Distributed Communication and Localization Algorithms for Homogeneous Robotic Swarm

Multiple blind sound source localization is the key technology for a myriad of applications such as robotic navigation and indoor localization. However, existing solutions can only locate a few sound sources simultaneously due to the limitation imposed by the number of microphones in an array. To this end, this paper proposes a novel multiple blind sound source localization algorithms using Source seParation and BeamForming (SPBF). Our algorithm overcomes the limitations of existing solutions and can locate more blind sources than the number of microphones in an array. Specifically, we propose a novel microphone layout, enabling salient multiple source separation while still preserving their arrival time information. After then, we perform source localization via beamforming using each demixed source. Such a design allows minimizing mutual interference from different sound sources, thereby enabling finer AoA estimation. To further enhance localization performance, we design a new spectral weighting function that can enhance the signal-to-noise-ratio, allowing a relatively narrow beam and thus finer angle of arrival estimation. Simulation experiments under typical indoor situations demonstrate a maximum of only 4∘ even under up to 14 sources.

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Accuracy analysis of BLE beacon-based localization in smart buildings

Journal of Ambient Intelligence and Smart Environments ◽

10.3233/ais-210607 ◽

2021 ◽

pp. 1-20

Author(s):

Rosen Ivanov

Keyword(s):

Use Case ◽

Accuracy Analysis ◽

Localization Algorithm ◽

Computing Power ◽

Smart Buildings ◽

Localization Accuracy ◽

Positioning Systems ◽

Deployment Strategy ◽

Accurate Localization ◽

Localization Algorithms

The majority of services that deliver personalized content in smart buildings require accurate localization of their clients. This article presents an analysis of the localization accuracy using Bluetooth Low Energy (BLE) beacons. The aim is to present an approach to create accurate Indoor Positioning Systems (IPS) using algorithms that can be implemented in real time on platforms with low computing power. Parameters on which the localization accuracy mostly depends are analyzed: localization algorithm, beacons’ density, deployment strategy, and noise in the BLE channels. An adaptive algorithm for pre-processing the signals from the beacons is proposed, which aims to reduce noise in beacon’s data and to capture visitor’s dynamics. The accuracy of five range-based localization algorithms in different use case scenarios is analyzed. Three of these algorithms are specially designed to be less sensitive to noise in radio channels and require little computing power. Experiments conducted in a simulated and real environment show that using proposed algorithms the localization accuracy less than 1 m can be obtained.

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Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement

Sensors ◽

10.3390/s21072400 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2400

Author(s):

Ziyong Zhang ◽

Xiaoling Xu ◽

Jinqiang Cui ◽

Wei Meng

Keyword(s):

Quadratic Programming ◽

Wide Band ◽

Area Coverage ◽

Localization Algorithm ◽

Placement Problem ◽

Position Information ◽

Relative Localization ◽

Coverage Control ◽

Localization Algorithms ◽

Multi Uav

This paper is concerned with relative localization-based optimal area coverage placement using multiple unmanned aerial vehicles (UAVs). It is assumed that only one of the UAVs has its global position information before performing the area coverage task and that ranging measurements can be obtained among the UAVs by using ultra-wide band (UWB) sensors. In this case, multi-UAV relative localization and cooperative coverage control have to be run simultaneously, which is a quite challenging task. In this paper, we propose a single-landmark-based relative localization algorithm, combined with a distributed coverage control law. At the same time, the optimal multi-UAV placement problem was formulated as a quadratic programming problem by compromising between optimal relative localization and optimal coverage control and was solved by using Sequential Quadratic Programming (SQP) algorithms. Simulation results show that our proposed method can guarantee that a team of UAVs can efficiently localize themselves in a cooperative manner and, at the same time, complete the area coverage task.

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Corrigendum to “Control optimization of an aerial robotic swarm in a search task and its adaptation to different scenarios” [J. Comput. Sci. 29 (2018) 107–118]

Journal of Computational Science ◽

10.1016/j.jocs.2020.101258 ◽

2020 ◽

pp. 101258

Author(s):

Pablo Garcia-Aunon ◽

Antonio Barrientos Cruz

Keyword(s):

Search Task ◽

Control Optimization ◽

Robotic Swarm

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Neural silences can be localized rapidly using noninvasive scalp EEG

Communications Biology ◽

10.1038/s42003-021-01768-0 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Alireza Chamanzar ◽

Marlene Behrmann ◽

Pulkit Grover

Keyword(s):

Center Of Mass ◽

Cost Effective ◽

Human Head ◽

Head Model ◽

Cortical Function ◽

Scalp Eeg ◽

Localization Algorithms ◽

Important Benefit ◽

Eeg Recordings ◽

Detection And Localization

AbstractA rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

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