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Navigation in an unknown environment without any preexisting positioning infrastructure has always been hard for mobile robots. This paper presents a self-deployable ultra wideband UWB infrastructure by mobile agents, that permits a dynamic placement and runtime extension of UWB anchors infrastructure while the robot explores the new environment. We provide a detailed analysis of the uncertainty of the positioning system while the UWB infrastructure grows. Moreover, we developed a genetic algorithm that minimizes the deployment of new anchors, saving energy and resources on the mobile robot and maximizing the time of the mission. Although the presented approach is general for any class of mobile system, we run simulations and experiments with indoor drones. Results demonstrate that maximum positioning uncertainty is always controlled under the user’s threshold, using the Geometric Dilution of Precision (GDoP). [ABSTRACT FROM AUTHOR]

The positioning problem addressed in this article amounts to finding the planar coordinates of a device from a collection of ranging measurements taken from other devices located at known positions. The solution based on weighted least square (WLS) is popular, but its accuracy depends from a number of factors only partially known. In this article, we explore the dependency of the uncertainty from the geometric configuration of the anchors. We show a refinement technique for the estimate produced by the WLS that compensates for the effects of geometry on the WLS and reduces the target uncertainty to a value very close to the Cramer–Rao Lower Bound. The resulting algorithm is called geometric WLS (G-WLS) and its application is particularly important in the most critical conditions for WLS (i.e., when the target is far apart from the anchors). The effectiveness of the G-WLS is proven theoretically and is demonstrated on a large number of experiments and simulations. [ABSTRACT FROM AUTHOR]

This article presents the application of the synthetic aperture radar (SAR) localization method for indoor positioning of ultrahigh-frequency (UHF)-radio frequency identification (RFID) tags when the robot-mounted reader antenna moves along multiple trajectories. By properly combining the phase data associated with a set of multiple paths, the whole length of the combined synthetic apertures enlarges, and then, the localization accuracy may improve. Besides, during consecutive inventory rounds, several tag position estimates are available, and they can be profitably combined to minimize the localization uncertainty. Different combination approaches are investigated to determine the best choice to improve the localization performance. The method capabilities are discussed through numerical analysis by considering different configurations of the multiple apertures and different measurement uncertainty sources. Finally, the proposed localization method is validated through an experimental analysis carried out with commercial RFID hardware and a robotic wheeled walker, in an indoor scenario, by employing different types of tags. The knowledge of the reader/robot trajectory required by the SAR method is here achieved with an optical system. [ABSTRACT FROM AUTHOR]

A well-known, crucial problem for indoor positioning of mobile agents (e.g., robots) equipped with exteroceptive sensors is related to the need to deploy reference landmarks in a given environment. Normally, anytime a landmark is detected, an agent estimates its own location and attitude with respect to landmark position and/or orientation in the chosen reference frame. When instead no landmark is recognized, other sensors (e.g., odometers in the case of wheeled robots) can be used to track the agent position and orientation from the last detected landmark. At the moment, landmark placement is usually based just on common-sense criteria, which are not formalized properly. As a result, positioning uncertainty tends to grow unpredictably. On the contrary, the purpose of this paper is to minimize the number of landmarks, while ensuring that localization uncertainty is kept within wanted boundaries. The developed approach relies on the following key features: a dynamic model describing agents’ motion, a model predicting the agents’ paths within a given environment and, finally, a conjunctive normal form formalization of the optimization problem, which can be efficiently (although approximately) solved by a greedy algorithm. The effectiveness of the proposed landmark placement technique is first demonstrated through simulations in a variety of conditions and then it is validated through experiments on the field, by using non-Bayesian and Bayesian position tracking algorithms. [ABSTRACT FROM AUTHOR]

Next-generation phasor measurement units (PMU) are expected to be more accurate than existing ones, especially to address the stricter requirements of future active distribution grids. From this perspective, the influence of acquisition wideband noise (which includes multiple contributions both in amplitude and in phase) has to be carefully evaluated. In this context, the contribution of this paper is twofold. First, it provides a general framework to evaluate the effect of wideband noise on synchrophasor amplitude, phase, frequency, and rate of change of frequency (ROCOF) estimation. The results of this analysis show that the influence of wideband noise can become critical for compliance with the requirements reported in the IEEE standards C37.118.1-2011 and C37.118.1a-2014, especially for frequency and ROCOF estimation. In addition, this paper reports general guidelines to choose the PMU effective resolution and sampling rate for which the impact of wideband noise on both P Class and M Class PMUs is negligible. [ABSTRACT FROM PUBLISHER]

Indoor localization and position tracking are essential to support applications and services for ambient-assisted living. While the problem of indoor localization is still open and already quite complex per se, in large public places, additional issues of cost, accuracy, and scalability arise. In this paper, the position estimation and tracking technique developed within the project devices for assisted living (DALi) is described, analyzed through simulations, and finally validated by means of a variety of experiments on the field. The goal of the DALi project is to design a robotic wheeled walker guiding people with psychomotor problems. Indeed, people with motor or cognitive impairments are often afraid of moving in large and crowded environments (e.g., because they could lose the sense of direction). In order to mitigate this problem, the position tracking approach described in this paper is based on multisensor data fusion and it is conceived to assure a good tradeoff between target accuracy, level of confidence, and deployment costs. Quite interestingly, the same approach could be used for indoor automated guided vehicles and robotics. [ABSTRACT FROM PUBLISHER]

As known, phasor measurement units (PMUs) greatly enhance smart grid monitoring capabilities with advantageous impacts on power network management. Generally, PMUs accuracy is expressed in terms of total vector error, which comprises the joint effect of both angle and magnitude errors, thus possibly concealing the algorithm ability to measure phase. Some recent research works emphasize the importance of measuring current or voltage phasor angle with high accuracy (in the order of a few milliradians) at the distribution level. Because this issue is seldom considered in the literature, in this paper the phase measurement accuracy of three algorithms, namely the basic DFT, the windowed Taylor–Fourier filter, and the interpolated dynamic DFT ( \mathrmIpD^2 FT) estimator, is extensively analyzed by means of simulations performed in various conditions described in the Standards IEEE C37.118.1:2011 and EN 50160:2010. In addition, some meaningful considerations about the uncertainty contributions due to imperfect synchronization are reported. [ABSTRACT FROM PUBLISHER]

Next-generation phasor measurement units (PMUs) are expected to play a key role for monitoring the behavior of future smart grids. While most of the PMUs used nowadays in transmission networks rely on static phasor models, more sophisticated representations and stricter accuracy requirements are needed to track amplitude, phase, and frequency changes of power waveforms in strongly dynamic scenarios as those expected in future distribution systems. In this paper, a discrete Fourier transform (DFT)-based algorithm based on a dynamic phasor model (referred to as interpolated dynamic DFT-based synchrophasor estimator) is used to estimate not only amplitude and phase of the collected waveforms, but also their frequency and rate of change of frequency. The performances of the proposed method are evaluated through multiple simulations in different steady-state and transient conditions described in the Standard IEEE C37.118.1-2011. [ABSTRACT FROM PUBLISHER]

Industrial networks for distributed monitoring, control, and automation purposes require high-accuracy clock synchronization in topologies including long chains of cascaded nodes. Unfortunately, accuracy typically degrades as the number of devices and the distance from the synchronization reference node (i.e., the master or grandmaster) grows, because of the accumulation of multiple uncertainty contributions. To mitigate this problem, the so-called transparent clocks are used in some synchronization protocols, such as the precision transparent clock protocol used in PROFINET IO isochronous real time networks and the precision time protocol version 2, standardized as IEEE 1588–2008. In this paper, an optimal servo-clock in the mean square sense is proposed. The controller relies on both a Kalman filter that estimates the clock state difference with respect to the master and a static-state feedback assuring mean square stability even under the effect of significant fluctuations of the synchronization period. Several multiparametric simulation results in a case study based on the features of PROFINET IO devices confirm that excellent performance can be achieved with the proposed approach. [ABSTRACT FROM PUBLISHER]

Indoor localization and tracking of moving human targets is a task of recognized importance and difficulty. In this paper, we describe a position measurement technique based on the fusion of various sensor data collected using a wearable embedded platform. Since the accumulated measurement uncertainty affecting inertial data (especially due to the on-board accelerometer) usually makes the measured position values drift away quickly, a heuristic approach is used to keep velocity estimation uncertainty in the order of a few percent. As a result, unlike other solutions proposed in the literature, localization accuracy is good when the wearable platform is worn at the waist. Unbounded uncertainty growth is prevented by injecting the position values collected at a very low rate from the nodes of an external fixed infrastructure (e.g., based on cameras) into an extended Kalman filter. If the adjustment rate is in the order of several seconds and if such corrections are performed only when the user is detected to be in movement, the infrastructure remains idle most of time with evident benefits in terms of scalability. In fact, multiple platforms could work simultaneously in the same environment without saturating the communication channels. [ABSTRACT FROM PUBLISHER]

The increasing diffusion of mobile and portable devices provided with wireless connectivity makes the problem of distance measurement based on radio-frequency technologies increasingly important for the development of next-generation nomadic applications. In this paper, the performance limitations of two classic wireless ranging techniques based on received signal strength (RSS) and two-way time-of-flight (ToF) measurements, respectively, are analyzed and compared in detail. On the basis of this study, a data fusion algorithm is proposed to combine both techniques in order to improve ranging accuracy. The algorithm has been implemented and tested on the field using a dedicated embedded prototype made with commercial off-the-shelf components. Several experimental results prove that the combination of both techniques can significantly reduce measurement uncertainty. The results obtained with the developed prototype are not accurate enough for fine-grained position tracking in Ambient Assisted Living applications. However, the platform can be successfully used for reliable indoor zoning, e.g., for omnidirectional and adjustable hazard proximity detection. Most importantly, the proposed solution is absolutely general, and it is quite simple and light from the computational point of view. Accuracy could be further improved by using a more isotropic antenna and by integrating the ToF measurement technique at the lowest possible level on the same radio chip used for communication. Usually, this feature is not available in typical low-cost short-range wireless modules, e.g., for wireless sensor networks. Thus, the results of this research suggest that combining RSS with ToF measurements could be a viable solution for chip manufacturers interested in adding ranging capabilities to their radio modules. [ABSTRACT FROM PUBLISHER]