I'm not fully understanding what you mean
AI is about intelligence. Most robots are not very intelligent.
Actually, AI today seems to be more focused on statistics and probabilities than anything else.
For the OP: If you are serious about this project, you'll want to take a few online (but free) classes. They will teach you a lot; also - if your knowledge/understanding of probabilities, statistics, and linear algebra is lacking, you'll want to brush up on those, first - ask me how I know...
Anyhow - here are the classes:http://www.udacity.com/course/st101
- Intro to Statshttp://www.udacity.com/course/cs271
- Intro to AIhttp://www.udacity.com/course/cs373
- AI for Roboticshttps://www.coursera.org/course/ml
- Machine Learning
You might also check out some of the other offerings; there are many other classes which could be beneficial from a robotics and AI/ML standpoint.
The "CS271 - Intro to AI" is a recent addition to Udacity's lineup; it was only recently announced. It, along with the Machine Learning course from Coursera, were the two original "free online" courses brought out by Stanford last fall/winter of 2011. I took both, but I ended up having to drop out of the AI class about halfway through due to personal issues related to my employment. I went on to complete the ML class, then earlier this year (spring 2012) I took the "CS373 - AI for Robotics" class from Udacity (Coursera and Udacity both were spinoffs that grew out of those original two Stanford classes; this was done so as not to dilute the Stanford brand and recognition, while being able to bring more courses and such to a wider audience).
I found the CS373 class to be very challenging - you basically learn enough in it to "build" (well, program) a simplified version of a self-driving, path-planning robotic vehicle, much like Google's self-driving car (take a guess as to who Prof Sebastian Thrun is, and what his relationship is to robotics and Google's car)...
The "Intro to AI" and "Machine Learning" courses are more generalized; geared toward a broad overview and application of AI/ML algorithms to computing problems in general - but I found them very interesting, and one guy in the ML course created this based on his learning and study:http://blog.davidsingleton.org/nnrccar
...so you can see that all of these courses have application and knowledge which can transfer to robotics, path-planning, navigation, etc.
I'm personally glad that Udacity brought back the "Intro to AI" course; many people were wanting it back, and when I had to drop out, it was during a very difficult time for me, and I hated having to do it. I intend to go back and take the course again, as I found it very instructive and informative (and it wasn't easy, either - I'm also going to take that intro to stats course as well - if there is one thing these courses have taught me, it's that I am very lacking in my understanding of statistics and probabilities, and understanding of both are needed for modern AI/ML algorithm implementations).
Oh - one other thing - that CS373 course offers one of the best explanations of the PID algorithm I have ever seen; you go from zero understanding of PID, to implementing the P, then the D, then the I (they do it this way to prove a point about the algorithm) - and having a real solid understanding of it at the end. The coding was done in Python (whereas the coding for the ML class was done in Octave/MatLab - which is a very cool language all on its own, but takes a bit to wrap your head around the primitives of vectors and matrices - but once you understand how to vectorize an algorithm, the possibility of immense processing power understanding is unleashed; if only I had access to a Beowulf cluster, or a nVidia Tesla machine!)...