*Find out what message is passed when you tap the dogs head for read/set/play
*Start looking for how to change the color tables since that's what Birgit said they
 had trouble with

---

Results:

* I didn't find out the message that is passed or the exact file that contains the code
that deals with this.  However, in the Python code (where most of the Behavior code exists)
I found files pReady.py & pSet.py in the PyCode directory.  These files are called from 
Behaviou.py:

        # Ready state - let me move to my correct position.
        if Global.state == Constant.READYSTATE or Debug.mustReady:
            pReady.readyMatchPlaying()
            hTeam.sendWirelessInfo()

        # Set state - I can't move my legs, but I can move head to look for
        # ball.
        elif Global.state == Constant.SETSTATE:
            pSet.DecideNextAction()
            hTeam.sendWirelessInfo()

        elif Global.state == Constant.FINISHEDSTATE:
            hTeam.sendWirelessInfo()

        else:
            profileFunc(Player.player.DecideNextAction)


The last line will be called if Global.state -- Constant.PLAYINGSTATE.  This probably
calls the DecideNextAction in pGoalie.py & pForward.py.  We should verify this.


* I found this in the Abstract of north-5-subsampled.pdf:

"...previously successful approaches to robot vision systems have focussed on colour recognition,
classification and blobbing, which provide for relatively fast and simple object recognition
algorithms. However, such methods require processing of every pixel in each camera frame, regardless
of its information content, creating a high lower bound on processing time. In addition,
colour-based methods respond poorly to variations in lighting conditions and are plagued with
problems in distinguishing colour blobs representing valid objects from those caused by noise
and background information.

This report moves towards an alternative image processing system for robots based on minimal
sampling of each frame. Interesting features are extracted from information-bearing regions
of each image based on relationships between neighbouring pixels, and object recognition is
performed over these features. This method has many advantages, including efficient implementation
and a reduced dependency on finely tuned colour classifications. These lead to robustness
to changes in lighting conditions and a much reduced calibration time compared with previous
methods. Sub-sampling methods were successfully implemented for the UNSW/NICTA team
rUNSWift, which placed third at the RoboCup1 Legged League2 World Championships in 2005."

This leads me to believe that the codebase we chose from rUNSWift may not need color tables
to be changed because of their approach to vision.  Further investigation is needed.