Real-time friction sensing, feedback and control for dexterous prosthetic and robotic manipulation

用于灵巧假肢和机器人操作的实时摩擦传感、反馈和控制

• 批准号: FT130100858
• 负责人: A/Prof Stephen Redmond
• 金额: $ 46.55万
• 依托单位: The University of New South Wales
• 依托单位国家: 澳大利亚
• 项目类别: ARC Future Fellowships
• 财政年份: 2014
• 资助国家: 澳大利亚
• 起止时间: 2014-02-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FT130100858
• 关键词: Real; time; friction; sensing; feedback

Prosthetic and robotic hands demonstrate poor dexterity during object manipulation, often dropping objects. Humans rarely allow objects to slip because we can sense when an object is slippery and adjust our grip. Exceptionally little research has been directed at replicating this ability to sense friction. This project aims to enable artificial hands to estimate frictional properties while grasping an object. Non-invasive methods to feed back this frictional information to an amputee will also be investigated. Finally, the friction-sensing system will be used to improve robotic gripper control. The outcomes of this research will significantly advance the fields of prosthetics, telesurgery, and service and manufacturing robotics.

假肢和机器人手在物体操作过程中表现出较差的灵巧性，经常掉落物体。人类很少允许物体滑动，因为我们可以感觉到物体何时滑动并调整我们的抓地力。很少有研究针对复制这种感知摩擦力的能力。该项目旨在使假手能够在抓取物体时估计摩擦特性。非侵入性的方法，反馈这种摩擦信息截肢者也将进行调查。最后，摩擦传感系统将用于改善机器人夹持器控制。这项研究的成果将大大推进假肢，外科手术，服务和制造机器人领域。