通过主动振荡驱动的接触传感

Rahul Mitra, Kirkland Boyd, Divas Subedi, Digesh Chitrakar, Edwin Aldrich, Ananya Swamy, Kevin Huang
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引用次数: 2

摘要

协作机器人方法寻求将直接的人工干预纳入以前适合孤立机器人设备的任务中,或者使用精确的机器(协作机器人)来协助敏感任务。协作机器人与人类一起工作,将机器人辅助的范围扩展到服务和动态工业或装配任务等空间。话虽如此,人类与机器的近距离接触需要安全的互动,这可以通过轻质材料和新型传感能力来实现。在一些机器人任务中,例如在装配中,需要固有的体力,这使得接触传感在引入人类合作者时特别值得关注。需要一种可以无缝地纳入现有设备的最小侵入性方法。本文对这种接触式传感器进行了原型设计和测试。该传感器是双向的,它主动向刚性连杆提供振荡驱动信号,同时记录和分析所述连杆的机械振动。阻尼引起的自振频移和能量集中变化与刚性连杆的不同接触方式一致。该方法重量轻,成本低,可快速集成到各种机械手中。开发的结构是有利的,因为它不需要任何精密的传感器在机器人身上,主要依赖于机械臂的驱动振荡。收集振荡加速度数据,随后使用基于频率的特征来训练和分类不同的接触位置。根据接触位置可以区分出三种不同的类别。结果是有希望的,并显示出良好的分类接触和接触位置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Contact Sensing via Active Oscillatory Actuation
Collaborative robotic approaches seek to incorporate either direct human intervention in tasks previously suited for isolated robot devices, or to use precise machines (cobots) to assist in sensitive tasks. Cobots work alongside humans to extend the scope of robot assistance to spaces such as service and dynamic industrial or assembly tasks. With that said, the close proximity of humans with machines necessitates safe interaction, which can be achieved via lightweight materials and novel sensing capabilities. The inherent physical strength needed in some robot tasks, such as in assembly, make contact sensing of particular concern when introducing a human collaborator. A minimally intrusive method that can be seamlessly subsumed into extant devices is desired. In this paper, such a contact sensor is prototyped and tested. The sensor is bidirectional in that it actively provides an oscillatory actuation signal to a rigid link while simultaneously recording and analyzing the mechanical vibration of said link. Natural oscillation frequency shifts and energy concentration changes due to damping are congruent with different types of contact with the rigid link. The method is lightweight, low-cost and can be quickly incorporated into various manipulators. The developed configuration is advantageous as it does not require any delicate sensors on the robot body and relies primarily on actuated oscillations of the manipulator. Oscillatory acceleration data is collected and subsequently used to train and classify different contact locations using frequency-based features. Three separate classes are distinguished according to contact location. Results are promising and show excellent classification of both contact and contact location.
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