TY - JOUR
T1 - Cloud-edge-device collaboration mechanisms of deep learning models for smart robots in mass personalization
AU - Yang, Chen
AU - Wang, Yingchao
AU - Lan, Shulin
AU - Wang, Lihui
AU - Shen, Weiming
AU - Huang, George Q.
N1 - Funding Information:
This work was supported in part by the National Key Research and Development Program of China under Grant 2021YFB1715700, in part by the National Natural Science Foundation of China under Grant 62103046 and 72192844 , in part by the Fundamental Research Funds for the Central Universities under Grant E1E40805X2 and the State Key Laboratory of Digital Manufacturing Equipment and Technology under Grant DMETKF2021012 .
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/10
Y1 - 2022/10
N2 - Personalized products have gradually become the main business model and core competencies of many enterprises. Large differences in components and short delivery cycles of such products, however, require industrial robots in cloud manufacturing (CMfg) to be smarter, more responsive and more flexible. This means that the deep learning models (DLMs) for smart robots should have the performance of real-time response, optimization, adaptability, dynamism, and multimodal data fusion. To satisfy these typical demands, a cloud-edge-device collaboration framework of CMfg is first proposed to support smart collaborative decision-making for smart robots. Meanwhile, in this context, different deployment and update mechanisms of DLMs for smart robots are analyzed in detail, aiming to support rapid response and high-performance decision-making by considering the factors of data sources, data processing location, offline/online learning, data sharing and the life cycle of DLMs. In addition, related key technologies are presented to provide references for technical research directions in this field.
AB - Personalized products have gradually become the main business model and core competencies of many enterprises. Large differences in components and short delivery cycles of such products, however, require industrial robots in cloud manufacturing (CMfg) to be smarter, more responsive and more flexible. This means that the deep learning models (DLMs) for smart robots should have the performance of real-time response, optimization, adaptability, dynamism, and multimodal data fusion. To satisfy these typical demands, a cloud-edge-device collaboration framework of CMfg is first proposed to support smart collaborative decision-making for smart robots. Meanwhile, in this context, different deployment and update mechanisms of DLMs for smart robots are analyzed in detail, aiming to support rapid response and high-performance decision-making by considering the factors of data sources, data processing location, offline/online learning, data sharing and the life cycle of DLMs. In addition, related key technologies are presented to provide references for technical research directions in this field.
KW - Cloud manufacturing
KW - Cloud-edge-device collaboration
KW - Collaborative learning
KW - Deep learning
KW - Distributed deep learning
KW - Mass personalization
KW - Smart robots
UR - http://www.scopus.com/inward/record.url?scp=85127198043&partnerID=8YFLogxK
U2 - 10.1016/j.rcim.2022.102351
DO - 10.1016/j.rcim.2022.102351
M3 - Journal article
AN - SCOPUS:85127198043
SN - 0736-5845
VL - 77
JO - Robotics and Computer-Integrated Manufacturing
JF - Robotics and Computer-Integrated Manufacturing
M1 - 102351
ER -