Home service robot task planning using semantic knowledge and probabilistic inference

2020 ◽  
Vol 204 ◽  
pp. 106174 ◽  
Author(s):  
Zhongli Wang ◽  
Guohui Tian ◽  
Xuyang Shao
2011 ◽  
Vol 121-126 ◽  
pp. 3330-3334
Author(s):  
Zhong Hai Yu

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.


2020 ◽  
pp. 1-16
Author(s):  
Yuxin Zhang ◽  
Qiang Gao ◽  
Yu Song ◽  
Zhe Wang

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.


2016 ◽  
Vol 7 (1) ◽  
pp. 56-77 ◽  
Author(s):  
Ahmed Abdulhadi Al-Moadhen ◽  
Michael Packianather ◽  
Rossitza Setchi ◽  
Renxi Qiu

A new method is proposed to increase the reliability of generating symbolic plans by extending the Semantic-Knowledge Based (SKB) plan generation to take into account the amount of information and uncertainty related to existing objects, their types and properties, as well as their relationships with each other. This approach constructs plans by depending on probabilistic values which are derived from learning statistical relational models such as Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inference together with semantic information to provide a basis for plausible learning and reasoning services in support of robot task-planning. The MLN module is constructed by using an algorithm to transform the knowledge stored in SKB to types, predicates and formulas which represent the main building block for this module. Following this, the semantic domain knowledge is used to derive implicit expectations of world states and the effects of the action which is nominated for insertion into the task plan. The expectations are matched with MLN output.


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