研究机器人集群相遇时的信息传播机制，揭示无预设会面下的扩散规律。
A Micro-Macro Model of Encounter-Driven Information Diffusion in Robot Swarms
- 基于平均自由路径思想构建微观与宏观两级模型，模拟随机相遇下的信息传播。
- 仿真验证了群组规模、通信范围和环境大小对信息扩散速度的影响。
- 为无调度会面场景下的机器人信息路由算法设计提供理论依据，适合分布式系统研究者。
本文提出相遇驱动信息扩散（EDID）问题：机器人仅在相遇时交换信息，且无法预知何时何地与谁相遇。为设计支持EDID的存储与路由算法，本文从基本原理出发，构建了包含微观模型（基于平均自由路径的推广）和宏观模型（捕捉全局扩散动态）的双层信息扩散模型。通过大规模机器人仿真，考察了群组规模、通信范围、环境尺寸及不同随机运动模式的影响。结果表明该模型能准确刻画信息传播特性，并为根据关键参数优化扩散算法提供理论支撑。
原文摘要 · Abstract (English)
In this paper, we propose the problem of Encounter-Driven Information Diffusion (EDID). In EDID, robots are allowed to exchange information only upon meeting. Crucially, EDID assumes that the robots are not allowed to schedule their meetings. As such, the robots have no means to anticipate when, where, and who they will meet. As a step towards the design of storage and routing algorithms for EDID, in this paper we propose a model of information diffusion that captures the essential dynamics of EDID. The model is derived from first principles and is composed of two levels: a micro model, based on a generalization of the concept of `mean free path'; and a macro model, which captures the global dynamics of information diffusion. We validate the model through extensive robot simulations, in which we consider swarm size, communication range, environment size, and different random motion regimes. We conclude the paper with a discussion of the implications of this model on the algorithms that best support information diffusion according to the parameters of interest.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。