Abstract
This chapter provides on overview on probabilistic modeling of swarming systems. We first show how population dynamics models can be derived from the master equation in physics. We then present models with increasing complexity and with varying degrees of spatial dynamics. We will first introduce a model for collaboration and show how macroscopic models can be used to derive optimal policies for the individual robot analytically. We then introduce two models for collective decisions; first modeling spatiality implicitly by tracking the number of robots at specific sites and then explicitly using a Fokker–Planck equation. The chapter is concluded with open challenges in combining non-spatial with spatial probabilistic modeling techniques.
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Abbreviations
- PDE:
-
partial differential equation
- SDE:
-
stochastic differential equation
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Correll, N., Hamann, H. (2015). Probabilistic Modeling of Swarming Systems. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_74
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DOI: https://doi.org/10.1007/978-3-662-43505-2_74
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