New motion control tools for robotics and rehabilitation

用于机器人和康复的新型运动控制工具

• 批准号: RGPIN-2018-04919
• 负责人: Maggiore, Manfredi
• 金额: $ 6.7万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=751625
• 关键词: New; motion; control; tools; robotics

This research proposal concerns the development of algorithms to make robots perform complex motions with proven stability. The tools of this research are grounded in nonlinear control theory and mechanics.Once relegated to the floors of manufacturing plants, today robots are spreading in our homes, hospitals, and the society at large. As the technology of robotics advances, there is an increasing need for control algorithms making robots perform complex tasks with proven safety. How to make a walking robot climb stairs or jump? How to make a fish-like robot swim or a snake robot crawl on land?The traditional approach to controlling the motion of robots was to partition the motion control problem into two conceptually separate sub-problems: motion planning and trajectory tracking. Over the past fifteen years, a new paradigm has emerged for motion control of robots which is capable of producing strikingly natural motions with a superior degree of robustness. The paradigm in question is that of virtual constraints. The idea is to represent a desired motion (e.g., bipedal walking) implicitly by means of constraints on the robot joints. Such constraints do not physically exist, but they are enforced by means of feedback control, hence the adjective "virtual". While virtual constraints have come to represent a prominent paradigm for the control of walking robots, their applicability in other areas of robotics remains largely unexplored. The proposed research is driven by the conjecture that the virtual constraint paradigm is in fact applicable to a large class of motion control problems for robots, and it is a good candidate to induce biologically inspired motions of great complexity. Over the past few years, my collaborators and I have developed a theory of virtual constraints for robots with degree of underactuation one, i.e., robots with one actuator less than their number of degrees-of-freedom. While the results we obtained so far are encouraging, much work remains to be done, and this is the subject of my current Discovery Grant application. The research I intend to perform over the next five years has two main thrusts: theoretical and experimental. On the theoretical side, I intend to develop a new theory of virtual constraints for robots with degree of underactuation greater than one. This will allow the virtual constraint paradigm to become viable in a wide variety of robot locomotion problems. On the experimental side, I will initiate a collaborative thrust with researchers in the area of medical rehabilitation, investigating the use of virtual constraints for prosthetic devices and exoskeletons.The long-term objective of this research is the development of a comprehensive set of tools for advanced motion control of biologically inspired robots, capable of producing complex motions that look natural, and which are endowed with a superior degree of robustness against environmental uncertainties.

本研究建议涉及的算法的发展，使机器人执行复杂的运动与证明稳定。本研究的工具是基于非线性控制理论和力学。曾经被降级到制造工厂的地板上，今天机器人正在我们的家庭，医院和整个社会中传播。随着机器人技术的进步，对控制算法的需求越来越大，这些控制算法使机器人能够以经证明的安全性执行复杂任务。如何让一个行走机器人爬楼梯或跳？如何让一个像鱼一样的机器人游泳或蛇机器人在陆地上爬行？传统的控制机器人运动的方法是将运动控制问题划分为两个概念上独立的子问题：运动规划和轨迹跟踪。在过去的十五年中，出现了一种新的范例，用于机器人的运动控制，其能够产生具有上级鲁棒性的惊人的自然运动。这里讨论的范例是虚拟约束。其思想是表示期望的运动（例如，双足行走）隐含地通过对机器人关节的约束。这种约束实际上并不存在，但它们是通过反馈控制来实施的，因此形容词“虚拟”。虽然虚拟约束已经成为步行机器人控制的一个突出范例，但它们在机器人技术其他领域的适用性在很大程度上仍未得到探索。所提出的研究是由虚拟约束范式实际上是适用于机器人的运动控制问题的一大类的猜想，它是一个很好的候选人，以诱导生物启发的运动非常复杂。在过去的几年里，我的合作者和我已经开发了一个理论的虚拟约束的机器人与程度的欠驱动一，即，一个执行器少于其自由度的机器人。虽然我们迄今为止取得的成果令人鼓舞，但仍有许多工作要做，这也是我目前申请发现补助金的主题。我打算在未来五年进行的研究有两个主要方向：理论和实验。在理论方面，我打算开发一个新的理论，虚拟约束的机器人的欠驱动程度大于1。这将使虚拟约束范例成为可行的各种各样的机器人运动问题。在实验方面，我将与医疗康复领域的研究人员合作，研究虚拟约束在假肢设备和外骨骼中的应用。这项研究的长期目标是开发一套全面的工具，用于生物启发机器人的高级运动控制，能够产生看起来自然的复杂运动，并且被赋予对抗环境不确定性的上级鲁棒性。