Cognition-Based Context-Aware Cloud Computing for Intelligent Robotic Systems in Mobile Education | Zendy

Jianbo Zheng | Zendy; Qieshi Zhang | Zendy; Shihao Xu | Zendy; Hong Peng | Zendy; Qin Wu | Zendy

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Cognition-Based Context-Aware Cloud Computing for Intelligent Robotic Systems in Mobile Education

Author(s) -

Jianbo Zheng,

Qieshi Zhang,

Shihao Xu,

Hong Peng,

Qin Wu

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2867880

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

At present, artificial intelligence (AI) has made considerable progress in recognition of speech, face, and emotion. Potential application to robots could bring significant improvement on intelligent robotic systems. However, limited resource on robots cannot satisfy the large-scale computation and storage that the AI recognition requires. Cloud provides an efficient way for robots, where they off-load the computation too. Therefore, we present a cognition-based context-aware cloud computing framework, which is designed to help robot’s sense environments including user’s emotions. Based on the recognized context information, robots could optimize their responses and improve the user’s experience on interaction. The framework contains a customizable context monitoring system on the mobile end to collect and process the data from the robot’s sensors. Besides, it integrates various AI recognition services in the cloud to extract the context facts by analyzing and understanding the data. Once the context data is extracted, the results are pushed back to mobile end for making a better decision in the next interactions. In this paper, we demonstrate and evaluate the framework by a real case, an educational mobile app for English learning. The results show that the proposed framework could significantly improve the interaction and intelligence of mobile robots.

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