Controlling object hand-over in human-robot collaboration via natural wearable sensing

Weitian Wang, Rui Li, Zachary Max Diekel, Yi Chen, Zhujun Zhang, Yunyi Jia

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

With the deployment of collaborative robots in intelligent manufacturing, object hand-over between humans and robots plays a significant role in human-robot collaborations. In most collaboration studies, human hand-over intentions were usually assumed to be known by the robot, and the research mainly focused on robot motion planning and control during the hand-over process. Several approaches have been developed to control the human-robot hand-over, such as vision-based approach and physical contact-based approach, but their applications in manufacturing environments are limited due to various constraints, such as limited human working ranges and safety concerns. In this paper, we develop a practical approach using a wearable sensory system, which has a natural and simple configuration and can be easily utilized by humans. This approach could make a robot recognize a human's hand-over intentions and enable the human to effectively and naturally control the hand-over process. In addition, the approach could recognize the attribute classes of the objects in the human's hand using the wearable sensing and enable the robot to actively make decisions to ensure that graspable objects are handed over from the human to the robot. Results and evaluations illustrate the effectiveness and advantages of the proposed approach in human-robot hand-over control.

Original languageEnglish
Article number8579107
Pages (from-to)59-71
Number of pages13
JournalIEEE Transactions on Human-Machine Systems
Volume49
Issue number1
DOIs
StatePublished - Feb 2019

Keywords

  • Hand-over control
  • human intention understanding
  • human-robot collaboration
  • object attribute recognition

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