RI: Medium: Planning and Control for Dynamic Robotic Manipulation

RI：中：动态机器人操纵的规划和控制

• 批准号: 0964665
• 负责人: Kevin Lynch
• 金额: $ 83.09万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-15 至 2016-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0964665&HistoricalAwards=false
• 关键词: RI; Medium; Planning; Control; Dynamic

Manipulation allows a robot to alter its environment by performing mechanical work on it. Combined with locomotion, autonomous manipulation will be a key enabling capability in future robot systems for exploration, home care, military, industrial, and space applications. While most current robotic manipulation consists of simple grasp-and-carry operations, the spectrum of manipulation methods is much broader, including throwing, pushing, rolling, sliding, tapping, etc. Humans and animals make use of these methods on a daily basis, but robots do not. What is missing is a theoretical framework for combining and sequencing such manipulation methods for automatic planning and control of robotic manipulation.This project is working toward autonomous manipulation by bridging the gap from individual robotic manipulation primitives to full manipulation sequence planning. The project is developing theoretical tools, a mechanics-based manipulation planning framework, feedback control algorithms, and an experimental manipulation testbed incorporating high-speed vision. The tools developed for autonomous planning and execution of manipulation sequences will allow future robot users to program robots at a much higher level of abstraction than currently possible. For example, instead of programming low-level control of motors, the user can simply say "place object A on object B." This will help grow the field of personal robotics, by allowing non-experts to interact with robots.International partners in this work include leading laboratories in Italy and Japan. These collaborations provide unique access for graduate and undergraduate researchers to the world's leading experts in robotic manipulation.

操纵允许机器人通过对其进行机械作业来改变其环境。 与运动相结合，自主操纵将成为未来探索、家庭护理、军事、工业和空间应用的机器人系统的关键支持能力。 虽然大多数当前的机器人操纵由简单的抓握和携带操作组成，但操纵方法的范围要广泛得多，包括投掷、推、滚动、滑动、敲击等。人类和动物每天都会使用这些方法，但机器人不会。 缺少的是一个理论框架，用于组合和排序此类操纵方法，以实现机器人操纵的自动规划和控制。该项目正在努力通过弥合从单个机器人操纵基元到完整操纵序列规划的差距来实现自主操纵。 该项目正在开发理论工具、基于力学的操纵规划框架、反馈控制算法以及结合高速视觉的实验操纵测试台。 为自主规划和执行操作序列而开发的工具将允许未来的机器人用户以比目前更高的抽象级别对机器人进行编程。 例如，用户可以简单地说“将对象 A 放在对象 B 上”，而不是对电机的低级控制进行编程。 通过允许非专家与机器人互动，这将有助于发展个人机器人领域。这项工作的国际合作伙伴包括意大利和日本的领先实验室。 这些合作为研究生和本科生研究人员提供了接触机器人操作领域世界领先专家的独特途径。