One of the most important promises of decentralized systems is scalability, which is often assumed to be present in robot swarm systems without being contested. Simple limitations, such as movement congestion and communication conflicts, can drastically affect scalability. In this work, we study the effects of congestion in a binary collective decision-making task. We evaluate the impact of two types of congestion (communication and movement) when using three different techniques for the task: Honey Bee inspired, Stigmergy based, and Division of Labor. We deploy up to 150 robots in a physics-based simulator performing a sampling mission in an arena with variable levels of robot density, applying the three techniques. Our results suggest that applying Division of Labor coupled with versioned local communication helps to scale the system by minimizing congestion.

翻译：去中心化系统最重要的优势之一是可扩展性，而机器人群体系统通常被认为天然具备这一特性，但这一假设往往未经检验。简单的限制因素（如移动拥塞和通信冲突）会显著影响可扩展性。本研究探讨了在二元集体决策任务中拥塞产生的影响。通过评估三种不同技术（蜂群启发式、基于协作踪迹的群体协作、以及分工协作）下两类拥塞（通信拥塞和移动拥塞）的作用效果，我们在物理仿真环境中部署了多达150个机器人，使其在具有不同机器人密度的实验场地中执行采样任务，并应用上述三种技术。结果表明：将分工协作与带版本控制的本地通信相结合，可通过最小化拥塞来有效扩展系统规模。