Decentralised systems composed of a large number of locally interacting agents often rely on coherent behaviour to execute coordinated tasks. Agents cooperate to reach a coherent collective behaviour by aligning their individual behaviour to the one of their neighbours. However, system noise, determined by factors such as individual exploration or errors, hampers and reduces collective coherence. The possibility to overcome noise and reach collective coherence is determined by the strength of social feedback, i.e. the number of communication links. On the one hand, scarce social feedback may lead to a noise-driven system and consequently incoherent behaviour within the group. On the other hand, excessively strong social feedback may require unnecessary computing by individual agents and/or may nullify the possible benefits of noise. In this study, we investigate the delicate balance between social feedback and noise, and its relationship with collective coherence. We perform our analysis through a locust-inspired case study of coherently marching agents, modelling the binary collective decision-making problem of symmetry breaking. For this case study, we analytically approximate the minimal number of communication links necessary to attain maximum collective coherence. To validate our findings, we simulate a 500-robot swarm and obtain good agreement between theoretical results and physics-based simulations. We illustrate through simulation experiments how the robot swarm, using a decentralised algorithm, can adaptively reach coherence for various noise levels by regulating the number of communication links. Moreover, we show that when the system is disrupted by increasing and decreasing the robot density, the robot swarm adaptively responds to these changes in real time. This decentralised adaptive behaviour indicates that the derived relationship between social feedback, noise and coherence is robust and swarm size independent.

中文翻译：

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社会反馈和噪音的分散平衡产生了连贯的集体行为

由大量本地交互代理组成的分散系统通常依赖于一致的行为来执行协调的任务。代理人通过使自己的个人行为与邻居中的一个保持一致来进行协作以达成一致的集体行为。但是，由诸如单个探测或错误之类的因素决定的系统噪声会阻碍并降低集体的连贯性。克服噪音并达到集体连贯性的可能性取决于社会反馈的强度，即沟通联系的数量。一方面，缺乏社会反馈会导致噪声驱动系统，从而导致群体内部行为不协调。另一方面，过强的社会反馈可能需要单个代理进行不必要的计算和/或使噪音的可能收益无效。在这个研究中，我们研究了社会反馈与噪音之间的微妙平衡，以及它与集体凝聚力之间的关系。我们通过以蝗虫为灵感的相干行军案例研究来进行分析，对对称破坏的二元集体决策问题进行建模。对于此案例研究，我们分析性地估算了实现最大的集体连贯性所需的最少通信链接数。为了验证我们的发现，我们模拟了500机器人群体，并在理论结果和基于物理的模拟之间取得了良好的一致性。我们通过仿真实验说明了机器人群如何通过使用分散算法通过调节通信链路的数量来自适应地达到各种噪声水平的连贯性。此外，我们表明，当通过增加和减少机器人密度来破坏系统时，机器人群会实时自适应地响应这些变化。这种分散的适应性行为表明，社会反馈，噪音和连贯性之间的派生关系是鲁棒的，并且群体规模独立。