[yash101]

I am thinking about the problem for A* a bit more universally. Say, the field was divided into pixels, Maybe 64px long and 32px wide. The depth sensor would basically find the obstacles and mark the pixels they fall under as a place to avoid. The field would be described in an extremely detailed configuration file. Some pixels, like field elements and walls would be dead zones -- navigate away from them. Anything else would be something that can move, be it a gamepiece or another robot. The algorithm could be programmed to have this as a low priority. This means, ram into here if there is no way around or if the way around is too far or impractical. The robot, then, will have the knowledge to navigate around the field with an excessively high accuracy -- higher than a human player could achieve. After the map is generated, the cRIO will be sent a signal to turn -- turn while told to turn. This will get the robot aligned to start. Next, the cRIO will be told to move forward. This turn command and forward command will constantly be sent, sending the robot in the right direction.

As of what everyone is saying, it seems as though a vision program that teaches itself the game would be highly impractical (and impossible for someone at my level).

However, there are a couple AI algorithms that would be lovely, like a robot that uses A* to navigate to a location automatically, or some Machine Learning algorithm to perfect the robot's shots.

Also, JamesBrown, I want to try AI for a few reasons. I want to try something that is challenging, that if I perform well enough, will pay back in the end. AI is something cool. I know many places where some ML/AI would be just awesome, and would increase the reliability of many systems. It is a very low percentage of my interest to do this for bragging rights or for showing off.