Accelerometer based localization for distributed

1
Accelerometer based localization for distributed
off-the-shelf robots (Cots-Bots) Thomas
Cheng, Sarah Bergbreiter Advisor Prof. K.S.J.
Pister
Hardware/software setup
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.

• Custom accelerometer boards1. 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-bots1. 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.)

• Software1. 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.

Accelerometer data analysis pass 2
Hardware in useCustom 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.
Sonar Sensor
ADXL202e Accelerometer

• 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.
• 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.

Useful data has very high magnitude compare to
noise
Cots bot with driver mote
Infrared Distance Sensor
Accelerometer data analysis pass 1
Hardware in useCustom accelerometer board with
max sampling frequency limited by 1st order
analog low-pass filter of pole frequency 500Hz.
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 _at_ 2 Hz)
Reasons for using this hardware Higher max
sampling frequency will allow the mote to resolve
higher-frequency 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.
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.

• Filtered accelerometer integrated for
  displacement 66th order low pass filter
  implemented in direct form transpose IISpects
  Fpass 0.5Hz, Fstop 2Hz, Fdigital_sample
  50Hz

• 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 21 at 10cm/s, and 11 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.

STDD 0.042 G
ADC units 3mV
STDD 0.088 G
ADC units 3mV
Samples
Frequency 0.25Hz
Digital filtering of accelerometer signals

• 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.

• Results
• The 49th 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.

Connector noise
Normalized ADC units 3mV
Red filtered Blue unfiltered
Packet loss