Approximate Consensus of Multiagent Systems With Inaccurate Sensor Measurements

2017 ◽  
Vol 139 (9) ◽  
Author(s):  
Teymur Sadikhov ◽  
Wassim M. Haddad ◽  
Tansel Yucelen ◽  
Rafal Goebel
Keyword(s):  
Measurement Uncertainty ◽  
Measurement Data ◽  
Graph Laplacian ◽  
Sensor Data ◽  
Consensus Protocol ◽  
Location Uncertainty ◽  
The Face ◽  
Sensor Measurement ◽  
Norm Bound ◽  
Time Invariant

One of the main challenges in robotics applications is dealing with inaccurate sensor data. Specifically, for a group of mobile robots, the measurement of the exact location of the other robots relative to a particular robot is often inaccurate due to sensor measurement uncertainty or detrimental environmental conditions. In this paper, we address the consensus problem for a group of agent robots with a connected, undirected, and time-invariant communication graph topology in the face of uncertain interagent measurement data. Using agent location uncertainty characterized by norm bounds centered at the neighboring agent's exact locations, we show that the agents reach an approximate consensus state and converge to a set centered at the centroid of the agents' initial locations. The diameter of the set is shown to be dependent on the graph Laplacian and the magnitude of the uncertainty norm bound. Furthermore, we show that if the network is all-to-all connected and the measurement uncertainty is characterized by a ball of radius r, then the diameter of the set to which the agents converge is 2r. Finally, we also formulate our problem using set-valued analysis and develop a set-valued invariance principle to obtain set-valued consensus protocols. Two illustrative numerical examples are provided to demonstrate the efficacy of the proposed approximate consensus protocol framework.

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

ISO/TS 20914:2019 – a critical commentary

2020 ◽  
Vol 58 (8) ◽  
pp. 1182-1190 ◽  
Author(s):  
Ian Farrance ◽  
Robert Frenkel ◽  
Tony Badrick
Keyword(s):  
Measurement Uncertainty ◽  
Measurement Data ◽  
International Normalized Ratio ◽  
Medical Laboratory ◽  
Uncertainty In Measurement ◽  
Medical Laboratories ◽  
Functional Relationships ◽  
Critical Commentary ◽  
Combined Uncertainty ◽  
Expression Of Uncertainty

AbstractThe long-anticipated ISO/TS 20914, Medical laboratories – Practical guidance for the estimation of measurement uncertainty, became publicly available in July 2019. This ISO document is intended as a guide for the practical application of estimating uncertainty in measurement (measurement uncertainty) in a medical laboratory. In some respects, the guide does indeed meet many of its stated objectives with numerous very detailed examples. Even though it is claimed that this ISO guide is based on the Evaluation of measurement data – Guide to the expression of uncertainty in measurement (GUM), JCGM 100:2008, it is with some concern that we believe several important statements and statistical procedures are incorrect, with others potentially misleading. The aim of this report is to highlight the major concerns which we have identified. In particular, we believe the following items require further comment: (1) The use of coefficient of variation and its potential for misuse requires clarification, (2) pooled variance and measurement uncertainty across changes in measuring conditions has been oversimplified and is potentially misleading, (3) uncertainty in the results of estimated glomerular filtration rate (eGFR) do not include all known uncertainties, (4) the international normalized ratio (INR) calculation is incorrect, (5) the treatment of bias uncertainty is considered problematic, (6) the rules for evaluating combined uncertainty in functional relationships are incomplete, and (7) specific concerns with some individual statements.

Sensor Fusion Based on Strong Tracking Filter for Augmented Reality Registration

2011 ◽  
Vol 467-469 ◽  
pp. 108-113
Author(s):  
Xin Yu Li ◽  
Dong Yi Chen
Keyword(s):  
Augmented Reality ◽  
Inertial Sensor ◽  
Measurement Data ◽  
Sensor Data ◽  
Data Synchronization ◽  
Error Covariance ◽  
Tightly Coupled ◽  
Strong Tracking Filter ◽  
Fading Factor ◽  
Tracking Filter

Accurate tracking for Augmented Reality applications is a challenging task. Multi-sensors hybrid tracking generally provide more stable than the effect of the single visual tracking. This paper presents a new tightly-coupled hybrid tracking approach combining vision-based systems with inertial sensor. Based on multi-frequency sampling theory in the measurement data synchronization, a strong tracking filter (STF) is used to smooth sensor data and estimate position and orientation. Through adding time-varying fading factor to adaptively adjust the prediction error covariance of filter, this method improves the performance of tracking for fast moving targets. Experimental results show the efficiency and robustness of this proposed approach.

scholarly journals OPC Protocol Application for Real-Time Carbon Monitoring System for Industrial Environment

2017 ◽  
Vol 7 (2) ◽  
pp. 1051 ◽  
Author(s):  
Mohd Faiz Rohani ◽  
Noor Azurati Ahmad ◽  
Shamsul Sahibuddin ◽  
Salwani Mohd Daud
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Ghg Emissions ◽  
Measurement Data ◽  
Sensor Data ◽  
Successful Implementation ◽  
Open Platform ◽  
Environmental Monitoring System ◽  
Normal Environment ◽  
Communications Protocol

Global warming is referred to the rise in average surface temperatures on earth primarily due to the Greenhouse Gases (GHG) emissions such as Carbon Dioxide (CO<sub>2</sub>). Monitoring the emissions, either direct or indirect from the industrial processes, is important to control or to minimize their impact on the environment. Most of the existing environmental monitoring system is being designed and developed for normal environment monitoring. Hence, the aim of this project is to develop industrial CO<sub>2 </sub>emission monitoring system which implements industrial Open Platform Communications (OPC) protocol in an embedded microcontroller. The software algorithm based on OPC data format has been designed and programmed into the Arduino microcontroller to interface the sensor data to any existing industrial OPC compliant Supervisory Control and Data Acquisition (SCADA) system<strong>. </strong>The system has been successfully tested in a lab with the suitable environment for real-time CO<sub>2 </sub>emissions measurement. The real-time measurement data has been shown in an industrial SCADA application which indicates successful implementation of the OPC communications protocol.

scholarly journals Semantic and Syntactic Interoperability for Agricultural Open-Data Platforms in the Context of IoT Using Crop-Specific Trait Ontologies

2020 ◽  
Vol 10 (13) ◽  
pp. 4460
Author(s):  
Sahin Aydin ◽  
Mehmet Nafiz Aydin
Keyword(s):  
Open Data ◽  
Sensor Data ◽  
Agricultural Product ◽  
Site Specific ◽  
Domain Specific ◽  
Integration Approach ◽  
Data Platform ◽  
Specific Trait ◽  
Sensor Measurement ◽  
Iot Devices

In recent years, Internet-of-Things (IoT)-based applications have been used in various domains such as health, industry and agriculture. Considerable amounts of data in diverse formats are collected from wireless sensor networks (WSNs) integrated into IoT devices. Semantic interoperability of data gathered from IoT devices is generally being carried out using existing sensor ontologies. However, crop-specific trait ontologies—which include site-specific parameters concerning hazelnut as a particular agricultural product—can be used to make links between domain-specific variables and sensor measurement values as well. This research seeks to address how to use crop-specific trait ontologies for linking site-specific parameters to sensor measurement values. A data-integration approach for semantic and syntactic interoperability is proposed to achieve this objective. An open-data platform is developed and its usability is evaluated to justify the viability of the proposed approach. Furthermore, this research shows how to use web services and APIs to carry out the syntactic interoperability of sensor data in agriculture domain.

scholarly journals Calibration of echosounder using standard target method

2019 ◽  
Vol 283 ◽  
pp. 05005
Author(s):  
Liuqing Yang ◽  
Yi Chen ◽  
Jun Zhang
Keyword(s):  
Measurement Uncertainty ◽  
Tungsten Carbide ◽  
Measurement Data ◽  
Calibration Experiment ◽  
Working Frequency ◽  
Research Institute

In this work, we develop an underwater echosounder and use the standard target method to calibrate the performance of the device. In the calibration experiments, a solid tungsten carbide sphere of 38mm diameter is used as a standard target for calibrating a HPCTB-200-35 echosounder (manufactured by Hangzhou Applied Acoustics Research Institute) with a working frequency of 220 kHz. Further, the measurement data and uncertainty are presented and analyzed; these results demonstrate that the standard target method can calibrate the combined transmitting-receiving response of echosounders effectively. In our calibration experiment, the combined transmitting-receiving response of HPCTB-200-35 is about 33.8 dB, and the measurement uncertainty is about 1.0 dB (k = 2).

scholarly journals Adaptive Linear Quadratic Attitude Tracking Control of a Quadrotor UAV Based on IMU Sensor Data Fusion

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 46 ◽  
Author(s):  
N. Koksal ◽  
M. Jalalmaab ◽  
B. Fidan
Keyword(s):  
Sensor Fusion ◽  
Sensor Data ◽  
Measurement Unit ◽  
Linear Quadratic ◽  
Attitude Tracking ◽  
Control Scheme ◽  
Controller Performance ◽  
Sensor Measurement ◽  
Measurement Noises ◽  
Quadratic Tracking

In this paper, an infinite-horizon adaptive linear quadratic tracking (ALQT) control scheme is designed for optimal attitude tracking of a quadrotor unmanned aerial vehicle (UAV). The proposed control scheme is experimentally validated in the presence of real-world uncertainties in quadrotor system parameters and sensor measurement. The designed control scheme guarantees asymptotic stability of the close-loop system with the help of complete controllability of the attitude dynamics in applying optimal control signals. To achieve robustness against parametric uncertainties, the optimal tracking solution is combined with an online least squares based parameter identification scheme to estimate the instantaneous inertia of the quadrotor. Sensor measurement noises are also taken into account for the on-board Inertia Measurement Unit (IMU) sensors. To improve controller performance in the presence of sensor measurement noises, two sensor fusion techniques are employed, one based on Kalman filtering and the other based on complementary filtering. The ALQT controller performance is compared for the use of these two sensor fusion techniques, and it is concluded that the Kalman filter based approach provides less mean-square estimation error, better attitude estimation, and better attitude control performance.

scholarly journals Assessment of the Equivalence of Low-Cost Sensors with the Reference Method in Measuring PM10 Concentration Using Selected Correction Functions

2020 ◽  
Vol 12 (13) ◽  
pp. 5368
Author(s):  
Tomasz Owczarek ◽  
Mariusz Rogulski ◽  
Piotr O. Czechowski
Keyword(s):  
Measurement Uncertainty ◽  
Low Cost ◽  
Measurement Data ◽  
Gravimetric Method ◽  
Reference Method ◽  
Testing Procedure ◽  
Coefficient Of Determination ◽  
Model Parameters ◽  
Equivalence Testing ◽  
Good Ability

The aim of the work is to demonstrate the possibility of building models to correct the results of measurements of particulate matter PM10 concentrations obtained using low-cost devices. Such devices apply the optical method to values comparable with those obtained using the reference gravimetric method. An additional goal is to show that the results corrected in this way can be used to carry out the procedure for testing equivalence of these methods. The study used generalized regression models (GRMs) to construct corrective functions. The constructed models were assessed using the coefficients of determination and the methodology of calculating the measurement uncertainty of the device. Measurement data from the two tested devices and the reference method were used to estimate model parameters. The measurement data were collected on a daily basis from 1 February to 30 June 2018 in Nowy Sącz. Regression allowed building multiple models with various functional forms and very promising statistical properties as well as good ability to describe the variability of reference measurements. These models also had very low values of measurement uncertainty. Of all the models constructed, a linear model using the original PM10 concentrations from the tested devices, air humidity, and wind speed was chosen as the most accurate and simplest model. Apart from the coefficient of determination, expanded relative uncertainty served as the measure of quality of the obtained model. Its small value, much lower than 25%, indicates that after correcting the results it is possible to carry out the equivalence testing procedure for the low-cost devices and confirm the equivalence of the tested method with the reference method.

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