[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.

The main code change that will need to be done is that the retroreflective targets will need to be coded in. It will be extremely difficult to write a program to find the targets and always find the correct one for pose estimation using ML.

Basically, a great portion of the game can be taught to the robot quite easily -- moving around the field, etc. However, as you said, it is quite hard to gather the robot location on the field. However, a gyro will give access to the direction so that the robot can tell which side it's looking at.

The pathfinding will be implemented almost exactly as if the program were a videogame!

I'm not trying to make a fully-autonomous robot, but instead a robot that has the level of AI/ML to assist the drivers and make gameplay more efficient.

I am thinking about using A* quite a bit. When the robot is stationary, a path plan would constantly be generated to keep the robot from moving without brakes, etc. However, that is just a might because that would create quite a bit of lag in the robot motion when a driver wants to run the bot.