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Nature is an inhabitant for enormous number of species. All the species do perform complex activities with simple and elegant rules for their survival. The property of emergence of collective behavior is remarkably supporting their activities. One form of the collective behaviour is the swarm intelligence -- all agents poses same rules and capabilities. This equality along with local cooperation in the agents tremendously leads to achieving global results. Some of the swarm behaviours in the nature includes birds formations , fish school maneuverings, ants movement. Recently, one school of research has studied these behaviours and proposed artificial paradigms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Glowworm Swarm Optimization (GSO) etc. Another school of research used these models and designed robotic platforms to detect (locate) multiple signal sources such as light, fire, plume, odour etc. Kinbots platform is one such recent experiment. In the same line of thought, this extended abstract presents the recently proposed butterfly inspired metaphor and corresponding simulations, ongoing experiments with outcomes., Comment: 2 pages, 3 figures, Accepted as Late Breaking Report at ICRA 2017

In biological species, cooperative social life and swarm intelligence have helped them to their survival sustainable growths. This paper considers one such recent inspiration model from butterflies so called Butterfly Mating Optimization (BMO), a meta-butterfly model preferred to capture all local-optima of multimodal functions simultaneously. This paper presents the design and architecture of Bflybots, a multi-mobile robot platform to the requirements of Bflies specified in the BMO algorithm. The multi-Bflybot swarm is designed to acts like butterflies in nature and follow the rules of algorithm. The practical experiments are conducted and the various results are presented. Finally, challenges that are faced and future aspects are initiated for further validation of the algorithm for other signal source localization.