A recent test run provided the following speed data from the wheel encoders, which digitize position at regular intervals.
Here's a detail view of one of the plots.
|Detail speed plot showing quantization error|
First I tried filtering the data but still ended up with a bunch of spikes. Before I could filter out the noise, I supposed that I should learn about the nature of the noise.
|Filtered speed, green, shows spikes|
|Picture of Pokey showing quantization error|
Dither adds random noise to each pixel, dissipating the quantization error effect. At least to our eyes. Here's the plot with some noise added.
That doesn't help the robot much. Our eyes/brains do quite a bit of noise filtering. One of the AVC entrants pointed me to the double exponential filter, basically a super fancy moving average. I opted to try a less fancy exponential filter (the green line below).
|Dithered speed, blue, and exponential filtering, green|
It's still not glass smooth, but it doesn't need to be. More smoothing means greater lag between the filtered signal and the real one. But clearly the filtered plot is much better; as with the picture above, the effective resolution of the signal is higher.
Is this filtering necessary? I don't know but I am considering some navigation-related calculations that depend on speed (and distance) to improve navigational accuracy. It seems to me that improved resolution will reduce error in these calculations. If it's simple to implement in the real world, I may just go ahead and do it.
If you need a faster response, you might also try a 2nd order filter, which uses the past *two* values rather than just the immediate past value. A 1st order filter will never overshoot, but will always lag. A 2nd order filter can react more quickly to changes (but you'll want your noise to be short spikes, don't want to overreact to them), but can overshoot. If you think of a step function input from 0 to 1, the first order filter will look like a simple low pass filter, ramping up to 1, whereas a 2nd order filter will initially overshoot the top of the input, and have a dampled oscillation down to the steady value of 1.ReplyDelete
double exp filter will actually remove some of the lag because it takes into consideration the trend of the data. Instead of just being a moving avg, its makes a prediction based on the avg. + the trend.ReplyDelete