CAREER: Underactuacted Precision Robotic Grasping and Manipulation

职业：欠驱动精密机器人抓取和操纵

• 批准号: 0953856
• 负责人: Aaron Dollar
• 金额: $ 49.86万
• 依托单位: Yale 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: 2010-03-01 至 2015-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0953856&HistoricalAwards=false
• 关键词: CAREER; Underactuacted; Precision; Robotic; Grasping

The vision of robotic assistants for domestic, health care, and workplace applications will not come to fruition without the ability to manipulate typical objects in human environments. However, grasping is challenging in unstructured environments because object models typically required to control the robot are not known beforehand and must be acquired through sensors that are imprecise and incomplete. The majority of research in robotic grasping and manipulation has attempted to address this problem through elaborate multifingered hands combined with tactile sensing and sophisticated planning and control algorithms, often following an anthropomorphic approach. This proposal utilizes an alternative approach involving a focus on the mechanics of the hand itself to accomplish most of the needed ?control.? By appropriately incorporating features such as compliance and underactuation, the uncertainty inherent in unstructured grasping tasks can be more easily accommodated. The proposed work addresses the problem of precision grasping of small objects from the surrounding environment and then begins to address the broader problem of dexterity by examining two-fingered precision manipulation, while investigating the role of compliance, underactuation, and configuration on performance in the presence of uncertainty. This work will be disseminated through publications in scholarly journals and conferences and will contribute to the fundamental understanding of the mechanical interaction of robot hands with small objects and the surrounding environment. These results are expected to lead to the development of low-dimensional hands for precision robotic grasping and manipulation with applications including assistive robots and prosthetics.

如果没有在人类环境中操纵典型物体的能力，用于家庭、医疗保健和工作场所应用的机器人助手的愿景就不会实现。然而，抓取在非结构化环境中是具有挑战性的，因为控制机器人通常所需的对象模型事先是未知的，必须通过不精确和不完整的传感器来获取。大多数机器人抓取和操纵的研究都试图通过精心设计的多指手结合触觉传感和复杂的规划和控制算法来解决这个问题，通常遵循拟人化的方法。这项建议利用了一种替代方法，涉及到手本身的力学重点，以完成大部分需要？控制通过适当地结合功能，如遵守和欠驱动，非结构化抓取任务中固有的不确定性，可以更容易地容纳。拟议的工作解决了从周围环境中的小物体的精确把握的问题，然后开始解决更广泛的问题，通过检查两个手指的精确操作的灵巧，同时调查的作用，遵守，欠驱动，和配置的性能存在的不确定性。这项工作将通过学术期刊和会议上的出版物传播，并将有助于对机器人手与小物体和周围环境的机械相互作用的基本理解。这些结果预计将导致低维的手的发展，精确的机器人抓取和操作的应用，包括辅助机器人和假肢。