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Modeling Swarm Systems and Formal Design Methods

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Abstract

We learn about why and how we model systems of swarm robotics and how sophisticated design methods can look like.Modeling is motivated and introduced as a dimension reduction technique for swarm robotics. Then we start from a discussion of local sampling, which is the challenge in swarm robotics of dealing with local information. The local samples are not representative for the whole swarm and hence we require a methodology to work with unreliable local information. Several modeling techniques are introduced that are frequently applied in swarm robotics, such as rate equations and spatial models based on ordinary and partial differential equations. We also discuss network models and the interesting option of using robot swarms as models for biology.

In the second part, we turn to formal design approaches starting from multi-scale modeling. Software engineering approaches and verification techniques are discussed and we look into the so-called concept of global-to-local programming.

That relatively simple algorithm is how my model works, and it manifests itself as complex hunting behavior when scaled up to a swarm of stigmergic agents.

—Daniel Suarez, Kill Decision

Leon and I could try to model a swarm of them electronically. We could give them various characteristics and see how long it takes for them to start acting like a brain.

—Frank Schätzing, The Swarm

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Notes

  1. 1.

    From Sect. 5.2.2 we know that the Binomial proportion 95%-confidence interval is defined by \(\hat {P}\pm 1.96 \sqrt {\frac {1}{n}\hat {P}(1-\hat {P})}\).

  2. 2.

    From Sect. 5.2.2 we know that the true probability is P = 2b/(sπ).

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    Heiko Hamann

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Hamann, H. (2018). Modeling Swarm Systems and Formal Design Methods. In: Swarm Robotics: A Formal Approach. Springer, Cham. https://doi.org/10.1007/978-3-319-74528-2_5

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