GESwarm: grammatical evolution for the automatic synthesis of collective behaviors in swarm robotics

  • Authors:

  • Eliseo Ferrante;Edgar Duéñez-Guzmán;Ali Emre Turgut;Tom Wenseleers

  • Affiliations:

  • KULeuven, Leuven, Belgium;KULeuven, Leuven, Belgium;THK University, Ankara, Turkey;KULeuven, Leuven, Belgium

  • Venue:
  • Proceedings of the 15th annual conference on Genetic and evolutionary computation
  • Year:
  • 2013

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Abstract

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.