Difference between revisions of "Watermelon"
(Lots of changes, new version!) |
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| author = [[User:Synapse|Synapse]] | | author = [[User:Synapse|Synapse]] | ||
| extends = [[AdvancedRobot]] | | extends = [[AdvancedRobot]] | ||
| − | | targeting = [[GuessFactor Targeting (traditional)]] | + | | targeting = [[GuessFactor Targeting (traditional)]] |
| − | | movement = [[Wave Surfing]] | + | | movement = [[Wave Surfing]] |
| released = 12 June 2009 | | released = 12 June 2009 | ||
| current_version = 6 | | current_version = 6 | ||
Revision as of 08:59, 12 June 2009
| Watermelon | |
 | |
| Author(s) | Synapse |
| Extends | AdvancedRobot |
| Targeting | GuessFactor Targeting (traditional) |
| Movement | Wave Surfing |
| Released | 12 June 2009 |
| Current Version | 6 |
| Download | |
Contents
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 as straightforward a manner as I can manage. 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.
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 on every combination of velocity, acceleration, time since last reversal, and lateral wall-distance. Segments are chosen based on the ratio of their maximum value to their average value, except if they have too few visits.
A rolling average ensures that the bot accomodates adaptive targeters.
Firing
Guess Factor gun, segmented on every combination of velocity, acceleration, distance, and lateral wall-distance. Segments are chosen based on the ratio of their maximum value to their average value, except if they have too few hits.
A rolling average ensures that the bot accomodates adaptive targeters.
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
Branched Surfing
Currently the danger from the second-soonest-arriving wave's danger is added proportionally to the current wave's danger, weighted by arrival proximity and by bullet power. I'd like to try branching, which could enable me to find a safer location on both of the next two waves.
