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Probabilistic Control and Swarm Dynamics in Mobile Robots and Ants

Probabilistic Control and Swarm Dynamics in Mobile Robots and Ants

Eugene Kagan, Alexander Rybalov, Alon Sela, Hava Siegelmann, Jennie Steshenko
Copyright: © 2014 |Pages: 37
ISBN13: 9781466660786|ISBN10: 1466660783|EISBN13: 9781466660793
DOI: 10.4018/978-1-4666-6078-6.ch002
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MLA

Kagan, Eugene, et al. "Probabilistic Control and Swarm Dynamics in Mobile Robots and Ants." Biologically-Inspired Techniques for Knowledge Discovery and Data Mining, edited by Shafiq Alam, et al., IGI Global, 2014, pp. 11-47. https://doi.org/10.4018/978-1-4666-6078-6.ch002

APA

Kagan, E., Rybalov, A., Sela, A., Siegelmann, H., & Steshenko, J. (2014). Probabilistic Control and Swarm Dynamics in Mobile Robots and Ants. In S. Alam, G. Dobbie, Y. Koh, & S. ur Rehman (Eds.), Biologically-Inspired Techniques for Knowledge Discovery and Data Mining (pp. 11-47). IGI Global. https://doi.org/10.4018/978-1-4666-6078-6.ch002

Chicago

Kagan, Eugene, et al. "Probabilistic Control and Swarm Dynamics in Mobile Robots and Ants." In Biologically-Inspired Techniques for Knowledge Discovery and Data Mining, edited by Shafiq Alam, et al., 11-47. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-6078-6.ch002

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Abstract

The chapter considers the method of probabilistic control of mobile robots navigating in random environments and mimicking the foraging activity of ants, which is widely accepted as optimal with respect to the environmental conditions. The control is based on the Tsetlin automaton, which is a minimal automaton demonstrating an expedient behavior in random environments. The suggested automaton implements probability-based aggregators, which form a complete algebraic system and support an activity of the automaton over non-Boolean variables. The considered mobile agents are based on the Braitenberg vehicles equipped with four types of sensors, which mimic the basic sensing abilities of ants: short- and long-distance sensing of environmental states, sensing of neighboring agents, and sensing the pheromone traces. Numerical simulations demonstrate that the foraging behavior of the suggested mobile agents, running both individually and in groups, is statistically indistinguishable from the foraging behavior of real ants observed in laboratory experiments.

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