RI: Medium: Collaborative Research: Real-Time Continuum Manipulation

RI：媒介：协作研究：实时连续操纵

• 批准号: 0904093
• 负责人: Jing Xiao
• 金额: $ 35.19万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0904093&HistoricalAwards=false
• 关键词: RI; Medium; Collaborative; Research; Real

The goal of this project is to investigate the potential of fundamentally new modes of robotic manipulation using novel continuum robots in less-structured environments with large uncertainties and even unknowns. A continuum robot, such as a trunk/tentacle robot arm, is in many senses ?dual? to a traditional robot manipulator (consisting of an articulated arm and a gripper or hand), featuring relatively low precision but high compliance: * Its inherent flexibility and compliance offer greater promise to enable deft manipulation under imprecise and uncertain conditions of objects over a wider range (orders of magnitude) of size and weight, of many different shapes, and with widely different physical characteristics (rigid, soft, flexible, etc.).. * On the other hand, its inherent lack of precision renders the traditional approaches to planning and control of robot manipulators unsuitable for manipulation with continuum robots. This project pioneers the study of the basic problem of autonomous manipulation of an object with much uncertainty by a single continuum robot. It introduces a novel and holistic approach that integrates real-time adaptive planning and robust control schemes under real-time sensing and large environmental uncertainty. It next extends the basic approach to address multiple continuum robots working in a common environment, where each robot needs not know the motion of another robot. The research combines theoretical/algorithmic development with real-world validation on an experimental test bed with real trunk/tentacle robots equipped with sensors. The results will be actively disseminated through publications, free software, and real-world demos to impact research, education, and applications.

该项目的目标是研究在具有较大不确定性甚至未知的结构化程度较低的环境中使用新型连续体机器人进行机器人操作的全新模式的潜力。一个连续体机器人，如躯干/肌腱机器人手臂，在许多意义上是？双重？传统的机器人操作器（由一个铰接臂和一个抓手或手组成），具有相对较低的精度，但具有较高的顺应性：* 其固有的灵活性和合规性提供了更大的承诺，使在更大范围内的对象的不精确和不确定条件下进行灵巧的操作尺寸和重量（数量级）、许多不同的形状以及广泛不同的物理特性（刚性、柔软、柔性等）。* 另一方面，其固有的精度不足，使传统的方法来规划和控制的机器人不适合连续体机器人的操作。该项目开创了由单个连续体机器人自主操作具有很大不确定性的物体的基本问题的研究。它介绍了一种新颖的和整体的方法，集成了实时自适应规划和鲁棒控制方案下的实时传感和大的环境不确定性。接下来，它扩展了基本方法，以解决多个连续体机器人在一个共同的环境中工作，其中每个机器人不需要知道另一个机器人的运动。该研究结合了理论/算法的发展与现实世界的验证实验测试床与真实的树干/tenches机器人配备传感器。研究结果将通过出版物、免费软件和真实世界的演示积极传播，以影响研究、教育和应用。