Motion-induced acoustic noise awareness for socially-aware robot navigation

Andrade, Juliane; Santana, Pedro; Almeida, Alexandre

doi:10.1109/icarsc.2018.8374155

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…This article is an extended and improved version of a previously published conference paper [1], providing a more detailed description of the proposed system alongside a deeper analysis of the experimental Fig. 1.…”

Section: �� Trod�c�o�mentioning

confidence: 99%

Self-Supervised Learning of Motion-Induced Acoustic Noise Awareness in Social Robots

Andrade¹,

Santana²,

Almeida³

2019

JAMRIS

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With the growing presence of robots in human populated environments, it becomes necessary to render their presence natural, rather than invasive. To do that, robots need to make sure the acous�c noise induced by their mo�on does not disturb people nearby. �n this line, this paper proposes a method that allows the robot to learn how to control the amount of noise it produces, taking into account the environmental context and the robot's mechanical characteris�cs. �oncretely, the robot adapts its mo�on to a speed that allows it to produce less noise than the environment's background noise and, hence, avoiding to disturb nearby humans. For that, before execu�ng any given task in the environment, the robot learns how much acous�c noise it produces at di�erent speeds in that environment by gathering acous�c in-forma�on through a microphone. The proposed method was successfully validated on various environments with various background noises. �n addi�on, a �� sensor was installed on the robot in order to test the robot's ability to trigger the noise-aware speed control procedure when a person enters the sensor's field of view. The use of a such a simple sensor aims at demonstra�ng the ability of the proposed system to be deployed in minimalis�c robots, such as micro unmanned aerial vehicles.

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Section: �� Trod�c�o�mentioning

confidence: 99%

Self-Supervised Learning of Motion-Induced Acoustic Noise Awareness in Social Robots

Andrade¹,

Santana²,

Almeida³

2019

JAMRIS

Self Cite

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“…Spatial analysis of sound and source localization can add new capabilities to such context-aware applications. On the other hand, spatial sensing of sound plays a key role in robotic navigation and situational awareness systems, both in air [2,26,27] and underwater [32,43]. However, traditional techniques for obtaining spatial information of sound require multiple streams of simultaneously recorded sound using an array of microphones -an energy-hungry hardware requirement often difficult to meet on the stand-alone sensing modules.…”

Section: Introductionmentioning

confidence: 99%

Owlet

Garg

Bai

Roy

2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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This paper presents a low-power and miniaturized design for acoustic direction-of-arrival (DoA) estimation and source localization, called Owlet. The required aperture, power consumption, and hardware complexity of the traditional array-based spatial sensing techniques make them unsuitable for small and power-constrained IoT devices. Aiming to overcome these fundamental limitations, Owlet explores acoustic microstructures for extracting spatial information. It uses a carefully designed 3D-printed metamaterial structure that covers the microphone. The structure embeds a direction-specific signature in the recorded sounds. Owlet system learns the directional signatures through a one-time in-lab calibration. The system uses an additional microphone as a reference channel and develops techniques that eliminate environmental variation, making the design robust to noises and multipaths in arbitrary locations of operations. Owlet prototype shows 3.6 • median error in DoA estimation and 10 median error in source localization while using a 1.5 × 1.3 acoustic structure for sensing. The prototype consumes less than 100 ℎ of the energy required by a traditional microphone array to achieve similar DoA estimation accuracy. Owlet opens up possibilities of low-power sensing through 3D-printed passive structures. CCS CONCEPTS• Computer systems organization → Embedded and cyberphysical systems; Sensors and actuators.

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Motion-induced acoustic noise awareness for socially-aware robot navigation

Cited by 2 publications

References 9 publications

Self-Supervised Learning of Motion-Induced Acoustic Noise Awareness in Social Robots

Self-Supervised Learning of Motion-Induced Acoustic Noise Awareness in Social Robots

Owlet

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