllers. A custom test stand has been designed to perform successful experiments on the UAV. Finally, we will present the experiments and the results of the estimation and control algorithms on a real model helicopter. EKF based attitude estimation and stabilization using catadioptric images has found to be a reliable alternative to other sensor usage.     

无人机平台下的行人与车辆目标实时检测

在无人机图像中快速准确地检测行人和车辆是一项有意义但又极具挑战的任务,其广泛应用于军事侦察、交通管制以及偏远地区救援等任务中。然而,由于无人机属于小型移动设备,其内存和计算能力非常有限,使得如何保证其检测实时性一直是难题。针对SSD算法模型过大、运行内存占用量过高、很难在无人机设备上运行的问题,精心设计了轻量级的基准网络,通过削减原始网络的通道数目以及卷积数目来降低网络的参数量;针对无人机场景下目标小、场景复杂等问题,提出轻量级感受野模块来增强网络特征表示能力,并结合上下文信息来进一步提高小型目标的检测精度。实验结果表明,提出的方法在基于无人机的行人与车辆目标检测任务上有较高的准确性和实时性。     

Design and implementation of an autonomous flight control law for a UAV helicopter

In this paper, we present the design and implementation of an autonomous flight control law for a small-scale unmanned aerial vehicle (UAV) helicopter. The approach is decentralized in nature by incorporating a newly developed nonlinear control technique, namely the composite nonlinear feedback control, together with dynamic inversion. The overall control law consists of three hierarchical layers, namely, the kernel control, command generator and flight scheduling, and is implemented and verified in flight tests on the actual UAV helicopter. The flight test results demonstrate that the UAV helicopter is capable of carrying out complicated flight missions autonomously.     

基于一致性的多无人机编队算法研究

多无人机协同编队是无人机技术发展的重要方向,相比于单架无人机,编队协同能够增大搜索面积,提高无人机执行任务的效率;为了提高编队控制算法的控制效果和执行效率,以一致性理论为基础,针对无人机的特点和实际飞行情况,设计了改进的一致性编队控制算法,对一致性变量的边界问题进行平滑设计以及限幅处理,设计了σ-范数来调整合适的控制量,通过分段规划获得最佳的轨迹控制效果,并在控制协议中加入对目标点控制,同时对控制算法中的参数进行优化设计,从而实现无人机一致性的稳定控制;最后根据无人机领航—跟随的控制逻辑设计了一致性编队算法,实现了不同紧密编队队形任务;仿真结果表明了经过改进的一致性编队控制算法的有效性。     

Real Time Relative and Absolute Dynamic Localization of Air–Ground Wireless Sensor Networks

A novel approach for relative and absolute localization of wireless sensor nodes using a potential field method is presented. The main idea of our work is to develop relative and absolute localization algorithms for the position estimate of stationary unattended ground sensor (UGS) nodes using a potential field method. A dynamical model is derived for each sensor node to estimate the relative and absolute position estimates under the influence of a certain fictitious virtual force. In the algorithm the sensor nodes do not move physically, but a virtual motion is carried out to generate optimal position estimates. The convergence of the estimator system to a least squares solution is guaranteed using Lyapunov theory. Separate control algorithms for relative and absolute localization are developed which guarantee the convergence of the position estimates. The relative localization algorithm assumes that distance (i.e. range) measurements between UGS nodes are available and for absolute localization algorithm, uninhabited aerial vehicles (UAV) are available with on board GPS such that they have absolute position information together with range measurement information. In the relative localization algorithm the UGS nodes are localized with respect to an internal co-ordinate frame. In absolute localization the UGS nodes are localized with respect to the known absolute position of UAV in the air–ground network. The effectiveness of the control algorithm is highlighted by the real time implementation results.     

Decentralized planning and control for UAV–UGV cooperative teams

In this paper we study a symbiotic aerial vehicle-ground vehicle robotic team where unmanned aerial vehicles (UAVs) are used for aerial manipulation tasks, while unmanned ground vehicles (UGVs) aid and assist them. UGV can provide a UAV with a safe landing area and transport it across large distances, while UAV can provide an additional degree of freedom for the UGV, enabling it to negotiate obstacles. We propose an overall system control framework that includes high-accuracy motion planning for each individual robot and ad-hoc decentralized mission planning for complex missions. Experimental results obtained in a mockup arena for parcel transportation scenario show that the system is able to plan and execute missions in various environments and that the obtained plans result in lower energy consumption.     

Rolling Horizon Path Planning of an Autonomous System of UAVs for Persistent Cooperative Service: MILP Formulation and Efficient Heuristics

A networked system consisting of unmanned aerial vehicles (UAVs), automated logistic service stations (LSSs), customer interface software, system orchestration algorithms and UAV control software can be exploited to provide persistent service to its customers. With efficient algorithms for UAV task planning, the UAVs can autonomously serve the customers in real time. Nearly uninterrupted customer service may be accomplished via the cooperative hand-off of customer tasks from weary UAVs to ones that have recently been replenished at an LSS. With the goal of enabling the autonomy of the task planning tasks, we develop a mixed integer linear programming (MILP) formulation for the problem of providing simultaneous. UAV escort service to multiple customers across a field of operations with multiple sharable LSSs. This MILP model provides a formal representation of our problem and enables use in a rolling horizon planner via allowance of arbitrary UAV initial locations and consumable reservoir status (e.g., battery level). As such, it enables automation of the orchestration of system activities. To address computational complexity, we develop efficient heuristics to rapidly derive near optimal solutions. A receding horizon task assignment (RHTA) heuristic and sequential task assignment heuristic (STAH) are developed. STAH exploits properties observed in optimal solutions obtained for small problems via CPLEX. Numerical studies suggest that RHTA and STAH are 45 and 2100 times faster than solving the MILP via CPLEX, respectively. Both heuristics perform well relative to the optimal solution obtained via CPLEX. An example demonstrating the use of the approach for rolling horizon planning is provided.     

Search based software testing of object-oriented containers

Automatic software testing tools are still far from ideal for real world object-oriented (OO) software. The use of nature inspired search algorithms for this problem has been investigated recently. Testing complex data structures (e.g., containers) is very challenging since testing software with simple states is already hard. Because containers are used in almost every type of software, their reliability is of utmost importance. Hence, this paper focuses on the difficulties of testing container classes with nature inspired search algorithms. We will first describe how input data can be automatically generated for testing Java containers. Input space reductions and a novel testability transformation are presented to aid the search algorithms. Different search algorithms are then considered and studied in order to understand when and why a search algorithm is effective for a testing problem. In our experiments, these nature inspired search algorithms seem to give better results than the traditional techniques described in literature. Besides, the problem of minimising the length of the test sequences is also addressed. Finally, some open research questions are given.     

On the Scheduling of Systems of UAVs and Fuel Service Stations for Long-Term Mission Fulfillment

The duration of missions that can be accomplished by a system of unmanned aerial vehicles (UAVs) is limited by the battery or fuel capacity of its constituent UAVs. However, a system of UAVs that is supported by automated refueling stations may support long term or even indefinite duration missions. We develop a mixed integer linear program (MILP) model to formalize the problem of scheduling a system of UAVs and multiple shared bases in disparate geographic locations. There are mission trajectories that must be followed by at least one UAV. A UAV may hand off the mission to another in order to return to base for fuel. To address the computational complexity of the MILP formulation, we develop a genetic algorithm to find feasible solutions when a state-of-the-art solver such as CPLEX cannot. In practice, the approach allows for a long-term mission to receive uninterrupted UAV service by successively handing off the task to replacement UAVs served by geographically distributed shared bases.