  Swarm robots, inspired by the emergence of animal herds, are robots that
assemble a large number of modules and self-organize themselves to form
specific morphologies and exhibit specific functions. These modular robots
perform relatively simple actions and controls, and create macroscopic
morphologies and functions through the interaction of a large number of modular
robots. This research focuses on such self-organizing robots or swarm robots.
The proposed algorithm is a model that applies the Turing pattern, one of the
self-organization models, to make a group of modules accumulate and stay within
a certain region. The proposed method utilizes the area within the spots of the
Turing pattern as the aggregation region of the modules. Furthermore, it
considers the value corresponding to the concentration distribution within the
spotted pattern of the Turing pattern model (referred to as the potential value
in this research), identifies the center of the region (spotted pattern), and
makes it the center of the module group. By controlling the modules in the
direction of the higher potential value, it succeeds in maintaining the shape
of the module group as a whole while moving. The algorithm was validated using
a two-dimensional simulation model. The unit module robot was assumed to have
the following properties: 1) limited self-drive, 2) no module identifier, 3)
information exchange only with adjacent modules, 4) no coordinate system, and
5) only simple arithmetic and memory functions. Using these modules, the
devised algorithm was able to achieve not only the creation of static forms but
also the realization of the following movements: 1) modules accumulate and
grow, 2) modules move to the light source, 3) exit the gap while maintaining
its shape, and 4) self-replication.
