Analysis of implicit control structure in object clustering phenomena

Living things exhibit adaptive and supple locomotion under the real world characterized by rapid changes, high uncertainty, and limited availability of information. But the systematic design of the behavior of living things like ants or bees (e.g. constructing very big and complicated nests) whose brains have very tiny memory abilities, has not been well established. In recent research, this design principle is considered to come from the interaction among the mechanical system (i.e. body), the control law (i.e. brain), and the environment (i.e. real world). To understand these principles with interaction, we propose on Implicit Control Law, which reflects the interaction among the body, the brain, and the environment. In this research, some simple robots named Swiss Robot, Aggregator Robot, Coronoc Robot, are focused. These robots show us interesting object clustering behaviors even if each robot is equipped with simple Explicit Control Law or no Explicit Control Law. From the systematic analyses of clustering behaviors, each Implicit Control Law is formulated. Though the Explicit Control Law and mechanical structures of each robot are exactly different, we can see common parts (principles) of Implicit Control Law. Furthermore, we demonstrate the correspondance between the Explicit Control Law and its clustering ability of each robot (e.g. number of clusters, clustering position).