用于人机协作抛光任务的纠正共享控制架构

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Hao Zhou , Xin Zhang , Jinguo Liu
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引用次数: 0

摘要

人机协作抛光可以整合人类和自动化的能力,以处理复杂的抛光任务。传统的基于阻抗控制的人机协作(HRC)需要操作员与机器人进行物理交互才能获得良好的抛光性能,这给操作员带来了不安全性。为解决这一问题,本文提出了一种使用触觉反馈的矫正共享控制架构,其中使用直接力反射来保证准确的人类意图干预。所提出的控制架构设计了两层:(i) 透明层,在此层中实现了直接力反馈和人机协作抛光策略;(ii) 被动层,在此层中设计了两个能量槽,并分别赋予主从两侧,同时采用耦合能量缩放策略来保证整个系统的被动性。在提出的架构下,采用恒定力打磨工件的正常区域,并根据人的意图施加修正力以处理突发问题。最后,我们进行了两组实验,从抛光效果和用户体验两个方面对所提出的架构进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A corrective shared control architecture for human–robot collaborative polishing tasks

Human–robot collaborative polishing can integrate the capabilities of humans and automation to deal with complex polishing tasks. Traditional impedance-control-based human–robot collaboration (HRC) requires operators to physically interact with robots for a good polishing performance, which brings unsafety to operators. To address this issue, a corrective shared control architecture using haptic feedback is proposed in this paper, where the direct force-reflection is used to guarantee the exact human-intention intervention. The proposed control architecture is designed with two layers: (i) the transparency layer in which the direct force-reflection and the human–robot collaborative polishing strategy are implemented; (ii) the passivity layer in which two energy tanks are designed and endowed with master and slave sides and a coupling energy scaling policy is employed to guarantee the passivity of the whole system. Under the proposed architecture, the constant force is adopted to polish normal areas of workpieces, and corrective force based on human intention is applied to deal with unexpected issues. Finally, two groups of experiments are conducted to evaluate the proposed architecture from two aspects: polishing effect and user experience.

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来源期刊
Robotics and Computer-integrated Manufacturing
Robotics and Computer-integrated Manufacturing 工程技术-工程:制造
CiteScore
24.10
自引率
13.50%
发文量
160
审稿时长
50 days
期刊介绍: The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.
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