Learning to Listen and Move: An Implementation of Audio-Aware Mobile Robot Navigation in Complex Indoor Environment

Sound is an essential navigation cue that intelligent robots can leverage for localizing sound-emitting targets. This work introduces a framework for the audio-aware navigation task of mobile robots equipped with a microphone array in a complex indoor environment. The robot initialized at a random starting position has to accurately localize a distant sound source and plan an optimal path towards the sound-emitting target. Auto-encoders are used to extract implicit acoustic features that are robust against environmental noise and reverberation. The proposed framework is based on two key ideas - a sound inference module (SIM) that maps the perceived acoustic information to a given geometric map of the physical space, and a path planner that generates a path from the robot's current position to the estimated position of the sound source. Experimental results show that the SIM achieved a minimum and maximum localization error of 0.31 m and 0.70 m at a robot-source distance of 1 m and 6 m, respectively at different environmental configurations. Additionally, the proposed framework achieved a minimum and maximum reliability of 4.38
$m^{-1}$
and 2.31
$m^{-1}$
at a robot-source distance of 1 m and 6 m, respectively under the influence of background noise.