Transcript Document

Accelerometer based localization for distributed
off-the-shelf robots (Cots-Bots)
Thomas Cheng, Sarah Bergbreiter
Advisor: Prof. K.S.J. Pister
Hardware/software setup
Custom
accelerometer boards:
1. 1st Order low-pass filter with pole freq. 500Hz.
2. 1st Order low-pass filter pole freq. 15Hz.
Acceleration data were sampled at 50Hz using the Atmega8L ADC on
mica mote.
Tradeoff: Higher pole freq. on the hardware permits more resolvable
detail at the cost of SNR and processing cost.
Cots-bots:
Software:
1. A mote will read the accelerometer,
accumulate 10 readings, and send
results wirelessly to a computer.
2. A separate driver-mote is used to drive
the robot semi-autonomously to minimize
packet transmission.
3. A large board is mounted on the
vehicle for positional acquisition by
sonar. (This will be used to check the
accuracy of localization system.)
1. A custom java program is
used to control the robot
wirelessly.
2. A separate program is
used to collect data and
write them to disk in matlab
readable form.
3. Matlab is used to do all
the analysis and digital
filtering. Sonar Sensor
ADXL202e Accelerometer
Objectives
Explore the possibility of using accelerometers as an
instrument to localize robots in short range.
A successful localization system can be a great addition to
micro-robots since the accelerometer is highly efficient in both
size and power consumption compare to any other type of
sensors currently used for localization.
Accelerometer data analysis pass 2
Hardware in use:
Custom accelerometer board with max sampling frequency limited by 1st
order analog low-pass filter of pole frequency 15Hz.
Reasons for using this hardware:
Low analog low-pass filters will prevent high-frequency, high magnitude gear
noise from aliasing down to interfere with signals of interest, namely, very low
frequency accelerations associated with vehicle movement.
Low freq. accelerometer data:
Unlike digital filters, analog low-pass filters will
prevent high-frequency, high magnitude gear
noise from aliasing down to interfere with signals
of interest while being able to maintain low
sampling rates.
Cots bot with driver mote
Infrared Distance Sensor
Accelerometer data analysis pass 1
Hardware in use:
Custom accelerometer board with max sampling frequency limited by 1st
order analog low-pass filter of pole frequency 500Hz.
Reasons for using this hardware:
Higher max sampling frequency will allow the mote to resolve higherfrequency acceleration signals without using up more power for the
accelerometer board. Higher resolution means the mote can acquire more
acceleration data to compute its position.
Useful data has very
high magnitude
compare to noise
Empirical observation of the FT shows that
most of the useful data lies within 1Hz of the
observed acceleration. (large amplitude)
When connector and gear noise are filtered
out. Typical (useful) acceleration is extremely
low. Peaking at 0.08G at its best. Code skipping
on the ADC becomes a significant problem.
Unfiltered accelerometer integrated for displacement:
The integrated acceleration produced velocity as well as displacement
information. Notice the acceleration was cleaner than those of pass 1 due to
the lower analog low pass filter. (oscillating in 30cm block @ 2 Hz)
Vibration analysis:
Vibration is proportional to speed in a non-linear fashion. The spikes seen in the frequency distribution
(around 12Hz) is the result of high frequency gear noise aliasing down from 110 Hz.
Connector noise from the 51-pin connector will start showing up as soon as the vehicle speed goes above
40cm/s (not shown in this graph). The spikes can show erroneous accelerations as high as 2G, which is
huge compare to a nominal acceleration of 0.08G.
ADC units [3mV]
ADC units [3mV]
Frequency [0.25Hz]
STDD: 0.042 G
STDD: 0.088 G
Samples
Results:
Significant noise from the mechanical gear is
comparable in magnitude compare to signal of
interest, resulting in very poor signal to noise
ratio of approx 2:1 at 10cm/s, and 1:1 at
40cm/s. (Typical acceleration is about 0.08G)
Gear noise is fairly predictable as a function
of speed. However, at higher speeds, its
frequency distribution will begin to spread,
which makes it difficult to filter.
Gear noise is less predictable at high speeds.
Digital filtering of accelerometer signals:
Normalized ADC units [3mV]
Connector
noise
Packet loss
Red – filtered
Blue – unfiltered
Results:
The
order digital low-pass filter
implemented in matlab did smooth the
data significantly, however, noise aliased
down from the gear can still have very
adverse effects on the data because our
digital filters are severely limited by the
sampling rate of the accelerometer. As a
result, some high-frequency noise
cannot be eliminated, resulting in bad
data.
49th
Filtered accelerometer integrated for displacement:
66th order low pass filter implemented in direct form transpose II
Spects: Fpass – 0.5Hz, Fstop – 2Hz, Fdigital_sample – 50Hz
Results:
The drift and noise contributed to unacceptable results. Total drift in 30
seconds – 10m, in addition, relative position was not characterized
accurately primarily because the useful signals were unexpectedly
weak. Much higher gain in addition to drift compensation will be required
to obtain better positional information.
Despite the fact that filtering reduces visible high-frequency noise in
measured acceleration significantly, it has very little impact on the
displacement obtained.