人-集群系统中动态控制结构的权衡
Trade-offs of Dynamic Control Structure in Human-swarm Systems
AI总结：
本文针对人-集群环境监测任务，比较集中式与分散式控制的权衡，提出一种灵活混合控制方法，在性能上比集中式系统提高19.2%，同时将发送给人类操作员的消息数量减少23.1%，证明集中化并非总是最优。
摘要：
集群机器人学是研究简单机器人仅通过与其他机器人及其环境进行局部交互而表现出复杂行为的学科。集群机器人中的控制主要是分布式的，而集中式控制则广泛用于机器人学的其他领域。集中式和分散式控制策略对于多机器人系统的控制各有一组独特的优点和缺点。分散式系统虽然更具可扩展性和弹性，但与集中式系统相比效率较低，并且会导致向人类操作员传输过多数据，从而造成认知过载。我们研究了在人-集群系统中执行环境监测任务时每种方法的权衡，并提出了一种灵活的混合方法，该方法结合了分层系统和分散式系统的要素。我们发现，灵活的混合系统可以优于集中式系统（在我们的环境监测任务中性能提高19.2%），同时减少发送给人类操作员的消息数量（此处减少23.1%）。我们得出结论，为系统建立集中化并不总是对性能最优，利用集中式和分散式系统的各个方面可以防止集群阻碍其自身性能。
英文摘要：
Swarm robotics is a study of simple robots that exhibit complex behaviour only by interacting locally with other robots and their environment. The control in swarm robotics is mainly distributed whereas centralised control is widely used in other fields of robotics. Centralised and decentralised control strategies both pose a unique set of benefits and drawbacks for the control of multi-robot systems. While decentralised systems are more scalable and resilient, they are less efficient compared to the centralised systems and they lead to excessive data transmissions to the human operators causing cognitive overload. We examine the trade-offs of each of these approaches in a human-swarm system to perform an environmental monitoring task and propose a flexible hybrid approach, which combines elements of hierarchical and decentralised systems. We find that a flexible hybrid system can outperform a centralised system (in our environmental monitoring task by 19.2%) while reducing the number of messages sent to a human operator (here by 23.1%). We conclude that establishing centralisation for a system is not always optimal for performance and that utilising aspects of centralised and decentralised systems can keep the swarm from hindering its performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
