A Vision Program that teaches itself the game

[Pault]

Originally Posted by yash101

I have come up with a plan on how to write something like this. The vision program will have a lot of manual set up, like describing the field, the obstacles, goals and boundaries. Other than that, the robot can start it's ml saga for the shooter using human intervention -- it will learn the sweet spots for the shots as the drivers shoot the ball and make/miss it. Over time, the data set will grow and the shots will become more and more accurate, just like our driver's shots.

When we are learning about the robot's capabilities, this is how we learn:
Shoot once. Was it low? Was it high? reposition. Try again.

This would be quite similar to what the Supervised learning algorithm will be. Using regression, the best shot posture will be estimated even if there is no data point. It needs to just know a couple data points for the answer.

Who says that next year is going to be a shooting game? What about the end game, if there is one?

Originally Posted by yash101

However, a gyro will give access to the direction so that the robot can tell which side it's looking at.

The gyro will not give you an accurate heading over the course of the match. A general rule that I have heard is that a good FRC gyro will give you about 15 degrees of drift over the length of a match. My recommendation on this end is to check your angle whenever possible using the vision targets, and when you can't see the vision target, just use the gyro to calculate your deviation from the last time you could. It may even be possible to do the same thing with the roboRIO's 3-axis accelerometer for location.