Demand for autonomous swarms, where robots can cooperate with each other without human intervention, is set to grow rapidly in the near future. Currently, one of the main challenges in swarm robotics is understanding how the behaviour of individual robots leads to an observed emergent collective performance. In this paper, a novel approach to understanding robot swarms that perform foraging is proposed in the form of the Information-Cost-Reward (ICR) framework. The framework relates the way in which robots obtain and share information (about where work needs to be done) to the swarm’s ability to exploit that information in order to obtain reward efficiently in the context of a particular task and environment. The ICR framework can be applied to analyse underlying mechanisms that lead to observed swarm performance, as well as to inform hypotheses about the suitability of a particular robot control strategy for new swarm missions. Additionally, the information-centred understanding that the framework offers paves a way towards a new swarm design methodology where general principles of collective robot behaviour guide algorithm design.

中文翻译：

---

理解机器人群觅食的信息成本奖励框架

在不久的将来，对自动群体的需求将迅速增长，在这种群体中，机器人无需人工干预即可相互协作。当前，群体机器人技术的主要挑战之一是了解单个机器人的行为如何导致观察到的紧急集体性能。在本文中，以信息成本报酬（ICR）框架的形式提出了一种新颖的方法来理解执行觅食的机器人群。该框架将机器人获取和共享信息的方式（需要在何处完成工作）与群体利用该信息以便在特定任务和环境中有效获得奖励的能力联系起来。ICR框架可用于分析导致观察到的群性能的潜在机制，并告知有关特定机器人控制策略是否适合新群体任务的假设。此外，该框架提供的以信息为中心的理解为一种新的群体设计方法铺平了道路，该方法采用集体机器人行为的一般原则指导算法设计。