Calibration and on-line data selection of multiple optical flow sensors for odometry applications

This work proposes a calibration method and a computational algorithm to integrate the data of multiple optical flow sensors for two-dimensional trajectory measurements. Optical flow sensors offer a different kind of odometer as compared to the wheel encoder. Using multiple sensors can reduce the effect of measurement uncertainties. Since all sensors are mounted on a rigid body, their measurement data must obey a certain relation, which is utilized in this work. Additionally, mathematical formulae are developed to realize the computation. Analytical results show that the calibration procedure can be cast as an optimization problem given measurement data. Furthermore, the rigid-body relation is formulated as a null-space constraint using the calibrated parameters. Unreliable sensor measurements can be removed during operation by accessing the error distance to the null space. Experimental results are presented to support the proposed methods.     

Complementing visual tracking of moving targets by fusion of tactile sensing

Robot control in uncertain and dynamic environments can be greatly improved using sensor-based control. Vision is a versatile low-cost sensory modality, but low sample rate, high sensor delay and uncertain measurements limit its usability, especially in strongly dynamic environments. Vision can be used to estimate a 6-DOF pose of an object by model-based pose-estimation methods, but the estimate is typically not accurate along all degrees of freedom. Force is a complementary sensory modality allowing accurate measurements of local object shape when a tooltip is in contact with the object. In multimodal sensor fusion, several sensors measuring different modalities are combined together to give a more accurate estimate of the environment. As force and vision are fundamentally different sensory modalities not sharing a common representation, combining the information from these sensors is not straightforward. We show that the fusion of tactile and visual measurements enables to estimate the pose of a moving target at high rate and accuracy. Making assumptions of the object shape and carefully modeling the uncertainties of the sensors, the measurements can be fused together in an extended Kalman filter. Experimental results show greatly improved pose estimates with the proposed sensor fusion.     

Using mobile beacons to locate sensors in obstructed environments

Locating sensors in an indoor environment is a challenging problem due to the insufficient distance measurements caused by short ultrasound range and the incorrect distance measurements caused by multipath effect of ultrasound. In this paper, we propose a virtual ruler approach, in which a vehicle equipped with multiple ultrasound beacons travels around the area to measure distances between pairwise sensors. Virtual Ruler can not only obtain sufficient distances between pairwise sensors, but can also eliminate incorrect distances in the distance measurement phase of sensor localization. We propose to measure the distance between pairwise sensors from multiple perspectives using the virtual ruler and filter incorrect values through a statistical approach. By assigning measured distances with confidence values, the localization algorithm can intelligently localize each sensor based on high confidence distances, which greatly improves localization accuracy. Our performance evaluation shows that the proposed approach can achieve better localization results than previous approaches in an indoor environment.     

传感器网络中数据驱动的睡眠调度机制

在能量受限的传感器网络中,尽量延长网络寿命同时保证服务质量(如感知覆盖和数据完整)是关键的研究问题.节点睡眠调度能有效延长网络寿命.研究数据驱动的睡眠调度机制,利用感知数据的时空相关性识别冗余节点.核心思想是用非参数回归方法为节点建立预测模型,求解最大支配数的节点支配集,调度多个支配集轮流工作.睡眠节点的数据可以由支配集节点恢复.分别给出集中式、半分布式和分布式3个睡眠调度方法.据知,这是第1个将统计回归模型用于睡眠调度并扩展到大规模网络的研究.实验结果表明,该方法能够有效地减少活跃节点个数,节省能耗从而延长网络寿命,同时在用户指定误差范围内保证数据的完整性.     

Constrained and quantized Kalman filtering for an RFID robot localization problem

In this paper a global localization problem of a robot moving in a known environment is considered. The environment is equipped with a relatively sparse set of passive RFID (Radio Frequency IDentification) tags. The robot can detect the presence of the tags when traveling in their proximity and combines this information with the one given by other sensors (e.g. odometry). The RFID measurements are characterized by a highly non Gaussian noise: for this reason in the literature Particle Filter (PF) methods have often been used to fuse these data with the measurements coming from other sensors. In this paper a different approach is pursued, based on the observation that RFID readings can be considered as noisy quantized measurements of the pose of the robot or as noisy dynamic constraints on the pose itself. This allows to exploit the rich literature on Kalman quantized filtering or Kalman constrained estimation, to realize reliable methods with a satisfactory performance which require a computational time significantly lower with respect to the one needed by a PF. Simulative and experimental results will be reported to illustrate the proposed methods.     

Sensor validation and process fault diagnosis for FCC units under MPC feedback

Model predictive control (MPC) has been widely applied in industry, especially in the refining industry. As all feedback controllers require correct sensor measurements, unreliable sensors can cause the MPC controller to move the process in an erroneous manner. Data validation of sensor measurements is a prerequisite in applying advanced control, particularly multivariable control which depends on many sensors. However, little research work is available on how feedback controllers like MPC complicate the task of sensor validation and process fault diagnosis. In theory, a controller can transfer the effect of a sensor fault in a controlled variable to the manipulated variables. In this paper, principal component analysis (PCA) is applied to detect, identify and reconstruct faulty sensors in a simulated FCC unit. A base PCA model is generated by perturbing the process throughout the operating region. Performance of MPC with and without data validation is compared. The same base PCA model is applied to detect and identify dynamic process faults. We demonstrate that process faults can be detected and diagnosed at an early stage.     

Sensor fault detection and isolation for aircraft control systems by kinematic relations

This paper presents a new approach to Fault Detection and Isolation (FDI) for sensors of aircraft. In the most general case, fault detection of these sensors on modern aircraft is performed by a logic that selects one of, or combines, the three redundant measurements. Such a method is compliant with current airworthiness regulations. However, in the framework of the global aircraft optimization for future and upcoming aircraft, it could be required, e.g., to extend the availability of sensor measurements. Introducing a form of analytical redundancy of these measurements can increase the fault detection performance and result in a weight saving of the aircraft. This can be achieved by exploiting the knowledge of the kinematic relations between the measured variables. These relations are exactly known giving the advantage that no model-mismatches need to be accounted for. Furthermore these relations are valid over the whole flight envelope and general for any type of aircraft. Two example applications will be presented, showing the applicability of the method for the FDI of air data sensors and measurements of the inertial reference unit.     

Sensor Selection in Arbitrary Dimensions

We address the sensor selection problem which arises in tracking and localization applications. In sensor selection, the goal is to select a small number of sensors whose measurements provide a good estimate of a target's state (such as location). We focus on the bounded uncertainty sensing model where the target is a point in the d -dimensional Euclidean space. Each sensor measurement corresponds to a convex polyhedral subset of the space. The measurements are merged by intersecting corresponding sets. We show that, on the plane, four sensors are sufficient (and sometimes necessary) to obtain an estimate whose area is at most twice the area of the best possible estimate (obtained by intersecting all measurements). We also extend this result to arbitrary dimensions and show that a constant number of sensors suffice for a constant factor approximation in arbitrary dimensions. Both constants depend on the dimensionality of the space but are independent of the total number of sensors in the network.     

New approach for aggregating multi-sensory data

The task of sensory data fusion may involve the aggregation of sensory measurements that may be from different phenomenological domains and that, in many cases, could embrace some conflicting information cues. It is rather a challenge to find suitable strategies by which measurements obtained by the different sensors of the system can be aggregated so that a consistent interpretation of these measurements is achieved. In this article, we present a novel approach to achieve this goal. A recursive group utility function that is capable of bringing the group of sensors into consensus is used. After each sensor in the group gathers information relevant to the sensory task, the group engages in what we call the uncertainty estimation stage. This is an information theorybased process that allows each sensor to assess its self-uncertainty and the conditional uncertainties of other sensors. This process facilitates the computation of a weighting scheme that operates recursively on sensor observations until the group reaches a consensus. Whenever new observations are made, the uncertainty estimates of sensors are updated and used to compute a new weighting scheme. To demonstrate the efficacy and to show how the methodology works, the article discusses how the method can be used to tackle the multi-sensor object identification problem. © 1993 John Wiley & Sons, Inc.     

On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage

In this note we consider the following problem. Suppose a set of sensors is jointly trying to estimate a process. One sensor takes a measurement at every time step and the measurements are then exchanged among all the sensors. What is the sensor schedule that results in the minimum error covariance? We describe a stochastic sensor selection strategy that is easy to implement and is computationally tractable. The problem described above comes up in many domains out of which we discuss two. In the sensor selection problem, there are multiple sensors that cannot operate simultaneously (e.g., sonars in the same frequency band). Thus measurements need to be scheduled. In the sensor coverage problem, a geographical area needs to be covered by mobile sensors each with limited range. Thus from every position, the sensors obtain a different view-point of the area and the sensors need to optimize their trajectories. The algorithm is applied to these problems and illustrated through simple examples.     

Soft analyzers for a sulfur recovery unit

This work deals with the design and implementation of soft sensors for a Sulfur Recovery Unit (SRU) in a refinery. Soft sensors are mathematical models able to emulate the behavior of existing sensors on the basis of available measurements. In this application, they are used when sensors are taken off for maintenance. The measurements considered in this work are very important for the environmental impact of the refinery, as they regard pollutant acid gas emissions. Four strategies have been implemented and compared: Multi-Layer Perceptrons (MLP) and Radial Basis Function neural networks, Neuro-Fuzzy networks and nonlinear Least-Squares (LSQ) fitting. The best performance is given by MLP neural networks and nonlinear LSQ, all of them implementing Nonlinear Moving Average models. The best soft sensors have been installed on the on-line distributed control systems of the refinery and on-line performance is highly satisfactory.     

Fault-Tolerant Indirect Adaptive Neurocontrol for a Static Synchronous Series Compensator in a Power Network With Missing Sensor Measurements

Identification and control of nonlinear systems depend on the availability and quality of sensor measurements. Measurements can be corrupted or interrupted due to sensor failure, broken or bad connections, bad communication, or malfunction of some hardware or software (referred to as missing sensor measurements in this paper). This paper proposes a novel fault-tolerant indirect adaptive neurocontroller (FTIANC) for controlling a static synchronous series compensator (SSSC), which is connected to a power network. The FTIANC consists of a sensor evaluation and (missing sensor) restoration scheme (SERS), a radial basis function neuroidentifier (RBFNI), and a radial basis function neurocontroller (RBFNC). The SERS provides a set of fault-tolerant measurements to the RBFNI and RBFNC. The resulting FTIANC is able to provide fault-tolerant effective control to the SSSC when some crucial time-varying sensor measurements are not available. Simulation studies are carried out on a single machine infinite bus (SMIB) as well as on the IEEE 10-machine 39-bus power system, for the SSSC equipped with conventional PI controllers (CONVC) and the FTIANC without any missing sensors, as well as for the FTIANC with multiple missing sensors. Results show that the transient performances of the proposed FTIANC with and without missing sensors are both superior to the CONVC used by the SSSC (without any missing sensors) over a wide range of system operating conditions. The proposed fault-tolerant control is readily applicable to other plant models in power systems.      

Enhanced maximum likelihood grid map with reprocessing incorrect sonar measurements

In this paper, we address the problem of building a grid map as accurately as possible using inexpensive and error-prone sonar sensors. In this research area, incorrect sonar measurements, which fail to detect the nearest obstacle in their beamwidth, generally have been dealt with in the same manner as correct measurements or have been excluded from the mapping. In the former case, the map quality may be severely degraded. In the latter case, the resulting map may have insufficient information after the incorrect measurements are removed because only correct measurements are frequently insufficient to cover the whole environment. We propose an efficient grid-mapping approach that incorporates incorrect measurements in a specialized manner to build a better map; we call this the enhanced maximum likelihood (eML) approach. The eML approach fuses the correct and incorrect measurements into a map based on sub-maps generated from each set of measurements. We also propose the maximal sound pressure (mSP) method to detect incorrect sonar readings using the sound pressure of the waves from sonar sensors. In several indoor experiments, integrating the eML approach with the mSP method achieved the best results in terms of map quality among various mapping approaches. We call this the maximum likelihood based on sub-maps (MLS) approach. The MLS map created using only two sonar sensors exhibited similar accuracy to the reference map, which was an accurate representation of the environment.     

Asynchronous decentralized event-triggered control

In this paper we propose an approach to the implementation of controllers with decentralized strategies triggering controller updates. We consider set-ups with a central node in charge of the computation of the control commands, and a set of not co-located sensors providing measurements to the controller node. The solution we propose does not require measurements from the sensors to be synchronized in time. The sensors in our proposal provide measurements in an aperiodic way triggered by local conditions. Furthermore, in the proposed implementation (most of) the communication between nodes requires only the exchange of one bit of information (per controller update), which could aid in reducing transmission delays and as a secondary effect result in fewer transmissions being triggered.     

氧化铟纳米线的水热-退火合成及其NO2气敏性能研究

以InCl3·4H2O、草酸和氢氧化钠为原料,采用水热-退火路径,合成出了In2O3纳米线.X射线粉末衍射(XRD)和扫描电子显微镜(SEM)表征结果表明:合成产物为立方相的In2O3纳米线,其直径约为30-50 nm,长径比超过40.气敏性能测试结果表明,以该法合成的In2O3纳米线对NO2灵敏度高,选择性好,响应恢复快,可以检测1 ppm(i.e.×10-6)的NO2,是一种应用前景良好的NO2敏感材料.     

Intelligent rotational direction control of passive robotic joint with embedded sensors

Passive compliant joints with springs and dampers ensure a smooth contact with the surroundings, especially if robots are in contact with humans, but the passive compliant joints cannot determine precisely the position of the members of the joint or direction of the collision force. In this paper was proposed the structure of a passive compliant robotic joint with conductive silicone rubber elements as internal embedded sensors. The sensors can operate as absorbers of excessive external collision force instead of springs and dampers and can be used for some measurements. Therefore, this joint presents one type of safe robotic mechanisms with an internally measuring system. The sensors were made by press-curing from carbon-black filled silicone rubber which is an electro active material. Various compression tests of the sensors were done. The main task of this study is to investigate the application of a control algorithm for detecting the direction of the robotic joint angular rotation when subjected to an external collision force. Soft computing methodology, adaptive neuro fuzzy inference strategy (ANFIS), was used for the controller development. The simulation results presented in this paper show the effectiveness of the developed method.     

Flexible hot-film anemometer arrays on curved structures for active flow control on airplane wings

A set of flexible MEMS sensor arrays for flow measurements in boundary layers is presented. The sensor principle of these anemometers is based on convective heat transfer from a hot-film into the fluid. Each sensor consists of a nickel sensing element between copper supply tracks. The functional layers are attached either on a ready-made polyimide foil or on a spin-on polyimide layer. These variants are designed to meet the requirements of measurements in different environments. Spin-on technology enables the use of very thin polyimide layers, ideally suited for measurements in transient flows. It is a unique characteristic of the presented arrays that their total thickness can be scaled from 7 to 52 μm. This is essential, because the sensor thickness has to be adapted to the varying thickness of the boundary layers in different aerodynamic tests. With these sensors we meet the special requirements of a wide range of fluid mechanic experiments but in particular those of future active flow control on airplane wings. For less critical flow conditions with much thicker boundary layers, thicker sensors might be sufficient and cheaper, so that sensors fabricated on ready-made foils are perfect for these applications. Since the presented sensors are flexible, they can be attached on curved aerodynamic structures without any geometric mismatches. The entire development, starting from theoretical investigations, is described. Further, the micro-fabrication is discussed, including photolithography, sputtering and wet-etching. In particular the wet-etching of the sensing element is found to be critical for the functional characteristics.     

A nonparametric learning approach to range sensing from omnidirectional vision

We present a novel approach to estimating depth from single omnidirectional camera images by learning the relationship between visual features and range measurements available during a training phase. Our model not only yields the most likely distance to obstacles in all directions, but also the predictive uncertainties for these estimates. This information can be utilized by a mobile robot to build an occupancy grid map of the environment or to avoid obstacles during exploration—tasks that typically require dedicated proximity sensors such as laser range finders or sonars. We show in this paper how an omnidirectional camera can be used as an alternative to such range sensors. As the learning engine, we apply Gaussian processes, a nonparametric approach to function regression, as well as a recently developed extension for dealing with input-dependent noise. In practical experiments carried out in different indoor environments with a mobile robot equipped with an omnidirectional camera system, we demonstrate that our system is able to estimate range with an accuracy comparable to that of dedicated sensors based on sonar or infrared light.     

Analysis of optochemical absorbance sensors based on bidimensional planar ARROW microoptics

A new approach for developing absorbance optochemical sensors is presented in this paper. It is based on a planar microoptic circuit where an optochemically active membrane, which responds selectively to a compound, is deposited in the device, yielding a part of the guiding planar structure. Light is propagated through the membrane, which changes its spectral absorption properties and controls the selectivity of the measurements by means of several immobilised compounds. This way, high sensitivity of the device can be easily obtained due to relatively long light paths through the membrane, and low response times can be achieved as the analyte diffusion occurs perpendicularly to the light path and through a thin membrane. Experimental results on measurements of the concentration of a specific ion in solution using the fabricated sensors are also presented.     

具有多传感器的CPS系统的攻击检测

信息物理系统（cyber-physical systems，简称CPS）是基于环境感知实现计算、通信与物理元素紧密结合的下一代智能系统，广泛应用于安全攸关的系统和工业控制等领域.信息技术与物理世界的相互作用使得CPS容易受到各种恶意攻击，从而破坏其安全性.主要研究存在瞬态故障的CPS中传感器的攻击检测问题.考虑具有多个传感器测量相同物理变量的系统，其中一些传感器可能受到恶意攻击并提供错误的测量.此外，使用抽象传感器模型，每个传感器为控制器提供一个真实值的可能间隔.已有的用于检测传感器被恶意攻击的方法是保守的.当专业攻击者在一段时间内轻微地或不频繁地操纵传感器的输出时，现有方法很难捕获到攻击，如隐身攻击.为了解决这个问题，设计了一种基于融合间隔和历史测量的传感器攻击检测方法.该方法首先为不同的传感器构建不同的故障模型，使用系统动力学方程把历史测量融入到攻击检测方法中，从不同的方面分析传感器的测量.另外，利用历史测量和融合间隔解决了两个传感器的测量相交时是否存在故障的问题.该方法的核心思想是利用传感器之间的成对不一致关系检测和识别攻击.从EV3地面车辆上获得真实的测量数据来验证算法的性能.实验结果表明，所提出的方法优于现有方法，对各种攻击类型都有较好的检测和识别性能，特别是对于隐身攻击，检测率和识别率大约提高了90%以上.