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.

机器人助手对家庭，医疗保健和工作场所应用程序的愿景将无法实现，而无需在人类环境中操纵典型的物体。但是，在非结构化环境中，抓地力是具有挑战性的，因为通常不知道控制机器人所需的对象模型，并且必须通过不准确和不完整的传感器获取。机器人抓握和操纵方面的大多数研究试图通过精心制作的多指针，结合触觉感应和复杂的计划和控制算法来解决这个问题，通常是按照拟人化方法。该建议利用一种替代方法，涉及关注手本身的机制来完成大部分所需的控制。通过适当地合并诸如合规性和未成名之类的功能，可以更容易地容易适应非结构​​化掌握任务的不确定性。所提出的工作解决了从周围环境中精确抓住小物体的问题，然后开始通过检查两指精确操作，同时研究不确定性存在下的合规性，不足和对性能的配置，从而解决了更广泛的敏捷问题。这项工作将通过学术期刊和会议的出版物进行传播，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。预计这些结果将导致低维手的发展，以通过包括辅助机器人和假肢在内的应用进行精确的机器人抓握和操纵。