Localizing an intermittent and moving sound source using a mobile robot

Nguyen, Quan V.; Colas, Francis; Charpillet, François

doi:10.1109/iros.2016.7759313

Cited by 15 publications

(10 citation statements)

References 22 publications

(34 reference statements)

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“…We model this belief via a mixture of Gaussians and estimate it using the mixture Kalman filter (MKF) implemented in [26]. In [26], we also define P (a t+1 |a t ) as the source activity transition probability table, P (X t+1 |X t ) as the state transition model and P (Z t+1 |X t+1 , a t+1 ) as the observation model. The latter is built by acoustic simulation, taking reverberation and noise into account.…”

Section: Mixture Kalman Filtermentioning

confidence: 99%

“…To start from an informative belief, the robot first follows a fixed trajectory while updating the belief using MKF [26] for 3 s. After this, it follows the proposed MCTS algorithm with λ = 0.5, T = 20, and 700 tree nodes. The action set contains 13 discrete actions.…”

Section: Algorithm Settingsmentioning

confidence: 99%

“…Each level of the tree corresponds to one future time step and each node in the tree represents one robot pose at one time. We estimate the belief corresponding to a sequence of poses by a nonlinear mixture Kalman filter [26]. Second, we conduct an experimental validation of our algorithm in simulation with several realistic scenarios and compare it with other motion planning algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Mixture Kalman Filtermentioning

confidence: 99%

Section: Algorithm Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…For example, microphones can be equipped around a room to track human activities [11,12]. Similar to drones, land-based robots can also benefit from microphone arrays, so they can detect and localize sound sources and recognize the environment of the surroundings [13][14][15][16][17]. These tracking methods can be applied to drone systems.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Sound Source Tracking Using Multiple Drones Equipped with Multiple Microphone Arrays

Yamada

Itoyama

Nishida

et al. 2021

IJERPH

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Drone audition techniques are helpful for listening to target sound sources from the sky, which can be used for human searching tasks in disaster sites. Among many techniques required for drone audition, sound source tracking is an essential technique, and thus several tracking methods have been proposed. Authors have also proposed a sound source tracking method that utilizes multiple microphone arrays to obtain the likelihood distribution of the sound source locations. These methods have been demonstrated in benchmark experiments. However, the performance against various sound sources with different distances and signal-to-noise ratios (SNRs) has been less evaluated. Since drone audition often needs to listen to distant sound sources and the input acoustic signal generally has a low SNR due to drone noise, making a performance assessment against source distance and SNR is essential. Therefore, this paper presents a concrete evaluation of sound source tracking methods using numerical simulation, focusing on various source distances and SNRs. The simulated results captured how the tracking performance will change when the sound source distance and SNR change. The proposed approach based on location distribution estimation tended to be more robust against distance increase, while existing approaches based on directional estimation tended to be more robust against decreasing SNR.

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“…Our first contribution lies in the development of an extended mixture Kalman filter (MKF) framework for jointly estimating the location and activity of an intermittent and moving source in a reverberant environment. As an extension from our preliminary work [47], in this paper we provide a detailed analysis of the robustness of the extended MKF framework to both false SAD and false AoA measurements. The second main contribution concerns the adaptation of the Monte Carlo tree search (MCTS) method to the problem of long-term robot motion planning for improving source localization.…”

Section: Introductionmentioning

confidence: 99%

Motion planning for robot audition

2019

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Robot audition refers to a range of hearing capabilities which help robots explore and understand their environment. Among them, sound source localization is the problem of estimating the location of a sound source given measurements of its angle of arrival with respect to a microphone array mounted on the robot. In addition, robot motion can help quickly solve the frontback ambiguity existing in a linear microphone array. In this article, we focus on the problem of exploiting robot motion to improve the estimation of the location of an intermittent and possibly moving source in a noisy and reverberant environment. We first propose a robust extended mixture Kalman filtering framework for jointly estimating the source location and its activity over time. Building on this framework, we then propose a long-term robot motion planning algorithm based on Monte Carlo tree search to find an optimal robot trajectory according to two alternative criteria: the Shannon entropy or the standard deviation of the estimated belief on the source location. These criteria are integrated over time using a discount factor. Experimental results show the robustness of the proposed estimation framework to false angle of arrival measurements within ±20 • and 10% false source activity detection rate. The pro

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Localizing an intermittent and moving sound source using a mobile robot

Cited by 15 publications

References 22 publications

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

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

Assessment of Sound Source Tracking Using Multiple Drones Equipped with Multiple Microphone Arrays

Motion planning for robot audition

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