Conference Paper

A cellular model of swarm intelligence in bees and robots

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... To analyze the behavior of the bees in this mixed society we use a simple cellular-automaton inspired by the BEECLUST algorithm [16] (Fig. 1c), an algorithmic honeybee behavioral model that was first introduced in [17]. We implement autonomous control and close the loop between the animals and the robots. ...
... Here we show an updated version of our two-dimensional cellular model, first introduced in [17]. In contrast to the original model we here introduce immobile robots, the CASUs, as additional agents to the model: Each CASU can autonomously regulate its own temperature in the model cells around it as a reaction of sensing local cells filled with bees. ...
... The model includes three free parameters (α, a parameter that influences the random walk behavior of the agents, σ, a parameter that represents the social influence of neighboring bees and ᴪ, a parameter that represents the probability that a bee switches into resting mode). Since α has already been extensively treated in [17], this section focuses on the newly introduced parameter ᴪ and its influence on σ. Fig. 3a and 3c reacted according to the positive feedback loop, resulting in a winning CASU (warmer, surrounded by bees) and a losing CASU (colder, surrounded by almost no bees). ...
... On the other hand, even such an extended model can still exhibit a low variance in its predictions, due to the implicit base assumptions of ODE models in principle, such as the assumption of optimal mixing and distribution of the modelled agents in space within the areas modelled by each system variable. In this case, a step to spatially explicit individual-based models and spatially more heterogeneous models, like cellular automata (Szopek et al., 2017) or multi-agent models (Stefanec et al., 2017b), might be more suitable to capture the effects of higher variances that are often observed in natural, and thus physically manifested, systems. ...
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... BEECLUST [4] has been used in different research studies with real-and simulatedrobots. Follow up studies on BEECLUST focused on: i) derivation of aggregation model based on the systematic honeybee experiments [13], [14], [15], ii) modification of parameters of BEECLUST to improve the performance [16], iii) macroscopic modelling of the aggregation [17], [18], iv) fuzzy-based decisioning [19], v) heterogeneity in behaviours [20], and vi) recently a bio-hybrid society of robots and honeybees [21], [22]. ...
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