CLC number: TP242.6
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-01-10
Cited: 0
Clicked: 6501
Citations: Bibtex RefMan EndNote GB/T7714
Qi WANG, Zhen FAN, Weihua SHENG, Senlin ZHANG, Meiqin LIU. Cloud-assisted cognition adaptation for service robots in changing home environments[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(2): 246-257.
@article{title="Cloud-assisted cognition adaptation for service robots in changing home environments",
author="Qi WANG, Zhen FAN, Weihua SHENG, Senlin ZHANG, Meiqin LIU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="2",
pages="246-257",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000431"
}
%0 Journal Article
%T Cloud-assisted cognition adaptation for service robots in changing home environments
%A Qi WANG
%A Zhen FAN
%A Weihua SHENG
%A Senlin ZHANG
%A Meiqin LIU
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 2
%P 246-257
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000431
TY - JOUR
T1 - Cloud-assisted cognition adaptation for service robots in changing home environments
A1 - Qi WANG
A1 - Zhen FAN
A1 - Weihua SHENG
A1 - Senlin ZHANG
A1 - Meiqin LIU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 2
SP - 246
EP - 257
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000431
Abstract: Robots need more intelligence to complete cognitive tasks in home environments. In this paper, we present a new cloud-assisted cognition adaptation mechanism for home service robots, which learns new knowledge from other robots. In this mechanism, a change detection approach is implemented in the robot to detect changes in the user‘s home environment and trigger the adaptation procedure that adapts the robot‘s local customized model to the environmental changes, while the adaptation is achieved by transferring knowledge from the global cloud model to the local model through model fusion. First, three different model fusion methods are proposed to carry out the adaptation procedure, and two key factors of the fusion methods are emphasized. Second, the most suitable model fusion method and its settings for the cloud–;robot knowledge transfer are determined. Third, we carry out a case study of learning in a changing home environment, and the experimental results verify the efficiency and effectiveness of our solutions. The experimental results lead us to propose an empirical guideline of model fusion for the cloud–;robot knowledge transfer.
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