论文标题

屏障功能可实现安全意识的力量反馈控制

Barrier functions enable safety-conscious force-feedback control

论文作者

Dawson, Charles, Garrett, Austin, Pollok, Falk, Zhang, Yang, Fan, Chuchu

论文摘要

为了成为人类的有效伴侣,机器人必须越来越舒适地与环境接触。不幸的是,机器人很难区分``足够的''和``太多''力:完成任务需要一些力量,但太多可能会损害设备或伤害人类。设计合规的反馈控制器(例如刚度控制)的传统方法需要对控制参数进行手工调整,并且使建立安全,有效的机器人合作者变得困难。在本文中,我们提出了一种新颖而易于实现的力反馈控制器,该控制器使用控制屏障功能(CBF)直接从用户的最大允许力和扭矩的用户规格中得出合并的控制器。我们比较了传统僵硬控制的方法,以证明控制架构的潜在优势,并在人类机器人协作任务中证明了控制器的有效性:对笨重对象的合作操纵。

In order to be effective partners for humans, robots must become increasingly comfortable with making contact with their environment. Unfortunately, it is hard for robots to distinguish between ``just enough'' and ``too much'' force: some force is required to accomplish the task but too much might damage equipment or injure humans. Traditional approaches to designing compliant force-feedback controllers, such as stiffness control, require difficult hand-tuning of control parameters and make it difficult to build safe, effective robot collaborators. In this paper, we propose a novel yet easy-to-implement force feedback controller that uses control barrier functions (CBFs) to derive a compliant controller directly from users' specifications of the maximum allowable forces and torques. We compare our approach to traditional stiffness control to demonstrate potential advantages of our control architecture, and we demonstrate the effectiveness of our controller on an example human-robot collaboration task: cooperative manipulation of a bulky object.

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