Watermelon

/Development

Watermelon
Author(s)	Synapse
Extends	AdvancedRobot
Targeting	GuessFactor Targeting (traditional)
Movement	Wave Surfing
Released	4 June 2009
Current Version	5
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Structure

It's built on a modular framework, coded cleanly. If I ever retire this bot I might release the source to the community. Occasionally I'll post snippets for free - see Watermelon/Code. Currently developing in Eclipse. Early on I used an educational IDE called BlueJ (its interface is a UML diagram, very pretty). I test by running battles against a few favorites, including CigaretBH and some others. For more specific testing I post a release to RoboRumble and see what happens.

Movement

Uses Wave Surfing, implemented in a straightforward manner. To the extent that that's possible _{grumble grumble}. I used Simonton's non-iterative Wall Smoothing code, and my own future position prediction. It took forever to realize that the future prediction needs to call the wall smoothing function for each imaginary tick, but I finally got it.

It kind of uses the Musashi Trick, by seeding the bins with a single hit at Guess Factor 0. The magnitude of the initial seed is such that the first real hit overrides it. Actual hits are smoothed, with a rolling average preferring new data to old.

Bot width is considered, averaging the danger from covered bins. There's no dive-in protection; bot width takes care of that.

Guess Factors are segmented by velocity and acceleration.

Firing

Guess Factor gun, segmented on velocity and acceleration. Up next: Automatic Segmentation.

Radar

Uses the same radar in melee and in 1v1 conflicts - turns the radar just past the furthest angle that the least recently seen bot could have reached since it was last spotted.

Debug Graphics

Paints waves for enemy bullets and its own bullets, with brighter segments where the bins are fuller. Also marks precisely predicted future positions.

What I'm Working On

Automatic Segmentation

I'm converting both the gun and the movement to automatically select a segmentation, based respectively on the following:

• whether that segmentation has enough data to be useful
• how many axes it involves
• how spiky it is (ratio of maximum to average)

The general strategy behind this is outlined on the old wiki at AutomatedSegmentation.

Saving Data

This one's a tough one - with the sheer quantity of bots in the rumble, and stored data stops being portable pretty quickly. I think the best approach may be just to dynamically alter the rolling average and not save data at all.