Nonlinear aircraft sensor fault reconstruction in the presence of disturbances validated by real flight data

This paper proposes an approach for Inertial Measurement Unit sensor fault reconstruction by exploiting a ground speed-based kinematic model of the aircraft flying in a rotating earth reference system. Two strategies for the validation of sensor fault reconstruction are presented: closed-loop validation and open-loop validation. Both strategies use the same kinematic model and a newly-developed Adaptive Two-Stage Extended Kalman Filter to estimate the states and faults of the aircraft. Simulation results demonstrate the effectiveness of the proposed approach compared to an approach using an airspeed-based kinematic model. Furthermore, the major contribution is that the proposed approach is validated using real flight test data including the presence of external disturbances such as turbulence. Three flight scenarios are selected to test the performance of the proposed approach. It is shown that the proposed approach is robust to model uncertainties, unmodeled dynamics and disturbances such as time-varying wind and turbulence. Therefore, the proposed approach can be incorporated into aircraft Fault Detection and Isolation systems to enhance the performance of the aircraft.     

基于无迹卡尔曼滤波传感器信息融合的车辆导航算法

针对复杂道路条件下车辆的导航问题,将全球定位系统(GPS)与车载终端传感器系统相结合,提出了基于多传感器系统的车辆精确定位模型,并针对扩展类卡尔曼滤波易产生突发性误差而导致的安全问题,采用基于Sigma点的无迹卡尔曼滤波器(UKF)传感器信息融合算法。根据实时的道路状况和车辆自身的运动状态给出符合要求的状态估值,实验与基于多项式扩展卡尔曼滤波车辆传感器信息融合算法在精度和效率方面进行了比较,结果表明,基于UKF传感器信息融合的算法在复杂路况下的估计精度和运行效率都有显著提高,能够根据当前的路线情况和车载传感器的反馈信息快速地估计出车辆的运动状态,实时计算出动态的车辆控制输入。     

Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter

In many environmental monitoring applications, since the data periodically sensed by wireless sensor networks usually are of high temporal redundancy, prediction-based data aggregation is an important approach for reducing redundant data communications and saving sensor nodes’ energy. In this paper, a novel prediction-based data collection protocol is proposed, in which a double-queue mechanism is designed to synchronize the prediction data series of the sensor node and the sink node, and therefore, the cumulative error of continuous predictions is reduced. Based on this protocol, three prediction-based data aggregation approaches are proposed: Grey-Model-based Data Aggregation (GMDA), Kalman-Filter-based Data Aggregation (KFDA) and Combined Grey model and Kalman Filter Data Aggregation (CoGKDA). By integrating the merit of grey model in quick modeling with the advantage of Kalman Filter in processing data series noise, CoGKDA presents high prediction accuracy, low communication overhead, and relative low computational complexity. Experiments are carried out based on a real data set of a temperature and humidity monitoring application in a granary. The results show that the proposed approaches significantly reduce communication redundancy and evidently improve the lifetime of wireless sensor networks.     

基于移动节点的无线传感器网络定位算法

节点定位技术是无线传感器网络应用的重要支撑技术之一,为了提高定位算法的准确性,提出了一种基于移动目标节点的两步定位算法。该算法利用一个移动目标节点遍历整个网络,并周期性地广播包含自身当前位置的信息。而传感器节点的自身定位过程则可用基于无迹卡尔曼滤波(UKF)的目标跟踪方法实现。由于所用的目标状态模型和量测模型有一定的不确定性,所以先选取不共线的3个拥有RSSI测距能力的目标节点信息,利用Euclidean定位法提高滤波的初始位置精度,从而改善定位效果。通过仿真、分析和比较该目标节点在多种移动轨迹情况下的定位误差,这种两步定位法可以改善对目标节点移动轨迹的特殊要求的限制,能取得较好的定位精度,而且更适合于实际情况。     

A Localization Method Based on Map-Matching and Particle Swarm Optimization

This paper presents a novel localization method for small mobile robots. The proposed technique is especially designed for the Robot@Factory, a new robotic competition which is started in Lisbon in 2011. The real-time localization technique resorts to low-cost infra-red sensors, a map-matching method and an Extended Kalman Filter (EKF) to create a pose tracking system that performs well. The sensor information is continuously updated in time and space according to the expected motion of the robot. Then, the information is incorporated into the map-matching optimization in order to increase the amount of sensor information that is available at each moment. In addition, the Particle Swarm Optimization (PSO) relocates the robot when the map-matching error is high, meaning that the map-matching is unreliable and the robot gets lost. The experiments presented in this paper prove the ability and accuracy of the presented technique to locate small mobile robots for this competition. Extensive results show that the proposed method presents an interesting localization capability for robots equipped with a limited amount of sensors, but also less reliable sensors.     

The SLAM Algorithm for Multiple Robots Based on Parameter Estimation

With the increasing number of feature points of a map, the dimension of systematic observation is added gradually, which leads to the deviation of the volume points from the desired trajectory and significant errors on the state estimation. An Iterative Squared-Root Cubature Kalman Filter (ISR-CKF) algorithm proposed is aimed at improving the SR-CKF algorithm on the simultaneous localization and mapping (SLAM). By introducing the method of iterative updating, the sample points are re-determined by the estimated value and the square root factor, which keeps the distortion small in the highly nonlinear environment and improves the precision further. A robust tracking Square Root Cubature Kalman Filter algorithm (STF-SRCKF-SLAM) is proposed to solve the problem of reduced accuracy in the condition of state change on the SLAM. The algorithm is predicted according to the kinematic model and observation model of the mobile robot at first, and then the algorithm updates itself by spreading the square root of the error covariance matrix directly, which greatly reduces the computational complexity. At the same time, the time-varying fading factor is introduced in the process of forecasting and updating, and the corresponding weight of the data is adjusted in real time to improve the accuracy of multi-robot localization. The results of simulation shows that the algorithm can improve the accuracy of multi-robot pose effectively.     

基于自适应卡尔曼滤波的运动车辆检测

卡尔曼滤波是一种根据时变随机信号的统计特性,对信号的未来值做出尽可能接近真值的一种估计方法,首先介绍了卡尔曼滤波原理,然后阐述了它在运动目标检测的应用。针对传统的固定值的卡尔曼滤波方法的缺陷,提出了自适应更新参数的卡尔曼滤波方法。通过与传统的卡尔曼滤波方法、帧差法、光流法和高斯混合模型方法的比较,证明了该方法的有效性。     

Collaborative Localization for Multi-Robot System with Fault Detection and Exclusion Based on the Kullback-Leibler Divergence

Multi-robot system attracted attention in various applications in order to replace the human operators. To achieve the intended goal, one of the main challenges of this system is to ensure the integrity of localization by adding a sensor fault diagnosis step to the localization task. In this paper, we present a framework able, in addition of localizing a group of robots, to detect and exclude the faulty sensors from the group with an optimized thresholding method. The estimator has the informational form of the Kalman Filter (KF) namely Information Filter (IF). A residual test based on the Kullback-Leibler divergence (KLD) between the predicted and the corrected distributions of the IF is developed. It is generated from two tests: the first acts on the means and the second deals with the covariance matrices. Thresholding using entropy based criterion and Receiver Operating Characteristics (ROC) curve are discussed. Finally, the validation of this framework is studied on real experimental data from a group of robots.     

Mobile robot localization with gyroscope and constrained Kalman filter

The odometry information used in mobile robot localization can contain a significant number of errors when robot experiences slippage. To offset the presence of these errors, the use of a low-cost gyroscope in conjunction with Kalman filtering methods has been considered by many researchers. However, results from conventional Kalman filtering methods that use a gyroscope with odometry can unfeasible because the parameters are estimated regardless of the physical constraints of the robot. In this paper, a novel constrained Kalman filtering method is proposed that estimates the parameters under the physical constraints using a general constrained optimization technique. The state observability is improved by additional state variables and the accuracy is also improved through the use of a nonapproximated Kalman filter design. Experimental results show that the proposed method effectively offsets the localization error while yielding feasible parameter estimation.     

Automatic real-time offset calibration of gyroscopes

This paper reports on real-time offset calibration of a three-axis gyroscope whereby the angular rate sensor is part of an IMU in the field. If the IMU is in motion, the orientation changes of the gyroscope can be compared with those from other calibrated attitude sensors to identify the offset of the gyroscope. However, the acceleration sensor of an IMU in the field is typically uncalibrated and thus prevents an exact offset calibration during motion. In this paper, a new multi-model error state Kalman filter is proposed in order to determine the gyroscope’s offset. This filter exclusively uses an uncalibrated accelerometer for determining the gyroscope's offset. Consequently, the resulting calibration system is only partially observable which in turn influences the observability of the requested offset parameters. However, the latter conditions are the typical situation encountered in an IMU in the field. Therefore, the observability conditions as well as the stability of the multi model error state Kalman filter will be analyzed in the second part of this paper.     

Event based distributed kalman filter for limited resource multirobot cooperative localization

This paper presents a multirobot cooperative event based localization scheme with improved bandwidth usage in a heterogeneous group of mobile robots. The proposed method relies on an agent based framework that defines the communications between robots and on an event based Extended Kalman Filter that performs the cooperative sensor fusion from local, global and relative sources. The event is generated when the pose error covariance exceeds a predefined limit. By this, the robots update the pose using the relative information available only when necessary, using less bandwidth and computational resources when compared to the time based methods, allowing bandwidth allocation for other tasks while extending battery life. The method is tested using a simulation platform developed in the programming language JAVA with a group of differential mobile robots represented by an agent in a JADE framework. The pose estimation performance, error covariance and number of messages exchanged in the communication are measured and used to compare the traditional time based approach with the proposed event based algorithm. Also, the compromise between the accuracy of the localization method and the bandwidth usage is analyzed for different event limits. A final experimental test with two SUMMIT XL robots is shown to validate the simulation results.     

An Adaptive Unscented Kalman Filter-based Controller for Simultaneous Obstacle Avoidance and Tracking of Wheeled Mobile Robots with Unknown Slipping Parameters

A novel unified control approach is proposed to simultaneously solve tracking and obstacle avoidance problems of a wheeled mobile robot (WMR) with unknown wheeled slipping. The longitudinal and lateral slipping are processed as three time-varying parameters and an Adaptive Unscented Kalman Filter (AUKF) is designed to estimate the slipping parameters online More specifically, an adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the Unscented Kalman Filter (UKF) context. A stable unified controller is applied to simultaneously handle tracking and obstacle avoidance for this WMR system to compensate for the unknown slipping effect. Applying Lyapunov stability theory, it is proved that tracking errors of the closed-loop system are asymptotically convergent regardless of unknown slipping, the tracking errors converge to the zero outside the obstacle detection region and obstacle avoidance is guaranteed inside the obstacle detection region. The effectiveness and robustness of the proposed control method are validated through simulation and experimental results.     

Humanoid odometric localization integrating kinematic,inertial and visual information

We present a method for odometric localization of humanoid robots using standard sensing equipment, i.e., a monocular camera, an inertial measurement unit (IMU), joint encoders and foot pressure sensors. Data from all these sources are integrated using the prediction-correction paradigm of the Extended Kalman Filter. Position and orientation of the torso, defined as the representative body of the robot, are predicted through kinematic computations based on joint encoder readings; an asynchronous mechanism triggered by the pressure sensors is used to update the placement of the support foot. The correction step of the filter uses as measurements the torso orientation, provided by the IMU, and the head pose, reconstructed by a VSLAM algorithm. The proposed method is validated on the humanoid NAO through two sets of experiments: open-loop motions aimed at assessing the accuracy of localization with respect to a ground truth, and closed-loop motions where the humanoid pose estimates are used in real-time as feedback signals for trajectory control.     

Incorporation of Feature Tracking into Simultaneous Localization and Map Building via Sonar Data

Simultaneous Localization and Map building (SLAM) is referred to as the ability of an Autonomous Mobile Robot (AMR) to incrementally extract the surrounding features for estimating its pose in an unknown location and unknown environment. In this paper, we propose a new technique for extraction of significant map features from standard Polaroid sonar sensors to address the SLAM problem. The proposed algorithm explicitly initializes and tracks the line (or wall) features from a comparison between two overlapping sensor measurements buffers. The experimental studies on a Pioneer 2DX mobile robot equipped with sonar sensors suggest that SLAM problem can be solved by the proposed algorithm. The estimated trajectory of AMR from the standard model based on Extended Kalman Filter (EKF) localization for the same experiment is also provided for comparison.     

基于权重参数实时更新的室内定位算法

为了提高室内节点的定位精度,提出一种基于权值参数实时更新的室内定位算法。选择3个最能反映待定位点信息的访问接入点,实时获取测距模型的参数,并采用最小二乘支持向量机对测距进行补偿,得到距离权重,三边定位算法根据权重对节点进行定位,并对计算中的距离进行加权处理,采用卡尔曼滤波法对定位误差的进行校正。实验结果表明,该算法可以较好地降低环境变化和测量误差对定位的不利影响,提高了室内节点的定位精度。     

Model-based 3D object detection

Fast detection of objects in a home or office environment is relevant for robotic service and assistance applications. In this work we present the automatic localization of a wide variety of differently shaped objects scanned with a laser range sensor from one view in a cluttered setting. The daily-life objects are modeled using approximated Superquadrics, which can be obtained from showing the object or another modeling process. Detection is based on a hierarchical RANSAC search to obtain fast detection results and the voting of sorted quality-of-fit criteria. The probabilistic search starts from low resolution and refines hypotheses at increasingly higher resolution levels. Criteria for object shape and the relationship of object parts together with a ranking procedure and a ranked voting process result in a combined ranking of hypothesis using a minimum number of parameters. The experimental evaluation of the method and experiments from cluttered table top scenes demonstrate the effectiveness and robustness of the approach, feasible for real world object localization and robot grasp planning.     

Robot Localization Using the Phase of Passive UHF-RFID Signals Under Uncertain Tag Coordinates

An indoor global localization problem for a mobile robot based on Radio Frequency IDentification (RFID) is considered. The localization system consists of a reader installed on the robot which measures the phase of UHF-RFID signals coming from a set of passive tags deployed on the ceiling of the environment. Assuming only an approximate information is available at the beginning on the position of the tags, this paper presents an algorithm, based on a Multi-Hypothesis Extended Kalman Filter, which improves the initial estimate on the tag coordinates while simultaneously localizing the robot. Simulative and experimental results are reported to illustrate the effectiveness of the proposed approach.     

COM motion estimation of a biped robot based on kinodynamics and torque equilibrium

A novel technique to estimate motion of the center of mass (COM) for a biped robot is proposed. A Kalman filter is synthesized where the time evolution of COM is predicted from the external force and corrected based on kinematic estimation and torque equilibrium. They complementarily work to compensate the initial estimation offset, the error accumulation, and errors in modeled mass properties. It makes use of the authors’ previous method to estimate the translational and rotational motion of the base body from inertial information and joint angle measurements. The information about torque equilibrium helps to reduce an uncertainty of the height of COM and to improve the estimation accuracy of it by utilizing an interference of the horizontal and vertical motion of COM. The parameters are tuned based on error analyses in mass properties and sensor signals. A comparative study showed a better performance of the proposed method than other methods through dynamics simulations.     

基于无极卡尔曼滤波算法的雅可比矩阵估计

在基于图像的机器人视觉伺服中,采用在线估计图像雅可比的方法,不需事先知道系统的精确模型,可以避免复杂的系统标定过程。为了有效改善图像雅可比矩阵的在线估计精度,进而提高机器人的跟踪精度,针对机器人跟踪运动目标的应用背景,提出了利用无极卡尔曼滤波算法在线估计总雅可比矩阵。在二自由度的机器人视觉伺服仿真平台上,分别用卡尔曼滤波器（KF）、粒子滤波器（PF）和无极卡尔曼滤波器（UKF）三种算法进行总雅可比矩阵的在线估计。实验结果证明,使用UKF算法的跟踪精度优于其他两种算法,时间耗费仅次于KF算法。     

基于自适应固定滞后卡尔曼平滑器的状态观测器在假手上的应用

针对以电位计为角度传感器的假手系统,提出了一种基于自适应固定滞后卡尔曼平滑器的状态观测器以观测手指的当前位置、速度和加速度信息.首先,分析了卡尔曼滤波器滤除电位计热噪声并观测速度与加速度的合理性,进而建立了其系统的离散状态转移矩阵.其次,相比卡尔曼滤波器,卡尔曼平滑器在参数相同的情况下具有更好的平滑效果,据此提出一种基于固定滞后卡尔曼平滑器的状态观测器,并通过引入渐消因子以提高动态响应特性.同时给出了一种将本文算法滞后特性降至一个控制周期的有效实现方式.最后,在HIT-V仿人假手实验平台上进行了实验验证.实验结果表明,相比对原始数据直接进行差分,该方法将速度噪声降低了20倍以上,加速度噪声降低了10 000倍以上.相比标准卡尔曼滤波器和固定滞后卡尔曼平滑器,该方法在动态响应方面具有更好的效果.