Abstract
Pheromone-based stigmergic communication is well suited for the coordination of swarm of robots in the exploration of unknown areas. We introduce a guided probabilistic exploration of an unknown environment by combining random movement and stigmergic guidance. Pheromone-based stigmergic communication among simple entities features various complexities that have significant effects on the overall swarm coordination, but are poorly understood. We propose a genetic algorithm for the optimization of parameters related to pheromone-based stigmergic communication. As a result, we achieve human-competitive tuning and obtain a better understanding of these parameters.
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Kuyucu, T., Tanev, I., Shimohara, K. (2012). Evolutionary Optimization of Pheromone-Based Stigmergic Communication. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2012. Lecture Notes in Computer Science, vol 7248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29178-4_7
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DOI: https://doi.org/10.1007/978-3-642-29178-4_7
Publisher Name: Springer, Berlin, Heidelberg
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