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GESwarm Grammatical Evolution for the Automatic Synthesis of Collective Behaviors in Swarm Robotics
Date
2013-07-10
Author
Ferrante, Eliseo
Turgut, Ali Emre
DuenezGuzman, Edgar
Wenseleers, Tom
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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In this paper we propose GESwarm, a novel tool that can automatically synthesize collective behaviors for swarms of autonomous robots through evolutionary robotics. Evolutionary robotics typically relies on artificial evolution for tuning the weights of an artificial neural network that is then used as individual behavior representation. The main caveat of neural networks is that they are very difficult to reverse engineer, meaning that once a suitable solution is found, it is very difficult to analyze, to modify, and to tease apart the inherent principles that lead to the desired collective behavior. In contrast, our representation is based on completely readable and analyzable individual-level rules that lead to a desired collective behavior. The core of our method is a grammar that can generate a rich variety of collective behaviors. We test GESwarm by evolving a foraging strategy using a realistic swarm robotics simulator. We then systematically compare the evolved collective behavior against an hand-coded one for performance, scalability and flexibility, showing that collective behaviors evolved with GESwarm can outperform the hand-coded one.
Subject Keywords
swarm robotics
,
evolutionary robotics
,
genetic programming
URI
https://hdl.handle.net/11511/68896
DOI
https://doi.org/10.1145/2463372.2463385
Collections
Department of Mechanical Engineering, Conference / Seminar