[Alex.Norton]

The biggest problem that I can see here (assuming all of the sensor, code, etc...) works perfectly is the development of an Artificial Intelligence. How does the machine decide when it is prudent to go for a game piece, heck how do you choose to go left/right at any given moment.

The AIs that I have worked with in the past used hundreds of runs to get good predictive and training data. How do you get your machine this type of data against a good opponent? Getting a machine that can run around an empty field is one problem. Getting a machine that can play against even another AI is extremely difficult. As an example I'm currently working on a biomimetic vision system that will runs of data sets well in excess of 10,000 images and despite this it can still only tell the difference between a zebra and a car 80% of the time.

The next major hurdle that I can on the horizon is processing power. From this discussion it would seems that most people want to bet on the camera for most of their more complex sensing needs. Doing this will involve object recognition (ball, other bots) integrated with position control to accurately place the robot on the field. Doing real time image processing (I haven't seen the NI libraries for this so I don't know what functions they do have) that must process and recognize objects takes a amazing amount of processing power. Power that I'm sure the cRio doesn't have, especially considering how much I've heard about the camera slowing the robot down too much.

If somebody has a proposal for how to solve the processing problem I would be happy to work on a project like this. Sadly I don't have access to a robot so I would need to work more from the strategy end. If somebody feels like organizing this PM me.