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This paper addresses a task allocation problem for a large-scale roboticswarm, namely swarm distribution guidance problem. Unlike most of the existingframeworks handling this problem, the proposed framework suggests utilisinglocal information available to generate its time-varying stochastic policies.As each agent requires only local consistency on information with neighbouringagents, rather than the global consistency, the proposed framework offersvarious advantages, e.g., a shorter timescale for using new information andpotential to incorporate an asynchronous decision-making process. We performtheoretical analysis on the properties of the proposed framework. From theanalysis, it is proved that the framework can guarantee the convergence to thedesired density distribution even using local information while maintainingadvantages of global-information-based approaches. The design requirements forthese advantages are explicitly listed in this paper. This paper also providesspecific examples of how to implement the framework developed. The results ofnumerical experiments confirm the effectiveness and comparability of theproposed framework, compared with the global-information-based framework.

Bio-Inspired Local Information-Based Control for Probabilistic Swarm Distribution Guidance