Why you WANT to use a Gyro this year

[nathanww]

By the time aX is showing roll that stands out above the noise floor, isn't your robot in a pretty precarious situation already? I mean, that means that you've both already started to list, and you're not in a position where it can be quickly corrected(the robot has to turn first).

I'm thinking about maybe a hybrid strategy--if the 1g from earth's gravity drops, the robot automatically checks its heading and corrects using a gyro. Of course, this has the same issue with having to integrate the gyro value over the course of a match. Maybe a digital compass?

[EricVanWyk]

Originally Posted by nathanww

By the time aX is showing roll that stands out above the noise floor, isn't your robot in a pretty precarious situation already?

I spent about a week of December doing a tolerance analysis of using an accelerometer as an inclinometer. For the specific part I used, my conclusion was that determining pitch and roll using the X and Y axes was more accurate than using the Z axis. This was assuming a single point calibration at start up.

[JesseK]

Hmm. Is there a way to experimentally determine the gyro drift as a function of time (or amount of degrees already turned...) and use that as an error adjustment during a match? Or is the drift non-constant?

[Jared Russell]

Originally Posted by JesseK

Hmm. Is there a way to experimentally determine the gyro drift as a function of time (or amount of degrees already turned...) and use that as an error adjustment during a match? Or is the drift non-constant?

Well, it's tricky.

Integration drift arises because we are integrating a noisy signal. In other words, every time we read signal X, we are actually reading a random variable which consists of the true X, plus or minus a noise term with a given mean and variance.

If Xobserved is what we see, the effect is:

Xobserved = Xactual + Noise with mean and variance (usually modeled as a Gaussian distribution)

Over time, all these small variances result in a "random walk" in position - you can't recover exactly what the variances were, but you can estimate the uncertainty of your current position estimate.

In other words, you can estimate (with reasonably high confidence), the variance of your current position reading, but you won't know where inside of this error bound you actually are.

In industry, we often try to augment fast measurement sources that drift (like gyros and accelerometers that get integrated) with slower, but stable, measurement sources (like compasses and GPS). Then, you get the best of both worlds.

(Though getting a compass to work on a metal FIRST robot is a challenge in itself).

[nathanww]

AFAIK, you need to use the Z-axis to prevent contamination of your tilt sensing with actual XY acceleration of the robot. You would have to use the X axis if you wanted to do roll sensing, but for a system where you're using a gyro to align yourself, you should only need the z axis.

[Jared Russell]

Originally Posted by nathanww

AFAIK, you need to use the Z-axis to prevent contamination of your tilt sensing with actual XY acceleration of the robot. You would have to use the X axis if you wanted to do roll sensing, but for a system where you're using a gyro to align yourself, you should only need the z axis.

That is correct - you either need to look at all three axes to isolate the robot's accelerations from gravity, or you need to stop the robot (thus ensuring that the only acceleration is gravity).

Check out this link at Analog Devices:

http://www.analog.com/static/importe...es/AN-1023.pdf

Fall Detection Application by Using 3-Axis Accelerometer ADXL345

Brian C

[Tom Line]

Is there any reason you couldn't use a second gyro, oriented vertically, to monitor the angular acceleration of the robot to protect against flipping?

[Jared Russell]

Take a look at the documentation for our kit accelerometer.

If set to +/- 2g operation, we're talking a resolution of 4mg (that's milli-g's) per LSB. The noise ceiling is spec'd at worst case +/- <1.5 LSB for the Z axis, <1 LSB for X and Y (at 100Hz data rate = 50Hz bandwidth).

In other words, at a 100Hz data rate, at roll of 5 degrees (0 degrees pitch), we would expect aX to read sin(5 degrees)*1g = 0.0872 g (or 87.2 mg). That's >20 LSBs - significantly above the expected noise margin.

In other words, I think you will find the accelerometer responds EXTREMELY quickly to angular changes. There's a reason they use accelerometers like ours on SUVs to detect roll conditions to deploy airbags.