First, download the starter code onto your laptop from this link (we will get Git up and running shortly, but for now, a zipfile will do). In order to get the code running, you will need an installation of Python, along with these packages:
On Ubuntu linux, you should be able to type something like this at the command prompt to install them:
sudo apt-get install python-numpy python-imaging python-opengl
If you have a Mac with MacPorts installed, you can instead run:
sudo port install py27-numpy py27-pil py27-opengl py27-opengl-accelerate sudo port select --set python python27
To be perfectly honest, I'm not sure what the best way to get these packages installed on Windows is. Let me know if you're having trouble doing so.
To run the software, go into the starter directory, and run any of the scripts there. I'd suggest running them in the following order:
The supporting code lives in the common directory. The code is actually fairly well commented, and the supporting modules even have pydoc compatible documentation for easy browsing. Take some time to read over the code and play with each of the demo scripts.
This is an open-ended task. Your main goal should be to understand how RRT's work, and to apply them in practice to solve a problem of your own design. Ideas to get you started:
Produce a webpage demonstrating your planner. Make at least one movie of a successful plan, and describe how often it succeeds and fails (visualations of the search tree can be very helpful here). Did you have to tweak a lot of parameters to get it to work? Would it still work well if you changed the problem slightly?
We will review all of the assignments in class on Monday the 28th. Be prepared to demonstrate your software in action.