A Novel Human-Like Control Framework for Mobile Medical Service Robot

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 trans...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2020, Vol.2020 (2020), p.1-11
Main Authors: Wang, Zhen, Hu, Yingbai, Zhou, Xuanyi, Qi, Wen, Li, Jiehao, Zhang, Xin, Quan, Hao
Format: Article
Language:English
Subjects:
Coronaviruses
Disease transmission
Drug delivery systems
Health services
Human activity recognition
Human-computer interaction
Infectious diseases
Meals
Medical supplies
Motion control
Moving object recognition
Neural networks
Predictive control
Probabilistic models
Probability
Robotics
Robots
Sensors
Service robots
Tracking control
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Summary: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.
ISSN:1076-2787
1099-0526
DOI:10.1155/2020/2905841