Next-Generation Safe and Dexterous Robot Hands using High-Torque Direct-Drive Actuation

对于工厂和仓库自动化应用来说，使用足够强大的机械手熟练而安全地操作对象是非常理想的。然而，由于驱动技术的重大限制，今天的机械手不能同时提供高强度、灵巧性和安全性。驱动技术是向机器人发出行动和/或移动信号的系统。齿轮电磁电机是机器人最广泛采用的驱动解决方案，在强度(高传动比时)和相互作用与后驱动能力(低传动比或直接驱动时)之间存在着基本的权衡，这迫使机器人手的性能得到妥协。为了应对这一挑战，该项目将从根本上推进机械手的驱动和控制，并创造和展示一种新型机械手，旨在在不牺牲强度和硬件简单性的情况下具有出色的操作灵活性和接触安全性。这项研究具有很大的潜力来提高仓库和工厂的生产率，从而缓解目前的劳动力短缺，从而造福于整个社会。此外，该奖项将支持研究生和本科生的研究培训，将研究纳入课程，并推广到K-12学生。这项研究的目标是为机械手创建一个新的驱动、控制和学习框架，同时允许增强操作灵活性、接触安全性、高强度和硬件简单性。其关键思想是创造由直接驱动游标永磁(VPM)电机(结合磁性齿轮和电机)驱动的新型机器人手，同时提供高扭矩和极好的透明度，这将扩大机器人手的操作条件。在硬件进步的基础上，该项目将开发新的无传感器本体感觉触觉估计算法和新的基于学习的灵巧操作算法，以充分利用驱动创新提供的新能力。该项目将(I)创建针对机械手优化的高扭矩直接驱动VPM执行器的系统设计框架；(Ii)开发高保真本体感知触觉算法以估计接触位置和力；(Iii)设计和构建新的机械手原型并创建用于灵巧和安全操作的学习算法。新的机器人硬件和集成方法的性能将通过实验和与最先进的机器人手进行比较来评估。该项目由跨部门的机器人基础研究计划支持，该计划由工程指导委员会(ENG)和计算机和信息科学与工程指导委员会(CEISE)联合管理和资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Skillfully and safely manipulating objects with a sufficiently strong robot hand is highly desirable for factory and warehouse automation applications. However, today’s robot hands cannot simultaneously offer high strength with dexterity and safety due to major limitations in actuation technology. Actuation technology is the system that signals for the robot to act and/or move. In geared electromagnetic motors, which is the most widely adopted actuation solution for robots, there exists a fundamental trade-off between strength (at a high gear ratio) and interaction and back-driving capability (at a low gear ratio or in direct drive), which enforces a compromise in the performance of robot hands. Aiming to address this challenge, this project will fundamentally advance the actuation and control of robot hands and create and demonstrate a new type of robot hand that intends to have excellent manipulation dexterity and contact safety without sacrificing strength and hardware simplicity. This research holds a strong potential to improve productivity in warehouses and factories and thus mitigate current labor shortages, thereby benefiting society at large. In addition, this award will support the training of graduate and undergraduate students in research, incorporating the research into curricula, and outreach to K-12 students.The objective of this research is to create a new actuation, control, and learning framework for robotic hands to simultaneously allow for enhanced manipulation dexterity, contact safety, high strength, and hardware simplicity. The key idea is to create new robot hands driven by direct drive Vernier permanent magnet (VPM) motors (combining magnetic gears and motors) that affords high torque and excellent transparency simultaneously, which will expand robot hand operating conditions. Building on the hardware advances, this project will develop new sensor-less proprioceptive tactile estimation algorithms and new learning-based dexterous manipulation algorithms to fully exploit the new capability afforded by actuation innovation. This project will (i) create a systematic design framework for high-torque direct-drive VPM actuators optimized for robot hands; (ii) develop high-fidelity proprioceptive tactile perception algorithms to estimate contact location and forces; (iii) design and build a new robotic hand prototype and create learning algorithms for dexterous and safe manipulation. The performance of novel robotic hardware and integrated methods will be evaluated through experiments and comparison to state-of-the-art robot hands.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.