Mobile robot and sound localization

Huang, Jie; Supaongprapa, Tadawute; Terakura, Ikutaka; Ohnishi, Noboru; Sugie, Noboru

doi:10.1109/iros.1997.655085

Cited by 11 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, the steepest intensity gradient generally takes the shortest path from source to the robot. Huang [1] showed sound-based servoing for mobile robots to localize a sound source. Østergaard [2] showed that even with a single microphone, an audio alarm signal has a detectable gradient which can be used to track down the path toward the sound source and they used the audio signals to help solve a multiple-robot-multiple-task allocation problem.…”

Section: Introductionmentioning

confidence: 99%

Sounds Good: Simulation and Evaluation of Audio Communication for Multi-Robot Exploration

Karimian

Brown

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

In order to guide the design of a new multi-robot system, we seek to evaluate two different designs of audio direction sensor. We have implemented a simple but useful audio propagation simulator as an extension to the Stage multi-robot simulator. We use the simulator to evaluate the use of audio signals to improve the performance of a team of robots at a prototypical search task. The results indicate that, for this task, (i) audio can significantly improve team performance, and (ii) binary discrimination of the direction of a sound source (left/right) performs no worse than high-resolution direction information. This result suggests that a simple two-microphone audio system will be useful for our real robots, without advanced signal processing to find sound direction.

show abstract

Section: Introductionmentioning

confidence: 99%

Sounds Good: Simulation and Evaluation of Audio Communication for Multi-Robot Exploration

Karimian

Brown

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

show abstract

“…In many robotic applications, including Human-Robot Interaction [1], Intruder Detection [2], and Sniper Localization [3], robots are being equipped with microphones. In some cases, entire teams of robots [4], are released into the world to accomplish some task requiring audition.…”

Section: Introductionmentioning

confidence: 99%

Noise maps for acoustically sensitive navigation

Martinson

Arkin

2004

SPIE Proceedings

View full text Add to dashboard Cite

More and more robotic applications are equipping robots with microphones to improve the sensory information available to them. However, in most applications the auditory task is very low-level, only processing data and providing auditory event information to higher-level navigation routines. If the robot, and therefore the microphone, ends up in a bad acoustic location, then the results from that sensor will remain noisy and potentially useless for accomplishing the required task. To solve this problem, there are at least two possible solutions. The first is to provide bigger and more complex filters, which is the traditional signal processing approach. An alternative solution is to move the robot in concert with providing better audition. In this work, the second approach is followed by introducing noise maps as a tool for acoustically sensitive navigation. A noise map is a guide to noise in the environment, pinpointing locations which would most likely interfere with auditory sensing. A traditional noise map, in an acoustic sense, is a graphical display of the average sound pressure level at any given location. An area with high sound pressure level corresponds to high ambient noise that could interfere with an auditory application. Such maps can be either created by hand, or by allowing the robot to first explore the environment. Converted into a potential field, a noise map then becomes a useful tool for reducing the interference from ambient noise. Preliminary results with a real robot on the creation and use of noise maps are presented.

show abstract