CAREER: Massive Uniform Manipulation: Algorithmic and Control Theoretic Foundations for Large Populations of Simple Robots Controlled by Uniform Inputs

微纳尺度的机器人操作可以从根本上改变疾病的治疗和小物体的组装。这个项目的目标是自下而上精准地运送材料和组装结构。这种精确的操控必须与大量的操纵者相结合，才能实现快速进展。潜在的影响是广泛的：大量的微操作器可以提供有针对性的治疗，执行微创手术，并设计组织。然而，微型和纳米机器人的小尺寸严重限制了它们的计算、传感和通信能力。该项目将在每个机器人接收完全相同的输入命令的约束下设计集中控制的新技术。这项提议引入了大规模操纵来解决这一问题。大规模操纵使用共享输入来驱动大量机器人进入任意目标状态。统一的主题是使用障碍物来有效地控制蜂群的形状、排列和位置。幸运的是，活体环境中充满了障碍，可以设计出人工工作空间来利用这些技术。应用了两种广泛的技术：1)设计反馈控制器，用于控制群体构型、通过障碍物的操纵和多部件装配。这些控制器将从公民科学网站SarmControl.net收集的人类用户数据中学习。2.)大规模并行汇编、高效聚合和可靠覆盖的算法技术。控制律和算法将通过分析模型、广泛的模拟和使用100个或更多Kilobot机器人的大规模硬件实验来验证。这些规模化的硬件实验支持快速重新配置和模拟由合作者提供的各种微尺度动态模型。

Robotic manipulation at the micro- and nano-scale can fundamentally transform disease treatment and the assembly of small objects. The goal of this project is to precisely deliver materials and assemble structures from the bottom-up. This precision manipulation must be coupled with a large population of manipulators to enable rapid progress. The potential impact is broad: large populations of micro-manipulators could provide targeted therapy, perform minimally invasive surgery, and engineer tissue. However, the small size of micro- and nano-robots severely limits their computation, sensing, and communication capabilities. This project will design new techniques for centralized control under the constraint that every robot receives exactly the same input commands. This proposal introduces massive manipulation to solve this problem. Massive manipulation uses a shared input to drive large populations of robots to arbitrary goal states. The unifying theme is using obstacles to efficiently control the shape, arrangement, and position of the swarm. Fortunately, in vivo environments are rich in obstacles, and artificial workspaces can be engineered to exploit these techniques. Two broad techniques are applied: 1.) Designing feedback controllers for controlling swarm configuration, manipulation through obstacles, and multi-part assembly. These controllers will learn from human-user data collected from the citizen-science site SwarmControl.net. 2.) Algorithmic techniques for massively-parallel assembly, efficient aggregation, and reliable coverage. Control laws and algorithms will be validated with analytical models, extensive simulations, and scaled hardware experiments using 100 or more kilobot robots. These scaled hardware experiments enable rapid reconfiguration and emulating a variety of micro-scale dynamic models provided by collaborators.