Bio-Inspired Distance Estimation using the Self-Induced Acoustic Signature of a Motor-Propeller System
Luke Calkins, Joseph Lingevitch, Loy McGuire, Jason Geder, Matthew Kelly, Michael M. Zavlanos, Donald Sofge, Daniel M. Lofaro
Abstract
In this paper we propose an algorithm to actively control the distance of a motor-propeller system (MPS) to a large obstacle using data from a single microphone. The method is based upon a broadband constructive/destructive interference pattern across the audible frequency band that is present when the MPS is near an obstacle. By taking the difference between the power spectrum in the obstacle-free case and the spectrum when recording near an obstacle, a broadband oscillation with respect to frequency is revealed. The frequency of this oscillation is linearly-related to the distance from the microphone to the wall. We present both static and dynamic experiments showcasing the ability of the proposed method to estimate the distance to a wall as well as actively control it.
BibTeX
@inproceedings{icra2020_bioinspireddista,
title = {Bio-Inspired Distance Estimation using the Self-Induced Acoustic Signature of a Motor-Propeller System},
author = {Luke Calkins and Joseph Lingevitch and Loy McGuire and Jason Geder and Matthew Kelly and Michael M. Zavlanos and Donald Sofge and Daniel M. Lofaro},
booktitle = {ICRA 2020},
year = {2020}
}