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Decentralized Evolution of Robotic Behavior Using Finite State Machines

Decentralized Evolution of Robotic Behavior Using Finite State Machines

Veröffentlicht: 2009 Dezember
Erscheinungsort: Bingley, UK
Journal: International Journal of Intelligent Computing and Cybernetics
Nummer: 4
Seiten: 695-723
Verlag: Emerald Group
Volume: 2

Referierte Veröffentlichung


Purpose: In Evolutionary Robotics (ER), robotic control systems are subject to a developmental process inspired by natural evolution. In this article, a control system representation based on Finite State Machines (FSMs) is utilized to build a decentralized online-evolutionary framework for swarms of mobile robots. Design/methodology/approach: A new recombination operator for multi-parental generation of offspring is presented and a known mutation operator is extended to harden parts of genotypes involved in good behavior, thus narrowing down the dimensions of the search space. A storage called Memory Genome for archiving the best genomes of every robot introduces a decentralized elitist strategy. These operators are studied in a factorial set of experiments by evolving two different benchmark behaviors such as Collision Avoidance and Gate Passing on a simulated swarm of robots. A comparison with a related approach is provided. Findings: The framework is capable of robustly evolving the benchmark behaviors. The Memory Genome and the number of parents for reproduction highly influence the quality of the results, the recombination operator leads to an improvement in certain parameter combinations only. Research limitations/implications: Future studies should focus on further improving mutation and recombination. Generality statements should be made by studying more behaviors and there is a need for experimental studies with real robots. Practical implications: The design of decentralized ER frameworks is improved. Originality/value: The framework is robust and has the advantage that the resulting controllers are easier to analyze than in approaches based on Artificial Neural Networks. The findings suggest improvements in the general design of decentralized ER frameworks.

ISSN: 1756-378X
Download: Media:FinalVersion.pdf
Weitere Informationen unter: Link
DOI Link: 10.1108/17563780911005845

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