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-informationbased 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.

Traffic conflict reduction based on distributed stochastic task allocation

“…An important feature of this LICA-based method is that using local information only, and enables an asynchronous implementation. Algorithms for Markov chain synthesis under flux upper limits, and, for local information based quorum model are also reported by Jang et al [30].…”

“…Task allocation is used to drive the speed distribution of vehicles to the desired distribution and maintain it. The methods used for task allocation are based on homogeneous Markov chains (HMC [28]), inhomogeneous Markov chains (IMC [29]) and inhomogeneous Markov chains using local information only (LICA IMC [30]).…”

“…The feedback-based swarm guidance algorithm was further developed Jang et al [30] to function without having to rely on global information or an estimation of the distribution of the entire swarm. This is called Local Information Consistency Assumption (LICA) as opposed to the general Global Information Consistency Assumption (GICA).…”

STRATA: unified framework for task assignments in large teams of heterogeneous agents

“…We note that it is possible to realize a decentralized implementation of the proposed optimization problem in ( 34)-(39). Indeed, prior work has demonstrated that one can compute the state information X locally, and enable online adaptation (re-optimization) of the transition rates as the state information is updated [3,15,24,28]. Our approach, while introducing new capabilities, directly inherits these advantages from prior work.…”

“…Furthermore, the covariance between any two elements of Y is given by Example The expected value of the species-trait matrix and the initial abstract state information of our example problem are defined in ( 6) and ( 7), respectively. Thus, the expected values of the initial trait distribution and the associated variances can be computed using (14) and (15), and are thus given by…”