Title: Bio-Inspired Local Information-Based Control for Probabilistic Swarm Distribution Guidance,
Abstract: This paper addresses a task allocation problem for a large-scale robotic
swarm, namely swarm distribution guidance problem. Unlike most of the existing
frameworks handling this problem, the proposed framework suggests utilising
local information available to generate its time-varying stochastic policies.
As each agent requires only local consistency on information with neighbouring
agents, rather than the global consistency, the proposed framework offers
various advantages, e.g., a shorter timescale for using new information and
potential to incorporate an asynchronous decision-making process. We perform
theoretical analysis on the properties of the proposed framework. From the
analysis, it is proved that the framework can guarantee the convergence to the
desired density distribution even using local information while maintaining
advantages of global-information-based approaches. The design requirements for
these advantages are explicitly listed in this paper. This paper also provides
specific examples of how to implement the framework developed. The results of
numerical experiments confirm the effectiveness and comparability of the
proposed framework, compared with the global-information-based framework.