Mobile Manipulation for the HSR Intelligent Home Service Robot

2019 ◽  
Author(s):  
Jae-Bong Yi ◽  
Seung-Joon Yi
Keyword(s):  
Service Robot ◽  
Mobile Manipulation ◽  
Intelligent Home ◽  
Home Service Robot ◽  
Home Service

Generic Technology of Home Service Robot

2011 ◽  
Vol 121-126 ◽  
pp. 3330-3334
Author(s):  
Zhong Hai Yu
Keyword(s):  
Modular Design ◽  
Service Robots ◽  
Service Robot ◽  
Home Nursing ◽  
Paper Briefly ◽  
Home Service Robot ◽  
Home Service ◽  
Reconfigurable Robot ◽  
Research Situation ◽  
Technology Standardization

The paper briefly looks back on current research situation of home service robots. It takes a home nursing robot as example to study and discuss some key generic technologies of home service robots. It generally overviewed robot’s mobile platform technology, modular design, reconfigurable robot technique, motion control, sensor technologies, indoor robot’s navigation and localization technology indoor, intelligentization, and robot’s technology standardization. Some the measures of technology standardization of home service robots have been put forward. It has realistic signification for industrialization of home service robots.

Implementation of an SSVEP-based intelligent home service robot system

2020 ◽  
pp. 1-16
Author(s):  
Yuxin Zhang ◽  
Qiang Gao ◽  
Yu Song ◽  
Zhe Wang
Keyword(s):  
Brain Activity ◽  
Neuromuscular Disorders ◽  
Motor Impairment ◽  
Service Robot ◽  
Task Completion ◽  
Home Service Robot ◽  
Home Service ◽  
Severe Motor ◽  
Severe Motor Impairment ◽  
The Subject

BACKGROUND: People with severe neuromuscular disorders caused by an accident or congenital disease cannot normally interact with the physical environment. The intelligent robot technology offers the possibility to solve this problem. However, the robot can hardly carry out the task without understanding the subject’s intention as it relays on speech or gestures. Brain-computer interface (BCI), a communication system that operates external devices by directly converting brain activity into digital signals, provides a solution for this. OBJECTIVE: In this study, a noninvasive BCI-based humanoid robotic system was designed and implemented for home service. METHODS: A humanoid robot that is equipped with multi-sensors navigates to the object placement area under the guidance of a specific symbol “Naomark”, which has a unique ID, and then sends the information of the scanned object back to the user interface. Based on this information, the subject gives commands to the robot to grab the wanted object and give it to the subject. To identify the subject’s intention, the channel projection-based canonical correlation analysis (CP-CCA) method was utilized for the steady state visual evoked potential-based BCI system. RESULTS: The offline results showed that the average classification accuracy of all subjects reached 90%, and the online task completion rate was over 95%. CONCLUSION: Users can complete the grab task with minimum commands, avoiding the control burden caused by complex commands. This would provide a useful assistance means for people with severe motor impairment in their daily life.

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