论文标题
智能粒子群的分层控制
Hierarchical Control of Smart Particle Swarms
论文作者
论文摘要
我们提出了一种使用两个机器人子集控制机器人群的方法：一组较大的简单，遗忘的机器人（我们称之为工人），该机器人由简单的当地吸引力支配，以及一个具有足够的任务知识的较小群体（指南），以创建和取代工人的工作人员，以创建和取代所需的工人。指南通过更改其相互作用参数来坐标以像智能粒子一样塑造工人。我们在基于物理的模拟器中使用大规模实验研究了该方法，该模拟器具有多达5000个机器人，形成了三种不同的模式。我们的实验表明，该方法随着机器人数量的增加而表现得很好，并且几乎没有模式失真。我们在物理群上评估了使用视觉惯性探射仪计算其相对位置并获得与模拟相当的结果的方法。这项工作奠定了设计和协调可配置的智能粒子的基础，并在智能材料和纳米医学上进行了应用。
We present a method for the control of robot swarms using two subsets of robots: a larger group of simple, oblivious robots (which we call the workers) that is governed by simple local attraction forces, and a smaller group (the guides) with sufficient mission knowledge to create and displace a desired worker formation by operating on the local forces of the workers. The guides coordinate to shape the workers like smart particles by changing their interaction parameters. We study the approach with a large scale experiment in a physics based simulator with up to 5000 robots forming three different patterns. Our experiments reveal that the approach scales well with increasing robot numbers, and presents little pattern distortion. We evaluate the approach on a physical swarm of robots that use visual inertial odometry to compute their relative positions and obtain results that are comparable with simulation. This work lays the foundation for designing and coordinating configurable smart particles, with applications in smart materials and nanomedicine.
