The Problem

The Problem

Robotic Sensing: Adaptive Robotic Control for Improved Acoustic Source Localization in 2D
Raphael Schwartz, Zachary Knudsen, Phani Chavali, Patricio S. La Rosa, Ed Richter, and Arye
Nehorai
Department of Electrical and Systems Engineering

Robotic Platform

Abstract
In this project we expand our previous work entitled "Design of a Robotic
Platform and Algorithms for Adaptive Control of Sensing Parameters". We
have shown that the performance of our algorithm for acoustic source
location in 2D can be improved by adaptively controlling the microphone
array geometry. To this end, we built a robotic microphone array with
capability of autonomous control of array geometry constrained to
movement in 1D. In this project we increase the degrees of freedom of our
robotic platform and design a new controlling algorithm in order to
improve even further the performance. In particular, our robots move in 2D
and the pair of microphones can also rotate independently of the robot
orientation. A heuristic approach for the control of robot locations is
presented and validated with real experiments. Labview and Matlab are
used for the implementation of the system.

Motivation
It can be shown that given a particular array geometry and sampling frequency, there are a
finite number of possible locations which can be estimated using two pairs of
microphones. This set of possible points is not uniformly distributed, as is shown in
Figure 2. Further the resolution (defined as the number of points around the actual source
location) depends on the orientation of the microphone pairs. In this work, we intend
mount the microphone pairs on a robot and adaptively move them such that they have a
good resolution around the source.

Mounting the microphone array on robots allows us to change the physical
parameters of the system in real time.
Altering the distance between microphone pairs affects the spatial distribution of the
possible estimation points.
Shifting the array brings the source in-line with the array center.
These movements increase the resolution near the source and improve the estimation.

Higher Resolution

Lower Resolution

Overview
Goal:
Linear Array Configuration

Design a system capable of acquiring measurements to estimate the
acoustic source position in real time and adaptively move the microphone
pairs in 2-D to improve localization resolution.
Approach:
Dataflow programming techniques were used to implement signal
processing architectures. A heuristic algorithm was used to control
movement based on our experimental observations.

Background :
y

v (TimeDelay)
cos
d
1 s

Source

P
[tan( 2 ) tan(1 )]
*
x 2
tan( 2 ) tan(1 )

y*

y*

P tan( 2 ) tan( 1 )
tan( 2 ) tan(1 )

R

Real Time Architecture and Controller Algorithm
Adaptive Control Algorithm for Robot movement
A simulation based heuristic algorithm is used to
determine potential resolution improvement of moving
the robots in each of the systems four degrees of
freedom. For each degree of freedom a number of
simulations are performed to model a variety of different
robotic movements. The robotic movements which
produced a significant simulated resolution improvement
are then used as the commands for the next movement.
This process is performed until the microphones have
been moved to a configuration which best optimizes
localization resolution, given the physical constraints of
movement. A threshold is defined to characterize the
optimal resolution.
The four degrees of freedom which the algorithm seeks
to optimize are the:

2
d

x*
P
L

1

x
d

Sensor

Variables
L is the length of the linear array

is the wavelength of the acoustic waveform
R is the radial distance from the source of the array
d if the distance between sensors pairs

Rotated Array Configuration

Figure 2

Distance between the robots
Orientation of the robots with respect to the source
Movement in the horizontal axis with respect to the
source (shifting movement)
Movement in the vertical axis with respect to the
source (approaching/retreating movement)

The adaptive algorithm also utilizes the robots rotational
capability to point each of the microphone pairs to best
face the source, further improving estimation

Statistical Signal Processing &
Position Control Al gorithms

PC

Sound
Output &
Waveform
Generation

Data
Acquisition
&
Microphone

New array
Configuration

Measurements
y
x

Resolution

Figure 4

Error

Compute
Source
Estimation

Controller &
Robots

Calculate
Resolution

Simulation

Processing &
Plotting

Move the
Robot to new
position

Robot
Movement
Command

Resolution
Improvement

?

Figure 5

Figure 3

References
Joshua York, Acoustic Source Location Using Cross-correlation Algorithms, Fall 2008, http://ese.wustl.edu/~nehorai/josh/students.cec.wustl.edu/_jly1/
Chase LaFont, Robotic Microphone Sensing: Design of A Robotic Platform and Algorithms for Adaptive Control of Sensing Parameters, Fall 2009

Converge & Diverge
Sideways
Shift
Rotate
Approach & Retreat

2X

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