A colony of robots using vision sensing and evolved neural controllers
Andrew L. Nelson, Edward Grant, Gregory J. Barlow, and Thomas C. Henderson

This paper describes the development and testing of a new evolutionary
robotics research test bed. The test bed consists of a colony of small
computationally powerful mobile robots that use evolved neural network
controllers and vision based sensors to generate team game-playing
behaviors. The vision based sensors function by converting video images
into range and object color data. Large evolvable neural network
controllers use these sensor data to control mobile robots. The networks
require 150 individual input connections to accommodate the processed
video sensor data. Using evolutionary computing methods, the neural
network based controllers were evolved to play the competitive team game
Capture the Flag with teams of mobile robots. Neural controllers were
evolved in simulation and transferred to real robots for physical
verification. Sensor signals in the simulated environment are formatted to
duplicate the processed real video sensor values rather than the raw video
images. Robot controllers receive sensor signals and send actuator
commands of the same format, whether they are driving physical robots in a
real environment or simulated robots agents in an artificial environment.
Evolved neural controllers can be transferred directly to the real mobile
robots for testing and evaluation. Experimental results generated with
this new evolutionary robotics research test bed are presented.