ロボット群における混雑とスケーラビリティ：集団的意思決定に関する研究
Congestion and Scalability in Robot Swarms: a Study on Collective Decision Making
最大150台のロボットを物理シミュレータで用い、二択の集団的意思決定タスクにおける通信と移動の混雑の影響を評価し、分業とバージョン付き局所通信の組み合わせが混雑を最小化してスケールすることを示した。
著者: Karthik Soma, Vivek Shankar Vardharajan, Heiko Hamann, Giovanni Beltrame
分類: cs.RO, cs.MA
原文アブストラクト
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.
