Robot Paintings Evolved Using Simulated Robots EvoMUSART ‘06 Gary R. Greenfield University of Richmond, USA Outline Motivation Background S-Robots Evolutionary Framework Assessment Parameters Evolved S-Robot Paintings On Autonomous Evaluation Conclusions Motivation
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
Robot Paintings Evolved Using Simulated Robots
Gary R. Greenfield
University of Richmond, USA
“Artistic talent is far from a magic indefinable essence, possessed by a few and jinxed by deconstruction. Rather it can be thought of as an adaptive system, consisting of a particular updating scheme and low level local rules or techniques, which have been arrived at through an evolutionary process.”
-- Katie Bentley, GA’02 Generative Art Conference, Exploring aesthetic pattern formation, pp. 201-213.
-- V. Ramos and F. Almeida (2000), Artificial ant colonies in digital image habitats – a mass behavior effect study on pattern recognition.
-- L. Moura and H. Pereira (2002), Artistic Swarm Robots (ArtSBot).
-- N. Monmarche et al (2003), Interactive evolution of ant colony paintings.
-- G. Greenfield (2005), Evolutionary methods for ant colony paintings.
(Binary valued) proximity sensor
(Tristimulus) color sensor
Notes: Discrete event simulation determines number of time steps needed when trying to complete a move or when trying to complete a swivel.
if (sensed red component == target value)
qzigzag(q); /* schedule “zigzag” motif */
q.put(SWI), q.put(20); q.put(PDN), q.put(P1);
q.put(SPD), q.put(750); q.put(MOV), q.put(12);
q.put(SWI), q.put(-10); q.put(PUP), q.put(P1)