PropertyValue
?:abstract
  • Recently, as a highly infectious disease of novel coronavirus (COVID-19) has swept the globe, more and more patients need to be isolated in the rooms of the hospitals, so how to deliver the meals or drugs to these infectious patients is the urgent work It is a reliable and effective method to transport medical supplies or meals to patients using robots, but how to teach the robot to the destination and to enter the door like a human is an exciting task In this paper, a novel human-like control framework for the mobile medical service robot is considered, where a Kinect sensor is used to manage human activity recognition to generate a designed teaching trajectory Meanwhile, the learning technique of dynamic movement primitives (DMP) with the Gaussian mixture model (GMM) is applied to transfer the skill from humans to robots A neural-based model predictive tracking controller is implemented to follow the teaching trajectory Finally, some demonstrations are carried out in a hospital room to illustrate the superiority and effectiveness of the developed framework © 2020 Xin Zhang et al
is ?:annotates of
?:creator
?:journal
  • Complexity
?:license
  • unk
?:publication_isRelatedTo_Disease
?:source
  • WHO
?:title
  • A Novel Human-Like Control Framework for Mobile Medical Service Robot
?:type
?:who_covidence_id
  • #934140
?:year
  • 2020

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