Gyro vs. Encoders for Driving Straight

[gerthworm]

Gyro for keeping straight.

As to autonomous stuff, seems like more and more teams are going to a path-planner solution. Before autonomous starts, a set of x/y coordinate waypoints are fed into the algorithm. An output is generated that indicates varying desired velocities for each side of the drivetrain over the time duration of the path traversal.

In this case, each side of the drivetrain has an encoder feeding a PID to control the actual wheel velocity to the desired velocity (from the path-planner). To augment this, a gyro may be used to provide a "correction factor", which reduces error from wheel scrub or going over rough defenses.

Net result in 2016 was a ~50% accurate high-goal autonomous routine under the low bar. Not as good as some of the really slick vision systems, but we're still pretty proud of it...

[AustinSchuh]

Originally Posted by gerthworm

As to autonomous stuff, seems like more and more teams are going to a path-planner solution. Before autonomous starts, a set of x/y coordinate waypoints are fed into the algorithm. An output is generated that indicates varying desired velocities for each side of the drivetrain over the time duration of the path traversal.

There are a couple teams doing this (and yours may be one of them), but I'd like to emphasize that you don't need a path planner (and all the complexity that comes with one) to do really cool autonomous modes. 971's autonomous modes do not have a planner involved, and they are plenty complicated. We have trapezoidal motion profiles in distance and theta hand tuned, and that's enough to do some pretty neat stuff.

There are places where a real planner with feedback would do better than simple profiles, but you don't *need* them in FRC.

Originally Posted by gerthworm

In this case, each side of the drivetrain has an encoder feeding a PID to control the actual wheel velocity to the desired velocity (from the path-planner). To augment this, a gyro may be used to provide a "correction factor", which reduces error from wheel scrub or going over rough defenses.

Net result in 2016 was a ~50% accurate high-goal autonomous routine under the low bar. Not as good as some of the really slick vision systems, but we're still pretty proud of it...

Nice!

Until we had vision working, we scored 5+ 1 ball under the low goal autonomous modes with 100% reliability at Davis with motion profiles in theta and distance, good control loops, and very careful alignment. It's all about staying away from traction limits and designing nice control loops which track well. We use a gyro for heading and encoders for distance, which works great.

Keep it as simple as possible while still achieving your goals. (I didn't mean to pick on you in particular, and we are definitely a team which pushes the limits, but I want to emphasize KISS. It's better to shoot lower and succeed than bite off too much. Especially given the level I'm reading the OP to be at.)