Guidance and nonlinear control system for autonomous flight of minirotorcraft unmanned aerial vehicles

Small unmanned aerial vehicles (UAVs) are becoming popular among researchers and vital platforms for several autonomous mission systems. In this paper, we present the design and development of a miniature autonomous rotorcraft weighing less than 700 g and capable of waypoint navigation, trajectory tracking, visual navigation, precise hovering, and automatic takeoff and landing. In an effort to make advanced autonomous behaviors available to mini‐ and microrotorcraft, an embedded and inexpensive autopilot was developed. To compensate for the weaknesses of the low‐cost equipment, we put our efforts into designing a reliable model‐based nonlinear controller that uses an inner‐loop outer‐loop control scheme. The developed flight controller considers the system's nonlinearities, guarantees the stability of the closed‐loop system, and results in a practical controller that is easy to implement and to tune. In addition to controller design and stability analysis, the paper provides information about the overall control architecture and the UAV system integration, including guidance laws, navigation algorithms, control system implementation, and autopilot hardware. The guidance, navigation, and control (GN&C) algorithms were implemented on a miniature quadrotor UAV that has undergone an extensive program of flight tests, resulting in various flight behaviors under autonomous control from takeoff to landing. Experimental results that demonstrate the operation of the GN&C algorithms and the capabilities of our autonomous micro air vehicle are presented. © 2009 Wiley Periodicals, Inc.     

Optic flow-based vision system for autonomous 3D localization and control of small aerial vehicles

The problem considered in this paper involves the design of a vision-based autopilot for small and micro Unmanned Aerial Vehicles (UAVs). The proposed autopilot is based on an optic flow-based vision system for autonomous localization and scene mapping, and a nonlinear control system for flight control and guidance. This paper focusses on the development of a real-time 3D vision algorithm for estimating optic flow, aircraft self-motion and depth map, using a low-resolution onboard camera and a low-cost Inertial Measurement Unit (IMU). Our implementation is based on 3 Nested Kalman Filters (3NKF) and results in an efficient and robust estimation process. The vision and control algorithms have been implemented on a quadrotor UAV, and demonstrated in real-time flight tests. Experimental results show that the proposed vision-based autopilot enabled a small rotorcraft to achieve fully-autonomous flight using information extracted from optic flow.     

Mini Rotorcraft Flight Formation Control Using Bounded Inputs

In this paper, the flight formation control and trajectory tracking control design of multiple mini rotorcraft systems are discussed. The dynamic model of a mini rotorcraft is presented using the Newton-Euler formalism. Our approach is based on a leader/follower structure of multiple robot systems. The centroid of the coordinated control subsystem is used for trajectory tracking purposes. A nonlinear controller based on separated saturations and a multi-agent consensus algorithm is developed. The analytic results are supported by simulation tests. Experimental results include yaw coordination and tracking only.     

Decentralised Standoff Tracking of Moving Targets Using Adaptive Sliding Mode Control for UAVs

This paper proposes a decentralised vector field guidance algorithm for coordinated standoff tracking of a ground moving target by multiple UAVs. In particular, this study introduces additional adaptive terms in an existing sliding mode control concept for standoff tracking guidance, in order to reduce the effect of unmodelled dynamics and disturbances. Decentralised angular separation control between UAVs, in conjunction with decentralised estimation, is also introduced using either velocity or orbit radius change by different information/communication structures. Numerical simulations are performed to verify the feasibility and benefits of the proposed approach under a realistic ground vehicle tracking scenario, using multiple UAVs having unknown parameters in the heading-hold autopilot.     

A Software-in-the-Loop Implementation of Adaptive Formation Control for Fixed-Wing UAVs

This paper discusses the design and software-in-the-loop implementation of adaptive formation controllers for fixed-wing unmanned aerial vehicles (UAVs) with parametric uncertainty in their structure, namely uncertain mass and inertia. In fact, when aiming at autonomous flight, such parameters cannot assumed to be known as they might vary during the mission (e.g. depending on the payload). Modeling and autopilot design for such autonomous fixed-wing UAVs are presented. The modeling is implemented in Matlab, while the autopilot is based on ArduPilot, a popular open-source autopilot suite. Specifically, the ArduPilot functionalities are emulated in Matlab according to the Ardupilot documentation and code, which allows us to perform software-in-the-loop simulations of teams of UAVs embedded with actual autopilot protocols. An overview of realtime path planning, trajectory tracking and formation control resulting from the proposed platform is given. The software-inthe-loop simulations show the capability of achieving different UAV formations while handling uncertain mass and inertia.      

Vision-aided Estimation of Attitude,Velocity, and Inertial Measurement Bias for UAV Stabilization

This paper studies vision-aided inertial navigation of small-scale unmanned aerial vehicles (UAVs) in GPS-denied environments. The objectives of the navigation system are to firstly online estimate and compensate the unknown inertial measurement biases, secondly provide drift-free velocity and attitude estimates which are crucial for UAV stabilization control, and thirdly give relatively accurate position estimation such that the UAV is able to perform at least a short-term navigation when the GPS signal is not available. For the vision system, we do not presume maps or landmarks of the environment. The vision system should be able to work robustly even given low-resolution images (e.g., 160 ×120 pixels) of near homogeneous visual features. To achieve these objectives, we propose a novel homography-based vision-aided navigation system that adopts four common sensors: a low-cost inertial measurement unit, a downward-looking monocular camera, a barometer, and a compass. The measurements of the sensors are fused by an extended Kalman filter. Based on both analytical and numerical observability analyses of the navigation system, we theoretically verify that the proposed navigation system is able to achieve the navigation objectives. We also show comprehensive simulation and real flight experimental results to verify the effectiveness and robustness of the proposed navigation system.     

多无人机目标追踪自适应控制

针对多无人机自动驾驶仪速度与航向角通道系数未知的目标轨迹追踪问题,提出一种自适应追踪控制方法.通过对自动驾驶仪通道系数进行在线估计,解决了系数未知所造成的设计困难.为克服外界干扰及系统内部误差因素对无人机运动控制系统的影响,设计了补偿项来消除干扰项的影响.采用Lyapunov定理证明了轨迹追踪误差最终可收敛于任意小的区域内.仿真结果验证了所设计方法的有效性.     

Optic flow regulation: the key to aircraft automatic guidance

We have developed a visually based autopilot which is able to make an air vehicle automatically take off, cruise and land, while reacting appropriately to wind disturbances (head wind and tail wind). This autopilot consists of a visual control system that adjusts the thrust so as to keep the downward optic flow (OF) at a constant value. This autopilot is therefore based on an optic flow regulation loop. It makes use of a sensor, which is known as an elementary motion detector (EMD). The functional structure of this EMD was inspired by that of the housefly, which was previously investigated at our Laboratory by performing electrophysiological recordings while applying optical microstimuli to single photoreceptor cells of the insect's compound eye.

We built a proof-of-concept, tethered rotorcraft that circles indoors over an environment composed of contrasting features randomly arranged on the floor. The autopilot, which we have called OCTAVE (Optic flow based Control sysTem for Aerial VEhicles), enables this miniature (100 g) rotorcraft to carry out complex tasks such as ground avoidance and terrain following, to control risky maneuvers such as automatic take off and automatic landing, and to respond appropriately to wind disturbances. A single visuomotor control loop suffices to perform all these reputedly demanding tasks. As the electronic processing system required is extremely light-weight (only a few grams), it can be mounted on-board micro-air vehicles (MAVs) as well as larger unmanned air vehicles (UAVs) or even submarines and autonomous underwater vehicles (AUVs). But the OCTAVE autopilot could also provide guidance and/or warning signals to prevent the pilots of manned aircraft from colliding with shallow terrain, for example.     

Inertially Aided Visual Odometry for Miniature Air Vehicles in GPS-denied Environments

Unmanned miniature air vehicles (MAVs) have recently become a focus of much research, due to their potential utility in a number of information gathering applications. MAVs currently carry inertial sensor packages that allow them to perform basic flight maneuvers reliably in a completely autonomous manner. However, MAV navigation requires knowledge of location that is currently available only through GPS sensors, which depend on an external infrastructure and are thus prone to reliability issues. Vision-based methods such as Visual Odometry (VO) have been developed that are capable of estimating MAV pose purely from vision, and thus have the potential to provide an autonomous alternative to GPS for MAV navigation. Because VO estimates pose by combining relative pose estimates, constraining relative pose error is the key element of any Visual Odometry system. In this paper, we present a system that fuses measurements from an MAV inertial navigation system (INS) with a novel VO framework based on direct image registration. We use the inertial sensors in the measurement step of the Extended Kalman Filter to determine the direction of gravity, and hence provide error-bounded measurements of certain portions of the aircraft pose. Because of the relative nature of VO measurements, we use VO in the EKF prediction step. To allow VO to be used as a prediction, we develop a novel linear approximation to the direct image registration procedure that allows us to propagate the covariance matrix at each time step. We present offline results obtained from our pose estimation system using actual MAV flight data. We show that fusion of VO and INS measurements greatly improves the accuracy of pose estimation and reduces the drift compared to unaided VO during medium-length (tens of seconds) periods of GPS dropout.     

Vision-Based Odometry and SLAM for Medium and High Altitude Flying UAVs

This paper proposes vision-based techniques for localizing an unmanned aerial vehicle (UAV) by means of an on-board camera. Only natural landmarks provided by a feature tracking algorithm will be considered, without the help of visual beacons or landmarks with known positions. First, it is described a monocular visual odometer which could be used as a backup system when the accuracy of GPS is reduced to critical levels. Homography-based techniques are used to compute the UAV relative translation and rotation by means of the images gathered by an onboard camera. The analysis of the problem takes into account the stochastic nature of the estimation and practical implementation issues. The visual odometer is then integrated into a simultaneous localization and mapping (SLAM) scheme in order to reduce the impact of cumulative errors in odometry-based position estimation approaches. Novel prediction and landmark initialization for SLAM in UAVs are presented. The paper is supported by an extensive experimental work where the proposed algorithms have been tested and validated using real UAVs.     

A vision-based landing system for small unmanned aerial vehicles using an airbag

Statistics show that the landing accounts for the largest portion of all mishaps of unmanned aerial vehicles (UAVs) due to many difficulties including limited situational awareness of the external pilot and the limited maneuverability during the low speed flight before touchdown. In this paper, a vision-based automatic landing system using a dome-shaped airbag is proposed for small UAVs. Its isotropic shape allows airplanes to approach in any direction to avoid crosswind unlike net-assisted landing. The dome’s distinctive color improves the detection owing to its strong visual cue. Color- and shape-based detection vision algorithms are applied for robust detection under varying lighting conditions. Due to the insufficient accuracy of navigation sensors, a direct visual servoing is used for terminal guidance. The proposed algorithm is validated in a series of flight tests.     

基于AFKF的多旋翼无人机组合导航技术研究

目前旋翼无人机组合导航系统大都使用扩展卡尔曼滤波算法,然而由于导航系统建模误差和传感器测量精度的影响,导航信息解算误差较大。为了改善旋翼无人机的飞行控制效果,应用自适应渐消卡尔曼滤波（Adaptive fading Kalman filter,AFKF）进行旋翼无人机组合导航解算,算法通过实时计算遗忘因子,对过去的数据权重进行削减,以提高扩展卡尔曼滤波算法的自适应能力。应用旋翼无人机真实飞行数据进行仿真,仿真结果表明,自适应渐消卡尔曼滤波算法能够有效抑制建模误差,弥补传感器测量精度不足,改善旋翼无人机组合导航解算结果。     

Autonomous feature following for visual surveillance using a small unmanned aerial vehicle with gimbaled camera system

This paper represents the development of feature following control and distributed navigation algorithms for visual surveillance using a small unmanned aerial vehicle equipped with a low-cost imaging sensor unit. An efficient map-based feature generation and following control algorithm is developed to make an onboard imaging sensor to track a target. An efficient navigation system is also designed for real-time position and velocity estimates of the unmanned aircraft, which is used as inputs for the path following controller. The performance of the proposed autonomous path following capability with a stabilized gimbaled camera onboard a small unmanned aerial robot is demonstrated through flight tests with application to target tracking for real-time visual surveillance.     

Modeling and Global Control of the Longitudinal Dynamics of a Coaxial Convertible Mini-UAV in Hover Mode

The aim of this paper is to present a configuration for a Convertible Unmanned Aerial Vehicle, which incorporates the advantages of the coaxial rotorcraft for hover flight and the efficiencies of a fixed-wing for forward flight. A detailed dynamical model, including the aerodynamics, is obtained via the Newton-Euler formulation. It is proposed a nonlinear control law that achieves global stability for the longitudinal vertical-mode motion. Indeed, we have performed a simulation study to test the proposed controller in presence of external perturbations, obtaining satisfactory results. We have developed an embedded autopilot to validate the proposed prototype and the control law in hover-mode flight.     

Vision-based autonomous tracking and landing of a fully-actuated rotorcraft

Autonomous landing is a challenging phase of flight for an aerial vehicle, especially when attempting to land on a moving target. This paper presents vision-based tracking and landing of a fully-actuated tilt-augmented quadrotor on a moving target. A fully-actuated vehicle allows higher freedom in terms of control design and a larger flight envelope since the position and attitude states are decoupled. An adaptive control law is designed to track a moving target with only relative position information from a camera. Low-cost hardware is used, and experiments are carried out to validate the proposed methodology for targets moving at realistic speeds.     

Fuzzy Logic Based Approach to Design of Autonomous Landing System for Unmanned Aerial Vehicles

This paper is concerned with autonomous flight of UAVs and proposes a fuzzy logic based autonomous flight and landing system controller. Besides three fuzzy logic controllers which are developed for autonomous navigation for UAVs in a previous work as fuzzy logic based autonomous mission control blocks, three more fuzzy logic modules are developed under the main landing system for the control of the horizontal and the vertical positions of the aircraft against the runway under a TACAN (Tactical Air Navigation) approach. The performance of the fuzzy logic based controllers is evaluated using the standard configuration of MATLAB and the Aerosim Aeronautical Simulation Block Set which provides a complete set of tools for rapid development of 6 degree-of-freedom nonlinear generic manned/unmanned aerial vehicle models. Additionally, FlightGear Flight Simulator and GMS aircraft instruments are deployed in order to get visual outputs that aid the designer in evaluating the performance and the potential of the controllers. The simulated test flights on an Aerosonde indicate the capability of the approach in achieving the desired performance despite the simple design procedure.     

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.     

小型飞艇自驾仪硬件在回路仿真平台设计

设计小型飞艇自驾仪的硬件在回路仿真平台,包括建立基于嵌入式系统ARM9的飞控系统验证机,采用分层结构方式的导航与控制模块。同时构建小型飞艇的动力学、压控系统和传感器仿真模型,充分发挥硬件在回路仿真测试系统软硬件结合的特点,缩短研发周期,提高系统可靠性。仿真结果表明了该平台的有效性。     

基于误差状态卡尔曼滤波估计的旋翼无人机输入饱和控制

针对GPS（global positioning system）信号缺失环境下无人机自主飞行控制问题,设计了一种基于视觉与IMU（inertial measurement unit）融合的误差状态卡尔曼滤波（ESKF）框架,并在此基础上提出了一种新的输入饱和控制方法以进一步缓解视野约束以及运动模糊问题．不同于传统的扩展卡尔曼滤波（EKF）框架,本文设计的滤波框架是对误差状态进行更新与校正,而不是直接对系统状态进行估计．由于误差状态是小量,并且其线性程度较高,因此相对于系统状态局部线性化而言,误差状态的局部线性化的模型误差更小,进而可以提高状态估计的精度．基于ESKF框架得到的全状态估计,本文提出了一种新的线性与双曲正切混合的饱和函数,进而设计了输入饱和控制器并通过李亚普诺夫函数证明了闭环系统平衡点的渐近稳定性．最后,在旋翼无人机平台上的对比实验结果表明：本文ESKF方法得到的状态估计精度更高．另外,本文所提出的输入饱和控制方法有助于保证视觉特征在视野之内,并且比有界积分控制方法有更好的暂态以及稳态性能．     

一种空间曲线轨迹跟踪的无人机自适应导航控制算法

随着固定翼无人机飞行任务复杂化,为了实现高精度的空间曲线导航控制,基于L1-Navigation非线性导航控制算法,设计自适应模糊控制器优化固定翼无人机跟踪空间曲线导航控制方法。以球面上的空间八字曲线为例,对八字曲线建模,通过坐标转换求得目标航点位置来计算无人机飞行加速度。为了优化加速度控制无人机跟踪空间曲线性能,在L1-Navigation导航控制器中,针对增益系数设计一个双输入单输出模糊控制系统,以轨迹误差和轨迹误差变化率为输入量,以计算横向加速度的增益系数常数为输出量。最后,在Ardupilot飞控中进行飞行模拟实验,飞行实验表明,所提出方法能够精确跟踪空间曲线路径,并且有很好的自适应性。