Abstract
Flocking is an archetype emergent behavior that is displayed by a wide variety of groups and has been extensively studied in both biological and robotic communities. Still today, the exact requirements on the detail and type of information required for the production of flocking motion is unclear; moreover, these requirements have large potential impacts on biological plausibility and robotic implementations. This work implements a previously published flocking algorithm (Local Crowed Horizon) on a robotic platform and in computer simulations to explore the effects that the type and detail of information have on the produced motions. Specifically, we investigate the level of detail needed for the observation of flock members and study the differences between the use of pose and bearing information. Surprisingly, the results show that there is no significant difference in the motions produce by any observation detail or type of information. From the results, we introduce and define information-abstracted flocking algorithms, which are structured in such a way that the rule is agnostic to the observation detail and/or type of information given as input. Moreover, we believe our implementation of the Local Crowded Horizon flocking algorithm produces motions that require the least and most simplistic type of information (bearing only) which has been validated on robotic hardware to date.
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Fine, B.T., Shell, D.A. (2012). Examining the Information Requirements for Flocking Motion. In: Ziemke, T., Balkenius, C., Hallam, J. (eds) From Animals to Animats 12. SAB 2012. Lecture Notes in Computer Science(), vol 7426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33093-3_44
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DOI: https://doi.org/10.1007/978-3-642-33093-3_44
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