First-order agent-based models of emergent behaviour of Dictyostelium discoideum and their inspiration for swarm robotics

Abstract

Collective behaviour in nature provides a source of inspiration to engineer artificial collective adaptive systems, due to their mechanisms favouring adaptation to environmental changes and enabling complex emergent behaviour to arise from a relatively simple behaviour of individual entities. As part of our ongoing research, we study the social amoeba Dictyostelium discoideum to derive agent-based models and mechanisms that we can then exploit in artificial systems, in particular in swarm robotics. In this paper, we present a selection of agent-based models of the aggregation phase of D. discoideum, their corresponding biological illustrations and how we used them as an inspiration for transposing this behaviour into swarms of Kilobots. We focus on the stream-breaking phenomenon occurring during the aggregation phase of the life cycle of D. discoideum. Results show that the breakup of aggregation streams depends on cell density, motility, motive force and the concentration of cAMP and CF. The breakup also comes with the appearance of late centres. Our computational results show similar behaviour to our biological experiments, using Ax2(ka) strain. For swarm robotics experiments, we focus on signalling and aggregation towards a centre.