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.

翻译：本文提出了遭遇驱动信息扩散问题。在EDID中，机器人仅允许在相遇时交换信息。关键的是，EDID假设机器人不允许调度其相遇事件。因此，机器人无法预知何时、何地以及将与谁相遇。作为EDID存储与路由算法设计的基础步骤，本文提出了一种捕捉EDID核心动态的信息扩散模型。该模型基于基本原理构建，包含两个层次：基于"平均自由程"概念泛化的微观模型，以及捕捉信息扩散全局动态的宏观模型。我们通过大量机器人仿真验证了该模型，其中考虑了群体规模、通信范围、环境尺寸及不同的随机运动机制。最后，本文讨论了该模型对如何根据关键参数优化信息扩散算法的影响。