Viscosity virtual sensor to control combustion in fossil fuel power plants

Thermo-electrical power plants utilize fossil fuel oil to transform the calorific power of fuel into electric power. An optimal combustion in the boiler requires the fuel oil to be in its best conditions. One of fuel's most important properties to consider is viscosity. Viscosity has influence on the optimal combustion between fuel and air. Hardware viscosity meters for fuel oils are expensive and unreliable to operate in power plant conditions. Chemical laboratory measures viscosity accurately with special apparatus, but they cannot be used in a real time process. This paper describes the development of a virtual sensor that estimates fuel oil viscosity in the combustion process of a power plant. A virtual sensor or soft sensor is a computer program that estimates the value of a certain variable based on related measurements and a model of the process where the variable participates. In this project, a probabilistic model is constructed using automatic learning algorithms with historical data and experts' advice. The learning and validation experiments are described and discussed. The virtual sensor is installed in the Tuxpan Power Plant in Veracruz, Mexico.     

High‐accuracy absolute positioning for the stationary planetary rover by integrating the star sensor and inclinometer

In this paper, we introduce a novel method for the high‐accuracy absolute position determination for planetary rovers using the star sensor and inclinometer. We describe the star sensor and inclinometer model and the alignment method for the two sensors. We deduce the compensation algorithm for the atmosphere refraction correction error in detail and provide the rover's position solution, which effectively eliminates the tilt correction error. The experimental site and hardware configuration are introduced, and the experimental steps for the one‐time positioning are described. Three field tests on Earth indicate that the accuracy of the one‐time positioning is higher than 40 m (1σ) using 8 star images and relative inclinometer measurements. Multiple positionings in one night can improve the accuracy to approximately 15 m.     

Cooperative multi-robot systems:: A study of vision-based 3-D mapping using information theory

Building cooperatively 3-D maps of unknown environments is one of the application fields of multi-robot systems. This article addresses that problem through a probabilistic approach based on information theory. A distributed cooperative architecture model is formulated whereby robots exhibit cooperation through efficient information sharing. A probabilistic model of a 3-D map and a statistical sensor model are used to update the map upon range measurements, with an explicit representation of uncertainty through the definition of the map’s entropy. Each robot is able to build a 3-D map upon measurements from its own range sensor and is committed to cooperate with other robots by sharing useful measurements. An entropy-based measure of information utility is used to define a cooperation strategy for sharing useful information, without overwhelming communication resources with redundant or unnecessary information. Each robot reduces the map’s uncertainty by exploring maximum information viewpoints, by using its current map to drive its sensor to frontier regions having maximum entropy gradient. The proposed framework is validated through experiments with mobile robots equipped with stereo-vision sensors.     

A Fuzzy Perceptual Model for Ultrasound Sensors Applied to Intelligent Navigation of Mobile Robots

The way of understanding the role of perception along the intelligent robotic systems has evolved greatly since classic approaches to the reactive behavior-based approaches. Classic approaches tried to model the environment using a high level of accuracy while in reactive systems usually the perception is related to the actions that the robot needs to undertake so that such complex models are not generally necessary. Regarding hybrid approaches is likewise important to understand the role that has been assigned to the perception in order to assure the success of the system. In this work a new perceptual model based on fuzzy logic is proposed to be used in a hybrid deliberative-reactive architecture. This perceptual model deals with the uncertainty and vagueness underlying to the ultrasound sensor data, it is useful to carry out the data fusion from different sensors and it allows us to establish various levels of interpretation in the sensor data. Furthermore, using this perceptual model an approximate world model can be built so that the robot can plan its motions for navigating in an office-like environment. Then the navigation is accomplished using the hybrid deliberative-reactive architecture and taking into account the perceptual model to represent the robot's beliefs about the world. Experiments in simulation and in an real office-like environment are shown for validating the perceptual model integrated into the navigation architecture.     

Design,dynamic modelling and experimental validation of a 2DOF flexible antenna sensor

A two-degree-of-freedom flexible antenna sensor platform was designed to physically simulate the ability of a robotic arm, which rapidly reorients and targets itself towards specific surfaces from different approachable angles. An accurate antenna model involves non-linear expressions that represent the system dynamics. Therefore, a comprehensive study along with experimental work has been carried out in order to achieve accurate system identification and validate the dynamic model. The model developed has proven useful in controlling the antenna tip, minimising the effects of the non-linear flexural dynamics and the Coulomb friction. The system was driven by servo motors. Algebraic controllers were developed for the antenna tip to track the reference trajectory. The platform system used encoders to measure the joint angles and a loadcell sensor to obtain the flexible link tip position. To validate the sensory information, the results obtained by the integrated sensors were compared to that of an external camera system.     

On-line soft sensor for polyethylene process with multiple production grades

Since online measurement of the melt index (MI) of polyethylene is difficult, a virtual sensor model is desirable. However, a polyethylene process usually produces products with multiple grades. The relation between process and quality variables is highly nonlinear. Besides, a virtual sensor model in real plant process with many inputs has to deal with collinearity and time-varying issues. A new recursive algorithm, which models a multivariable, time-varying and nonlinear system, is presented. Principal component analysis (PCA) is used to eliminate the collinearity. Fuzzy c-means (FCM) and fuzzy Takagi–Sugeno (FTS) modeling are used to decompose the nonlinear system into several linear subsystems. Effectiveness of the model is demonstrated using real plant data from a polyethylene process.     

Bayesian modeling of uncertainty in low-level vision

The need for error modeling, multisensor fusion, and robust algorithms is becoming increasingly recognized in computer vision. Bayesian modeling is a powerful, practical, and general framework for meeting these requirements. This article develops a Bayesian model for describing and manipulating the dense fields, such as depth maps, associated with low-level computer vision. Our model consists of three components: a prior model, a sensor model, and a posterior model. The prior model captures a priori information about the structure of the field. We construct this model using the smoothness constraints from regularization to define a Markov Random Field. The sensor model describes the behavior and noise characteristics of our measurement system. We develop a number of sensor models for both sparse and dense measurements. The posterior model combines the information from the prior and sensor models using Bayes' rule. We show how to compute optimal estimates from the posterior model and also how to compute the uncertainty (variance) in these estimates. To demonstrate the utility of our Bayesian framework, we present three examples of its application to real vision problems. The first application is the on-line extraction of depth from motion. Using a two-dimensional generalization of the Kalman filter, we develop an incremental algorithm that provides a dense on-line estimate of depth whose accuracy improves over time. In the second application, we use a Bayesian model to determine observer motion from sparse depth (range) measurements. In the third application, we use the Bayesian interpretation of regularization to choose the optimal smoothing parameter for interpolation. The uncertainty modeling techniques that we develop, and the utility of these techniques in various applications, support our claim that Bayesian modeling is a powerful and practical framework for low-level vision.     

Graphical simulation for sensor based robot programming

The programming of robots is slowly evolving from traditional teach pendant methods to graphical Off-Line Programming (OLP) methods. Graphical simulation tools, such as OLP, are very useful for developing and testing robot programs before they are run on real industrial equipment. OLP systems are also used to develop task level programs. Traditional OLP systems, however, suffer from the limitations of using only position control which does not account for inherent robot inaccuracies and dynamic environments. This paper describes our work on improving and supplementing traditional position control programming methods. A baseline OLP system was implemented at NIST's Automated Manufacturing Research Facility (AMRF). Experience gained in implementing this system showed that an effective OLP system must accurately simulate the real world and must support sensor programming to compensate for real-world changes that cannot be simulated. The developed OLP geometric world model is calibrated using robot mounted ultrasound ranging sensors. This measurement capability produces a baseline geometric model of relatively good static accuracy for off-line programming. The graphical environment must also provide representations of sensor features. For this specific application, force is simulated in order to include force based commands in our robot programs. These sensor based programs are able to run reliably and safely in an unpredictable industrial environment. The last portion of this paper extends OLP and describes the functionality of a complete system for programming complex robot tasks.     

Kriging‐based robotic exploration for soil moisture mapping using a cosmic‐ray sensor

Soil moisture monitoring is a fundamental process to enhance agricultural outcomes and to protect the environment. The traditional methods for measuring moisture content in the soil are laborious and expensive, and therefore there is a growing interest in developing sensors and technologies which can reduce the effort and costs. In this work, we propose to use an autonomous mobile robot equipped with a state‐of‐the‐art noncontact soil moisture sensor building moisture maps on the fly and automatically selecting the most optimal sampling locations. We introduce an autonomous exploration strategy driven by the quality of the soil moisture model indicating areas of the field where the information is less precise. The sensor model follows the Poisson distribution and we demonstrate how to integrate such measurements into the kriging framework. We also investigate a range of different exploration strategies and assess their usefulness through a set of evaluation experiments based on real soil moisture data collected from two different fields. We demonstrate the benefits of using the adaptive measurement interval and adaptive sampling strategies for building better quality soil moisture models. The presented method is general and can be applied to other scenarios where the measured phenomena directly affect the acquisition time and need to be spatially mapped.     

Quadratic programming based data assimilation with passive drifting sensors for shallow water flows

We present a method for assimilating Lagrangian sensor measurement data into a shallow water equation model. The underlying estimation problem (in which the dynamics of the system are represented by a system of partial differential equations) relies on the formulation of a minimisation of an error functional, which represents the mismatch between the estimate and the measurements. The corresponding so-called variational data assimilation problem is formulated as a quadratic programming problem with linear constraints. For the hydrodynamics application of interest, data is obtained from drifting sensors that gather position and velocity. The data assimilation method refines the estimate of the initial conditions of the hydrodynamic system. The method is implemented using a new sensor network hardware platform for gathering flow information from a river, which is presented in this article for the first time. Validation of the results is performed by comparing them to an estimate derived from an independent set of static sensors, some of which were deployed as part of our field experiments.     

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.     

Virtual 3D City Model for Navigation in Urban Areas

In this paper, we propose to study the integration of a new source of a priori information, which is the virtual 3D city model. We study this integration for two tasks: vehicles geo-localization and obstacles detection. A virtual 3D city model is a realistic representation of the evolution environment of a vehicle. It is a database of geographical and textured 3D data. We describe an ego-localization method that combines measurements of a GPS (Global Positioning System) receiver, odometers, a gyrometer, a video camera and a virtual 3D city model. GPS is often consider as the main sensor for localization of vehicles. But, in urban areas, GPS is not precise or even can be unavailable. So, GPS data are fused with odometers and gyrometer measurements using an Unscented Kalman Filter (UKF). However, during long GPS unavailability, localization with only odometers and gyrometer drift. Thus, we propose a new observation of the location of the vehicle. This observation is based on the matching between the current image acquired by an on-board camera and the virtual 3D city model of the environment. We also propose an obstacle detection method based on the comparison between the image acquired by the on-board camera and the image extracted from the 3D model. The following principle is used: the image acquired by the on-board camera contains the possible dynamic obstacles whereas they are absent from the 3D model. The two proposed concepts are tested on real data.     

Magnetic survey and autonomous target reacquisition with a scalar magnetometer on a small AUV

A scalar magnetometer payload has been developed and integrated into a two‐man portable autonomous underwater vehicle (AUV) for geophysical and archeological surveys. The compact system collects data from a Geometrics microfabricated atomic magnetometer, a total‐field atomic magnetometer. Data from the sensor is both stored for post‐processing and made available to an onboard autonomy engine for real‐time sense and react behaviors. This system has been characterized both in controlled laboratory conditions and at sea to determine its performance limits. Methodologies for processing the magnetometer data to correct for interference and error introduced by the AUV platform were developed to improve sensing performance. When conducting seabed surveys, detection and characterization of targets of interest are performed in real‐time aboard the AUV. This system is used to drive both single‐ and multiple‐vehicle autonomous target reacquisition behaviors. The combination of on‐board target detection and autonomous reacquire capability is found to increase the effective survey coverage rate of the AUV‐based magnetic sensing system.     

基于Agent的虚拟抢修任务系统的设计与实现

针对复杂装备抢修现实问题,提出了构建虚拟抢修任务系统概念模型,建立了其系统框架,并搭建了基于多Agent的系统结构,分析确定了系统运行的事件驱动机制.在完成该Agent系统实现方法、系统行为及Agent行为推理的Petri网模型研究之后,设计实现了该虚拟抢修任务系统.该系统对于优化复杂装备的抢修性设计,提高装备使用单位抢修水平都有一定的借鉴和指导意义,可以有效地解决复杂装备现实抢修面临的诸多问题.     

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

Gaining information about an unknown gas source is a task of great importance with applications in several areas, including responding to gas leaks or suspicious smells, quantifying sources of emissions, or in an emergency response to an industrial accident or act of terrorism. In this paper, a method to estimate the source term of a gaseous release using measurements of concentration obtained from an unmanned aerial vehicle (UAV) is described. The source term parameters estimated include the three‐dimensional location of the release, its emission rate and other important variables needed to forecast the spread of the gas using an atmospheric transport and dispersion model. The parameters of the source are estimated by fusing concentration observations from a gas detector on‐board the aircraft, with meteorological data and an appropriate model of dispersion. Two models are compared in this paper, both derived from analytical solutions to the advection–diffusion equation. Bayes’ theorem, implemented using a sequential Monte Carlo algorithm, is used to estimate the source parameters to take into account the large uncertainties in the observations and formulated models. The system is verified with novel, outdoor, fully automated experiments, where observations from the UAV are used to estimate the parameters of a diffusive source. The estimation performance of the algorithm is assessed subject to various flight path configurations and wind speeds. Observations and lessons learned during these unique experiments are discussed, and areas for future research are identified.     

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.     

基于流演算的智能虚拟人模型研究与实现*

在研究流演算理论及其实现语言FLUX的基础上,将流演算与虚拟现实技术中的虚拟人相结合,提出了一个基于目标驱动的、有自主行动能力的虚拟人模型。设计了动作检测模块,同时使用了动作队列,根据动作检测的结果来决定是否执行下一个动作,使虚拟人可以针对动态变化的虚拟环境进行有效的行动规划。利用此模型可以快速构建出一个在不完全可知的虚拟环境中通过感知到的有限信息进行实时的、自主行动推理的智能虚拟人。最后,实现了办公室场景中智能虚拟人行动推理系统。     

基于GIS和VR的网上房展系统的设计与实现

研究利用WebGIS的空间可视化和空间分析的方法以及虚拟现实的相关技术开发网上房产信息展示系统.给出系统架构和主要功能,重点对系统的邻近分析、专题地图和虚拟房屋等关键技术的原理、方法和应用过程进行的阐述.系统基于房产实时数据,运用最新的WebGIS和VR的相关技术,在网上对房产信息进行基于矢量地图和虚拟场景的展示,为房产商提供了一个集成先进技术、统一管理、科学决策、节省成本的产品展示平台.最后对系统的特点和应用进行了总结.     

Metaphor and Manifestation Cross-Reality with Ubiquitous Sensor/Actuator Networks

A series of projects by the MIT Media Lab's Responsive Environments Group explore ways of bridging the rapidly expanding reach of networked electronic sensors with the limited realm of human perception. These include various implementations of cross-reality which render and manifest phenomena between the real world and shared online virtual environments via densely embedded sensor and actuator networks. We visualize information from ubiquitously deployed real-world smart power strips and sensor-rich media portals at different levels of abstraction through analogous Second Life constructs. Conversely, we manifest virtual world events into physical space using on-platform actuators and displays. We also show a set of simpler 2D visualizations that enable mobile devices to efficiently browse and interact with sensor network data. We touch on a recently developed system that uses a small badge to passively manage dynamic privacy in environments such as these that stream potentially revealing information across the real/virtual divide. These technologies' application areas involve fluid browsing of and interaction with the geographically dispersed real world in an unconstrained virtual environment and ubiquitous multiscale telepresence.     

Strain sensors based on stress-annealed Co69Fe2Cr7Si8B14 amorphous ribbons

An application of the sensors in strain and load measurements for outdoor applications requires both high corrosion resistance and independence of measuring signal on long-term moisture of the surroundings. One of the best candidates for this field of application is a magnetoelastic sensor using the amorphous magnetic ribbons with negative magnetostriction. Two-coil strain sensors based on stress-annealed Co₆₉Fe₂Cr₇Si₈B₁₄ amorphous magnetic ribbons were designed, fabricated, and evaluated. Their sensitivity is much more higher compared with resistance gauges, allowing low-price electronic portable equipment for outdoor measurements.