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

职业：大规模均匀操纵：均匀输入控制的大量简单机器人的算法和控制理论基础

• 批准号: 1553063
• 负责人: Aaron Becker
• 金额: $ 55万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1553063&HistoricalAwards=false
• 关键词: CAREER; Massive; Uniform; Manipulation; Algorithmic

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.

在微观和纳米尺度上的机器人操作可以从根本上改变疾病治疗和小物体的组装。这个项目的目标是精确地交付材料和自下而上地组装结构。这种精确的操作必须与大量的机械手相结合，以实现快速的进展。潜在的影响是广泛的：大量的微操纵器可以提供靶向治疗，进行微创手术和工程组织。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。本项目将在每个机器人接收完全相同的输入命令的约束下，设计新的集中控制技术。这个建议引入了大量的操作来解决这个问题。大规模操作使用共享输入来驱动大量机器人进入任意目标状态。统一的主题是利用障碍物有效地控制蜂群的形状、排列和位置。幸运的是，体内环境充满了障碍，人工工作空间可以被设计成利用这些技术。应用了两种广泛的技术：1)设计反馈控制器控制群体配置，操纵通过障碍，和多部件组装。这些控制器将从公民科学网站SwarmControl.net收集的人类用户数据中学习。2.） 大规模并行装配、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个或更多千瓦机器人的规模硬件实验进行验证。这些缩放硬件实验能够快速重新配置和模拟合作者提供的各种微尺度动态模型。

项目成果

Planar Orientation Control and Torque Maximization Using a Swarm With Global Inputs

• DOI: 10.1109/tase.2019.2925908
• 发表时间: 2019-07
• 期刊: IEEE Transactions on Automation Science and Engineering
• 影响因子: 5.6
• 作者: Shiva Shahrokhi;Lillian Lin;Aaron T. Becker

3D Path-Following Using MRAC on a Millimeter-Scale Spiral-Type Magnetic Robot

• DOI: 10.1109/lra.2020.2969159
• 发表时间: 2020-01
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Haoran Zhao;J. Leclerc;Maria Feucht;Olivia Bailey;Aaron T. Becker

3D Control of Rotating Millimeter-Scale Swimmers Through Obstacles

• DOI: 10.1109/icra.2019.8794045
• 发表时间: 2019-05
• 期刊: 2019 International Conference on Robotics and Automation (ICRA)
• 作者: J. Leclerc;Haoran Zhao;Aaron T. Becker

Adaptive Beamforming Using Scattering From a Drone Swarm

利用无人机群的散射进行自适应波束形成

• DOI: 10.1109/wmcs49442.2020.9172335
• 发表时间: 2020
• 期刊: 2020 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems (WMCS
• 作者: Egarguin, Neil J.;Jackson, David R.;Onofrei, Daniel;Leclerc, Julien;Becker, Aaron
• 通讯作者: Becker, Aaron

Planning Coordinated Event Observation for Structured Narratives

规划结构化叙事的协调事件观察

• DOI: 10.1109/icra.2019.8794450
• 发表时间: 2019
• 期刊: Planning Coordinated Event Observation for Structured Narratives
• 作者: Shell, Dylan A.;Huang, Li;Becker, Aaron T.;O'Kane, Jason M.
• 通讯作者: O'Kane, Jason M.