[davidthefat]

Originally Posted by Tristan Lall

I'm actually more interested in what margin of error is acceptable for the officials' decisions—and that's kind of what I hoped the thread was about when I saw it in the portal—but that's a subject for another thread.

What's the second sensor for the Kalman filter? Even without knowing the plan, given that many teams do without even PID, my educated guess would be that you can get away without it—so it's not "necessary". Are you planning to use this for autonomous modes, or just for analyzing robot motion after the fact? How accurate are your measurements, anyway? What are you using as the baseline? Have you considered how much error is inherent to backlash in the drivetrain, for example?

The real answer is that it depends. You're going to need to define what's important to you, and assess it in that framework. Do some failure mode analysis: if the error on the encoder is 3%, and given certain other assumptions about the robot, what are the effects? Are those tolerable to you?

Another part of the analysis concerns how difficult it is for you to acquire or implement a suitable Kalman filter. Are you good at coding? Do you understand the principle of operation? What other things could you be doing instead of implementing a Kalman filter?

It is mainly for autonomous mode. The second sensor would most likely be a gyro. The error is mostly due to mechanical issues. The chains jiggle a lot so that translates into noise in the readings. My mentor has the mentality that all software can eliminate mechanical issues. And he kind of already has put all his eggs into the software basket this year. I've already written most of the relating to the drive train for this year... Coding is not the issue; the real issue is properly understanding and implementing the filter. As far as I know, that only requires some linear algebra and a lot of reading up on it. I spent the last 2-3 days just reading up on it.

I really do not have much to do other than implement that filter and tuning the PID constants. Perhaps I can be doing my winter break homework

Originally Posted by Jared341

Is the encoder on a driven or non-driven wheel?

Non-driven wheels ("follower wheels", typically using a primitive suspension and an omniwheel) will just about always give you better encoder data than powered wheels simply because wheel slip is not an issue. I would try using a non-driven wheel before attempting something like a Kalman filter.

All our wheels are driven (WCD)