This paper proposes a closed-loop decentralised framework for swarm distribution guidance, which disperses homogeneous agents over bins to achieve a desired density distribution by using feedback gains from the current swarm status. The key difference from existing works is that the proposed framework utilises only local information, not global information, to generate the feedback gains for stochastic policies. Dependency on local information entails various advantages including reduced inter-agent communication, a shorter timescale for obtaining new information, asynchronous implementation, and deployability without a priori mission knowledge. Our theoretical analysis shows that, even utilising only local information, the proposed framework guarantees convergence of the agents to the desired status, while maintaining the advantages of existing closed-loop approaches. Also, the analysis explicitly provides the design requirements to achieve all the advantages of the proposed framework. We provide implementation examples and report the results of empirical tests. The test results confirm the effectiveness of the proposed framework and also validate the robustness enhancement in a scenario of partial disconnection of the communication network.

中文翻译：

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基于本地信息的概率群控制指南

本文提出了一种用于群体分布指导的闭环分散框架，该框架通过利用当前群体状态的反馈增益，将均质剂分散在垃圾箱上，以实现所需的密度分布。与现有工作的主要区别在于，提议的框架仅利用本地信息，而不利用全球信息，以产生随机策略的反馈收益。对本地信息的依赖具有各种优势，包括减少代理之间的通信，缩短获取新信息的时间范围，异步实施以及无需先验任务知识的可部署性。我们的理论分析表明，即使仅利用本地信息，所提出的框架也可以确保将代理聚合到所需状态，同时保持现有闭环方法的优势。同样，分析明确提供了实现所提出框架的所有优点的设计要求。我们提供了实施示例并报告了实证测试的结果。