Abstract:
Inspired by the chemotaxis interaction of living cells, we have developed
an agent-based approach for self-organizing shape formation. Since all our
simulations begin with a different uniform random configuration and our
agents move stochastically, it has been observed that the self-organization
process may form two or more stable final configurations. These differing
configurations may be characterized via statistical moments of the agents'
locations. In order to direct the agents to robustly form one specific
configuration, we generate biased initial conditions whose statistical
moments are related to moments of the desired configuration. With this
approach, we are able to successfully direct the aggregating swarms to
produced a desired macroscopic shape, starting from randomized initial
conditions with controlled statistical properties.
