Discovery of sound sources by an autonomous mobile robot

Martinson, Eric; Schultz, Alan C.

doi:10.1007/s10514-009-9123-1

Cited by 24 publications

(26 citation statements)

References 18 publications

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“…With formulas (3.9) and (3.10) in hand, recalling the closure relations [16] 14) which hold in the sense of distributions, shows that a potential candidate for the filter W (ω) should be…”

Section: Substituting Intoqmentioning

confidence: 99%

“…The main objective of this paper is to present an original approach for detecting the spatial support of noise sources in an attenuating electromagnetic or acoustic medium. The main application envisaged by our work concerns robotic sound or microwave noise source localization and tracking; see, for instance, [12,13,14,15,21]. It is a quite challenging problem to build an autonomous robotic system for finding, investigating, and modeling ambient electromagnetic or sound noise sources in the environment.…”

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confidence: 99%

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Noise Source Localization in an Attenuating Medium

Ammari¹,

Bretin²,

Garnier³

et al. 2012

SIAM J. Appl. Math.

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Abstract. In this paper we consider the problem of reconstructing the spatial support of noise sources from boundary measurements using cross correlation techniques. We consider media with and without attenuation and provide efficient imaging functionals in both cases. We also discuss the case where the noise sources are spatially correlated. We present numerical results to show the viability of the different proposed imaging techniques.AMS subject classifications. 35L05, 35R30; Secondary 47A52, 65J20Key words. Passive imaging, wave propagation, attenuation 1. Introduction. The main objective of this paper is to present an original approach for detecting the spatial support of noise sources in an attenuating electromagnetic or acoustic medium. The main application envisaged by our work concerns robotic sound or microwave noise source localization and tracking; see, for instance, [12,13,14,15,21]. It is a quite challenging problem to build an autonomous robotic system for finding, investigating, and modeling ambient electromagnetic or sound noise sources in the environment. On the other hand, a robot can be a rather significant source of electromagnetic and/or acoustic noise. Detecting or hiding the robot to reduce the risk of being detected is also another challenging problem. As will be seen in this paper, at least two robots have to be used in order to locate noise sources by cross correlation.Passive imaging from noisy signals has been a very active field. It has been shown that the Green's function of the wave equation in an inhomogeneous medium can be estimated by cross correlating the signals emitted by ambient noise sources and recorded by a passive sensor array [7,17,18]. The idea has been used for travel time estimation and background velocity estimation in geophysical contexts, and also for passive sensor imaging of reflectors [9,10], which consists of backpropagating or migrating the cross correlation matrix of the recorded signals. The relation between the cross correlation of ambient noise signals recorded at two observation points and the Green's function between these two points can be proved using the Helmholtz-Kirchhoff identity when the ambient noise sources surround the observation region [5,20] or using stationary phase arguments in the high-frequency regime when the support ambient noise sources are spatially limited [9,19].In [11] the noise source imaging problem is analyzed in a high-frequency asymptotic regime and the support of the noise sources is identified with a special Radon transform. Here we shall consider a general context in non-attenuating and attenuating media. In attenuating media, one can think to first pre-process the data as originally done in [4] and then backpropagate the cross correlation of the pre-processed data in a non-attenuating medium. However, this seems impossible because the recorded data are very long and usually contain a huge amount of additional measurement noise. Instead, we backpropagate the cross correlation of the recorded data with a regularized versi...

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“…With formulas (3.9) and (3.10) in hand, recalling the closure relations [16] 14) which hold in the sense of distributions, shows that a potential candidate for the filter W (ω) should be…”

Section: Substituting Intoqmentioning

confidence: 99%

mentioning

confidence: 99%

Noise Source Localization in an Attenuating Medium

Ammari¹,

Bretin²,

Garnier³

et al. 2012

SIAM J. Appl. Math.

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“…Second, limited mounting space on top of or around a physical robot platform restricts the potential accuracy of sound source localization algorithms because of small inter-microphone distances. A small array, in general, can find the angle to a sound source, but not the distance [4]. Solutions to these problems tend to be application specific.…”

Section: Related Workmentioning

confidence: 99%

Dynamically reconfigurable microphone arrays

Martinson

Fransen²

2011

2011 IEEE International Conference on Robotics and Automation

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Robotic sound localization has traditionally been restricted to either on-robot microphone arrays or embedded microphones in aware environments, each of which have limitations due to their static configurations. This work overcomes the static configuration problems by using visual localization to track multiple wireless microphones in the environment with enough accuracy to combine their auditory streams in a traditional localization algorithm. In this manner, microphones can move or be moved about the environment, and still be combined with existing on-robot microphones to extend array baselines and effective listening ranges without having to re-measure inter-microphone distances.

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“…For example, head rotation can resolve the ambiguity on the angle of arrival [15,16]. Moving towards a source can increase the signal-to-noise ratio [17,18]. However, any robot motion is not optimal in all situations.…”

Section: Introductionmentioning

confidence: 99%

“…The localization accuracy can be improved by following a fixed patrol loop and covering a potential maneuver area [17]. More sophisticated motion strategies based on informationtheoretic criteria, i.e., Shannon entropy, have been proposed [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Long-term robot motion planning for active sound source localization with Monte Carlo tree search

Nguyen

Colas

Charpillet

2017

2017 Hands-Free Speech Communications and Microphone Arrays (HSCMA)

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We consider the problem of controlling a mobile robot in order to localize a sound source. A microphone array can provide the robot with information on source localization. By combining this information with the movements of the robot, the localization accuracy can be improved. However, random robot motion or short-term planning may not result in optimal localization. In this paper, we propose an optimal longterm robot motion planning algorithm for active source localization. We introduce a Monte Carlo tree search (MCTS) method to find a sequence of robot actions that minimize the entropy of the belief on the source location. A tree of possible robot movements which balances between exploration and exploitation is first constructed. Then, the movement that leads to minimum uncertainty is selected and executed. Experiments and statistical results show the effectiveness of our proposed method on improving sound source localization in the long term compared to other motion planning methods.

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Discovery of sound sources by an autonomous mobile robot

Cited by 24 publications

References 18 publications

Noise Source Localization in an Attenuating Medium

Noise Source Localization in an Attenuating Medium

Dynamically reconfigurable microphone arrays

Long-term robot motion planning for active sound source localization with Monte Carlo tree search

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