Rausch Ilja, Simoens Pieter, Khaluf Yara
IDLab - Department of Information Technology, Ghent University-IMEC, Ghent, Belgium.
Front Robot AI. 2020 Jul 9;7:86. doi: 10.3389/frobt.2020.00086. eCollection 2020.
Group interactions are widely observed in nature to optimize a set of critical collective behaviors, most notably sensing and decision making in uncertain environments. Nevertheless, these interactions are commonly modeled using local (proximity) networks, in which individuals interact within a certain spatial range. Recently, other interaction topologies have been revealed to support the emergence of higher levels of scalability and rapid information exchange. One prominent example is scale-free networks. In this study, we aim to examine the impact of scale-free communication when implemented for a swarm foraging task in dynamic environments. We model dynamic (uncertain) environments in terms of changes in food density and analyze the collective response of a simulated swarm with communication topology given by either proximity or scale-free networks. Our results suggest that scale-free networks accelerate the process of building up a rapid collective response to cope with the environment changes. However, this comes at the cost of lower coherence of the collective decision. Moreover, our findings suggest that the use of scale-free networks can improve swarm performance due to two side-effects introduced by using long-range interactions and frequent network regeneration. The former is a topological consequence, while the latter is a necessity due to robot motion. These two effects lead to reduced spatial correlations of a robot's behavior with its neighborhood and to an enhanced opinion mixing, i.e., more diversified information sampling. These insights were obtained by comparing the swarm performance in presence of scale-free networks to scenarios with alternative network topologies, and proximity networks with and without packet loss.
在自然界中，群体互动广泛存在，以优化一系列关键的集体行为，最显著的是在不确定环境中的感知和决策。然而，这些互动通常使用局部（邻近）网络进行建模，即个体在一定空间范围内进行互动。最近，人们发现其他互动拓扑结构有助于实现更高水平的可扩展性和快速信息交换。一个突出的例子是无标度网络。在本研究中，我们旨在研究在动态环境中为群体觅食任务实施无标度通信时的影响。我们根据食物密度的变化对动态（不确定）环境进行建模，并分析具有由邻近网络或无标度网络给出的通信拓扑结构的模拟群体的集体反应。我们的结果表明，无标度网络加速了建立快速集体反应以应对环境变化的过程。然而，这是以集体决策的连贯性降低为代价的。此外，我们的研究结果表明，使用无标度网络可以提高群体性能，这归因于使用长程交互和频繁网络再生所带来的两个副作用。前者是一种拓扑结果，而后者是机器人运动的必然结果。这两种效应导致机器人行为与其邻域的空间相关性降低，以及意见混合增强，即信息采样更加多样化。这些见解是通过将存在无标度网络时的群体性能与具有替代网络拓扑结构的场景以及有无数据包丢失的邻近网络进行比较而获得的。
