TY - JOUR
T1 - A fusion-based spiking neural network approach for predicting collaboration request in human-robot collaboration
AU - Zhang, Rong
AU - Li, Jie
AU - Zheng, Pai
AU - Lu, Yuqian
AU - Bao, Jinsong
AU - Sun, Xuemin
N1 - Funding Information:
This work is financially supported by National Key Research and Development Plan of China (Grant 2019YFB1706300), Shanghai Sailing Program (19YF1401600), Shanghai Municipal Natural Science Foundation (21ZR1400800), and in part by Fundamental Research Funds for the Central Universities (No. 2232019D3-32).
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/12
Y1 - 2022/12
N2 - In human-robot collaborative (HRC) manufacturing systems, how the collaborative robots engage in the collaborative tasks and complete the corresponding work in a timely manner according to the actual state has been a critical factor that hinders the efficiency of HRC. Inappropriate collaborative behaviors will result in a poor perceptual experience for human operators (e.g., robots starting action too early or too late). To address this issue, a fusion-based spiking neural networks (FSNNs) approach for collaboration request prediction is proposed, aiming to find the right collaboration timing for robots in HRC assembly system and to minimize human aversion without affecting human operation behaviors. By encoding human behavior, product state and robot pose into spiking signals that can be processed by FSNNs, the spatio-temporal coupling relationship between those three aspects can be comprehensively analyzed, and to solve the appropriate timing of robot participation in collaboration. Finally, demonstrative experiments are carried out on the HRC assembly of generator end caps in the lab environment. Compared with the baseline methods, the decision accuracy of the proposed one is improved by nearly 30%, which further proves its effectiveness.
AB - In human-robot collaborative (HRC) manufacturing systems, how the collaborative robots engage in the collaborative tasks and complete the corresponding work in a timely manner according to the actual state has been a critical factor that hinders the efficiency of HRC. Inappropriate collaborative behaviors will result in a poor perceptual experience for human operators (e.g., robots starting action too early or too late). To address this issue, a fusion-based spiking neural networks (FSNNs) approach for collaboration request prediction is proposed, aiming to find the right collaboration timing for robots in HRC assembly system and to minimize human aversion without affecting human operation behaviors. By encoding human behavior, product state and robot pose into spiking signals that can be processed by FSNNs, the spatio-temporal coupling relationship between those three aspects can be comprehensively analyzed, and to solve the appropriate timing of robot participation in collaboration. Finally, demonstrative experiments are carried out on the HRC assembly of generator end caps in the lab environment. Compared with the baseline methods, the decision accuracy of the proposed one is improved by nearly 30%, which further proves its effectiveness.
KW - Collaboration prediction
KW - Human-robot collaboration
KW - Spiking neural networks
UR - http://www.scopus.com/inward/record.url?scp=85131128174&partnerID=8YFLogxK
U2 - 10.1016/j.rcim.2022.102383
DO - 10.1016/j.rcim.2022.102383
M3 - Journal article
AN - SCOPUS:85131128174
SN - 0736-5845
VL - 78
JO - Robotics and Computer-Integrated Manufacturing
JF - Robotics and Computer-Integrated Manufacturing
M1 - 102383
ER -