Coordination of a Human Arm and a Robot Manipulator

人臂与机器人机械手的协调

• 批准号: 9405276
• 负责人: Yuan Zheng
• 金额: $ 21.6万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-09-01 至 1998-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9405276&HistoricalAwards=false
• 关键词: Coordination; Human; Arm; Robot; Manipulator

Zheng This is the first year funding of a three-year continuing award. The research studies coordination mechanisms for a manipulator to work cooperatively with a human operator's arm. By coordinating a robot manipulator with the operator's arm, unstructured environments can be approached through the intelligence of the human operator, while the load of the object can be shared by the manipulator and the human operator. A three-step approach is taken to study the arm-manipulator coordination problem relative to compliant motion, reflexive motion, and motion learning. A compliant motion approach is applied to the interaction of a robot with a human operator in order to make the robot move in accordance with motion of the human operator. Reflexive motion is a new mechanism to be developed in this research. The purpose is for the manipulator to respond appropriately to the intention of the human operator when the operator applies a cueing force to the manipulator via the object being cooperatively handled. The result is that the manipulator will move actively and the human operator will not need not to apply continuous cueing forces to the manipulator. Another new mechanism being explored is motion learning. This allows a manipulator to learn a trajectory that best coordinates with the human operator during the compliant and reflexive motion stages. As a result, when the same manipulation task is repeated, the manipulator can use the learned trajectory, further reducing the load on the human arm. This research will lead to a theoretical foundation for the development of human arm- manipulator coordination technologies which could greatly extend the range of robotic applications with benefit to manufacturing, service, and construction industries.

这是一个为期三年的连续奖励的第一年资助。 研究了机械手与操作者手臂协同工作的协调机制，通过机械手与操作者手臂的协调，可以通过操作者的智能接近非结构化环境，同时可以由机械手和操作者分担物体的负载。采用三步方法研究了与柔顺运动、自反运动和运动学习相关的机械臂协调问题。 一个顺应运动的方法被应用到机器人与人类操作员的相互作用，以使机器人根据人类操作员的运动。 自反运动是本研究中要发展的一种新机构。 其目的是使操纵器在操作员通过被协同处理的对象向操纵器施加提示力时适当地响应人类操作员的意图。 其结果是，机械手将主动移动，人类操作员将不需要不施加连续的提示力的机械手。 另一个正在探索的新机制是运动学习。 这允许操纵器学习在顺应和自反运动阶段期间与人类操作员最佳协调的轨迹。 其结果是，当相同的操作任务是重复的，机械手可以使用学习的轨迹，进一步减少对人的arm. This研究的负载将导致一个理论基础的发展，人的手臂-机械手协调技术，可以大大扩展范围的机器人的应用，有利于制造业，服务业和建筑业。