基于EKF算法的太阳能无人机低成本飞控状态估计

当大展弦比太阳能无人机(UAV)采用由低成本传感器组成的飞控平台时,受限于传感器误差精度、无人机长航时、广域度的任务要求,传统数据融合算法无法实现其姿态、空速和风场长时间的准确和可靠估计.从飞控搭载的传感器测量原理出发,对测量过程的误差特性和温度影响进行建模,基于扩展卡尔曼滤波算法实现状态的可靠估计.首先,将压力传感器与惯导的数据进行融合以实现姿态估计;其次,结合无人机的布局特征将磁力计独立安装以实现航向估计;最后,融合GPS的数据进行导航估计.仿真结果表明,较传统的变增益估计算法(VGO),所提出算法的层次更分明,结果更可靠,而且可以与太阳能无人机的特征较好地结合.     

一套完整的基于视觉光流和激光扫描测距的室内无人机导航系统

提出了一套室内四旋翼无人机控制, 导航, 定位和地图构建的完整解决方案. 无人机机载系统包括三个主要传感器, 即惯性测量单元, 下视相机和激光扫描测距仪. 经过处理, 融合这些传感器的测量数据, 无人机能够可靠的估计自己的飞行速度和实时位置, 并且沿着室内的墙壁进行无碰撞飞行. 通过收集一个完整飞行实验的数据, 无人机的飞行路径和在室内的环境也可以被很好地估计出来. 这套系统中的自主导功能不需要任何远程传感信息或脱机计算能力. 这套室内导航方案的性能和可靠性已在实际的飞行实验中被验证.     

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.     

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.     

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。     

Some accuracy and resolution aspects of computer vision distance measurements

The distance between an object and stereo vision sensors can be measured using image processing and known system parameters. A detailed distance measurement synthesis procedure to meet system specifications is presented and illustrated with an example. An error analysis shows that error is proportional to distance. System parameters such as separation between sensor elements, sensor focal length, and sensor array dimensions are related in the design and error equations presented. The main desired design goal is to establish the smallest image sensor array size which will meet system operating specifications. Minimum and maximum distance, object height, optic parameters, scene shift, and sensor array parameters are related.     

Real-Time Omnidirectional Image Sensors

Conventional T.V. cameras are limited in their field of view. A real-time omnidirectional camera which can acquire an omnidirectional (360 degrees) field of view at video rate and which could be applied in a variety of fields, such as autonomous navigation, telepresence, virtual reality and remote monitoring, is presented. We have developed three different types of omnidirectional image sensors, and two different types of multiple-image sensing systems which consist of an omnidirectional image sensor and binocular vision. In this paper, we describe the outlines and fundamental optics of our developed sensors and show examples of applications for robot navigation.     

Design and Implementation of a Multi Sensor Based Brain Computer Interface for a Robotic Wheelchair

In this study, design and implementation of a multi sensor based brain computer interface for disabled and/or elderly people is proposed. Developed system consists of a wheelchair, a high-power motor controller card, a Kinect camera, electromyogram (EMG) and electroencephalogram (EEG) sensors and a computer. The Kinect sensor is installed on the system to provide safe navigation for the system. Depth frames, captured by the Kinect’s infra-red (IR) camera, are processed with a custom image processing algorithm in order to detect obstacles around the wheelchair. A Consumer grade EMG device (Thalmic Labs) was used to obtain eight channels of EMG data. Four different hand movements: Fist, release, waving hand left and right are used for EMG based control of the robotic wheelchair. EMG data is first classified using artificial neural network (ANN), support vector machines and random forest schemes. The class is then decided by a rule-based scheme constructed on the individual outputs of the three classifiers. EEG based control is adopted as an alternative controller for the developed robotic wheelchair. A wireless 14-channels EEG sensor (Emotiv Epoch) is used to acquire real time EEG data. Three different cognitive tasks: Relaxing, math problem solving, text reading are defined for the EEG based control of the system. Subjects were asked to accomplish the relative cognitive task in order to control the wheelchair. During experiments, all subjects were able to control the robotic wheelchair by hand movements and track a pre-determined route with a reasonable accuracy. The results for the EEG based control of the robotic wheelchair are promising though vary depending on user experience.     

Vision Based Neuro-Fuzzy Controller for a Two Axes Gimbal System with Small UAV

This paper presents the development of a vision-based neuro-fuzzy controller for a two axes gimbal system mounted on a small Unmanned Aerial Vehicle (UAV). The controller uses vision-based object detection as input and generates pan and tilt motion and velocity commands for the gimbal in order to keep the interest object at the center of the image frame. A readial basis function based neuro-fuzzy system and a learning algorithm is developed for the controller to address the dynamic and non-linear characteristics of the gimbal movement. The controller uses two separate, but identical radial basis function networks, one for pan and one for tilt motion of the gimbal. Each system is initialized with a fixed number of neurons that act as rules basis for the fuzzy inference system. The membership functions and rule strengths are then adjusted with the feedback from the visual tracking system. The controller is trained off-line until a desired error level is achieved. Training is then continued on-line to allow the system to accommodate air speed changes. The algorithm learns from the error computed from the detected position of the object in image frame and generates position and velocity commands for the gimbal movement. Several tests including lab tests and actual flight tests of the UAV have been carried out to demonstrate the effectiveness of the controller. Test results show that the controller is able to converge effectively and generate accurate position and velocity commands to keep the object at the center of the image frame.     

ECW技术在无人机图像管理中的应用

针对有限内存显示海量大尺度图像的难题,基于小波变换的方法比基于图像块亚采样的方法表现出更加优越的性能,得到了国内外有关机构的深入研究。介绍了ECW的技术特点和开发途径,总结了大尺度图像漫游软件开发的一般步骤,利用ECW SDK开发了一个带有导航功能的大尺度图像漫游软件。该软件实现了T字节甚至更大图像的快速显示、漫游、放大、缩小、导航和区域数据提取等功能,其操作速度和内存资源消耗基本与图像尺寸大小无关,目前已成功用于无人机图像信息系统之中。     

Omnidirectional vision applied to Unmanned Aerial Vehicles (UAVs) attitude and heading estimation

This paper presents an aircraft attitude and heading estimator using catadioptric images as a principal sensor for UAV or as a redundant system for IMU (Inertial Measure Unit) and gyro sensors. First, we explain how the unified theory for central catadioptric cameras is used for attitude and heading estimation, explaining how the skyline is projected on the catadioptric image and how it is segmented and used to calculate the UAV’s attitude. Then, we use appearance images to obtain a visual compass, and we calculate the relative rotation and heading of the aerial vehicle. Finally the tests and results using the UAV COLIBRI platform and the validation of them in real flights are presented, comparing the estimated data with the inertial values measured on board.     

Nondestructive evaluation sensor fusion with autonomous robotic system for civil infrastructure inspection

Civil infrastructure inspection is crucial to maintaining the quality of that infrastructure, which has a great impact on the economy. Performing this inspection is costly work that requires workers to be trained on how to use varying technologies, which can be error prone when performed manually and can result in damage to the infrastructure in some cases. For this reason, nondestructive evaluation (NDE) sensors are preferred for civil infrastructure inspection as they can perform the necessary inspection without damaging the infrastructure. In this paper, we develop a fully autonomous robotic system capable of real‐time data collection and quasi‐real‐time data processing. The robotic system is equipped with several NDE sensors that allow for a sensor fusion method to be developed that successfully minimizes inspection time while performing adequate inspection of areas that require more in‐depth data to be collected. A detailed discussion of the inspection framework developed for this robotic system, and the dual navigation modes for both indoor and outdoor autonomous navigation is presented. The developed robotic system is deployed to inspect several infrastructures (e.g., parking garages, bridges) at and near by the University of Nevada, Reno campus.     

Retina-like visual sensor for fast tracking and navigation robots

This paper describes the development of an anthropomorphic visual sensor which generates a spatially variant resolution image by using a retina-like structure. This sensor consists of a dove prism for image rotation and two linear CCD sensors with 512 pixel/line resolution and holds approximately 45 kbytes of image data. The retina-like sensor has variable resolution with increasing density towards the center of the visual field and yields a polar-coordinate image directly. The motion analysis of the object in the scene from the optical flow is considerably simplified if the velocity is represented in polar coordinates, compared to the case when the image is represented in cartesian coordinates. A calibration procedure for the proposed retina-like sensor is also presented with experimental data to verify the validity of the system. Development of this sensor holds promise in applications to high-speed tracking systems, such as the eyes of navigation robots, because it has data reduction and polar mapping characteristics.     

磁航向传感器在无人机飞行控制系统中的应用

磁航向传感器V2XG利用"磁感法"测量地磁航向,有成本低、易集成等特点。将V2XG应用于无人飞机的航向控制回路,设计了传感器的接口电路,给出了零位误差和灵敏度误差的消除方法。实测结果表明,航向测量准确度达到±2°,采样频率达到5Hz,设计方案有较高性价比,满足了无人机飞行控制对航向测量的要求。     

An integrated multisensor aircraft track recovery system for remote sensing

This paper describes an application of the Kalman filter in a track recovery system (TRS) for postflight processing of aircraft navigation sensor data. The track recovery system has been successfully used as a key component of the Canadian aerial hydrography pilot project for mapping of shallow coastal waters. Recorded data from an inertial navigation system (INS) is combined with data obtained from a number of auxiliary sensors to construct a set of error measurements. The measurements are prefiltered to compress the data and are then processed using aU-Dfactorized Kalman filter and a modified Bryson-Frazier smoother to produce estimates of the time-correlated sensor errors. The flight profile is obtained by subtracting the computed error estimates from the recorded INS data. The residual errors observed in processing real data collected in a number of field tests are less than 1 m in position and less than 0.03 degrees in attitude.     

电磁诱导农用喷雾机器人路径导航系统的设计与实现

针对温室内农药喷洒作业自动化的需求，设计了一种电磁诱导式农用喷雾机器人路径导航系统．采用数字波形合成技术设计实现了基于ARM7单片机的信号发生器系统．研制了机器人位置检测传感器和以霍尔芯片为核心的磁标志检测传感器．利用信号发生器和传感器成功实现了喷雾机器人的导航控制．     

A new space and time sensor fusion method for mobile robot navigation

To fully utilize the information from the sensors of mobile robot, this paper proposes a new sensor‐fusion technique where the sample data set obtained at a previous instant is properly transformed and fused with the current data sets to produce a reliable estimate for navigation control. Exploration of an unknown environment is an important task for the new generation of mobile service robots. The mobile robots may navigate by means of a number of monitoring systems such as the sonar‐sensing system or the visual‐sensing system. Notice that in the conventional fusion schemes, the measurement is dependent on the current data sets only. Therefore, more sensors are required to measure a given physical parameter or to improve the reliability of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequences of the data sets are stored and utilized for the purpose. The basic principle is illustrated by examples and the effectiveness is proved through simulations and experiments. The newly proposed STSF (space and time sensor fusion) scheme is applied to the navigation of a mobile robot in an environment using landmarks, and the experimental results demonstrate the effective performance of the system. © 2004 Wiley Periodicals, Inc.     

Fuzzy image processing scheme for autonomous navigation of human blind

The main objective of this work is to develop an electronic travel aid to assist the blinds for obstacle identification in their navigation. This navigation assistance for visually impaired (NAVI) system presented in this paper consists of a single board processing system (SBPS), a vision sensor mounted headgear and a pair of stereo earphones. The image environment in front of the blind is captured by the vision sensor. The image is processed by a new real time image processing scheme using fuzzy clustering algorithms. The processed image is mapped onto a specially structured stereo acoustic patterns and transferred to the stereo earphones in the system. Blind individuals were trained with NAVI system and tested for obstacle identification. Suggestions from the blind volunteers regarding pleasantness and discrimination of sound pattern were also incorporated in the prototype. The proposed processing methodology is found to be effective for object identification and for producing stereo sound patterns in the NAVI system.     

一种小型无人机高度测量方法的研究与实现

为了满足某小型无人机飞行控制中对高度信号的采集与处理,提出了用无线电高度来确定基准高度,用气压高度来确定相对高度,用起飞点的实测温度进行补偿的设计思想,综合这3种手段来有效消除外界环境对气压高度测量精度的影响。传感器数据融合方面,利用飞行器运动学推算出的高度状态方程以及传感器观测模型,采用Kalm an滤波技术来估计和修正传感器的测量高度。测试结果表明,此方法得到了较准确的高度测量信息,可以满足无人机飞行控制的需求。     

Development of Unmanned Aerial Vehicle (UAV) system with waypoint tracking and vision-based reconnaissance

This paper introduces the developed UAV system for low cost operation and an EOS (Electro Optical System) laboratory. This paper highlights an autonomous navigation system based on microcontrollers that can track a target using images, take three-dimensional measurements of the target, and acquire high quality images. The hardware system and an algorithm for the EOS verify the performance of the image tracking system and 3-D measurement of the target’s position. 3-D position estimations for the target are solved using the mathematical relationship between the UAV and target. Although an on-board EOS can make errors in 3-D measurement, the proposed approach shows improved accuracy and confidence for 3-D target tracking using a postprocessing method.