There is an old white paper posted on CD
http://www.chiefdelphi.com/media/papers/1449
as noted in the white paper, this game is relatively
standard in the signal processing business.
Assuming that you have a gyro that has inherent noise
that is larger than the quantization error, for a stationary
robot there will be a sequence of random (integer) values
returned by the ADC. What you want is the average and
this is not an integer. Obtaining this average, in suitable
precision, is the goal of sampling the gyro for a suitable
length of time while the robot is known to be still.
We represent the average in binary fixed point, a 32 bit
value in 256ths, so to speak. We then use this average
as the zero value for our time integration. It is very
straight forward to do, using the trapezoidal rule we
just shift the old value and the new value up by the right
number of bits before we subtract the average.
The resulting accuracy is good enough that the temperature
dependent drift of the gyro can become significant.
We address this by watching the drift of the integral and
re-measuring the required average when the drift becomes
larger than we want. Another approach is to read the temperature
output signal provided and compensate.
I'll package our code for this year's robot in a zip file and
put it in a place accessible to you and send you an email
when I have done that.
Quote:
Originally Posted by Kevin Watson
I certainly understand how some noise components can help improve resolution, but I just don't see how integrating gyro noise when the 'bot is known to be still can help. Yes, I realize a deadband would cause problems in applications where precise low angular rate measurements need to be taken, but that doesn't apply to many FRC 'bots.
Can you point me toward a paper or anything else that can help me understand your point? And yes, please send me any code you have that I can work with. I think we have a rate table at work and it might be fun to see if I can get some freebie time on it to take some data.
-Kevin
|