TY - JOUR
T1 - Towards proactive human–robot collaboration: A foreseeable cognitive manufacturing paradigm
AU - Li, Shufei
AU - Wang, Ruobing
AU - Zheng, Pai
AU - Wang, Lihui
N1 - Funding Information:
This research is partially funded by the Laboratory for Artificial Intelligence in Design (Project Code: RP2-1), Hong Kong Special Administrative Region, and the Research Committee of the Hong Kong Polytechnic University (1-BE2X).
Publisher Copyright:
© 2021 The Society of Manufacturing Engineers
PY - 2021/7
Y1 - 2021/7
N2 - Human–robot collaboration (HRC) has attracted strong interests from researchers and engineers for improved operational flexibility and efficiency towards mass personalization. Nevertheless, existing HRC development mainly undertakes either human-centered or robot-centered manner reactively, where operations are conducted by following the pre-defined instructions, thus far from an efficient integration of robotic automation and human cognitions. The prevailing research on human-level information processing of cognitive computing, the industrial IoT, and robot learning creates the possibility of bridging the gap of knowledge distilling and information sharing between onsite operators, robots and other manufacturing systems. Hence, a foreseeable informatics-based cognitive manufacturing paradigm, Proactive HRC, is introduced as an advanced form of Symbiotic HRC with high-level cognitive teamwork skills to be achieved stepwise, including: (1) inter-collaboration cognition, establishing bi-directional empathy in the execution loop based on a holistic understanding of humans and robots’ situations; (2) spatio-temporal cooperation prediction, estimating human–robot–object interaction of hierarchical sub-tasks/activities over time for the proactive planning; and (3) self-organizing teamwork, converging knowledge of distributed HRC systems for self-organization learning and task allocation. Except for the description of their technical cores, the main challenges and potential opportunities are further discussed to enable the readiness towards Proactive HRC.
AB - Human–robot collaboration (HRC) has attracted strong interests from researchers and engineers for improved operational flexibility and efficiency towards mass personalization. Nevertheless, existing HRC development mainly undertakes either human-centered or robot-centered manner reactively, where operations are conducted by following the pre-defined instructions, thus far from an efficient integration of robotic automation and human cognitions. The prevailing research on human-level information processing of cognitive computing, the industrial IoT, and robot learning creates the possibility of bridging the gap of knowledge distilling and information sharing between onsite operators, robots and other manufacturing systems. Hence, a foreseeable informatics-based cognitive manufacturing paradigm, Proactive HRC, is introduced as an advanced form of Symbiotic HRC with high-level cognitive teamwork skills to be achieved stepwise, including: (1) inter-collaboration cognition, establishing bi-directional empathy in the execution loop based on a holistic understanding of humans and robots’ situations; (2) spatio-temporal cooperation prediction, estimating human–robot–object interaction of hierarchical sub-tasks/activities over time for the proactive planning; and (3) self-organizing teamwork, converging knowledge of distributed HRC systems for self-organization learning and task allocation. Except for the description of their technical cores, the main challenges and potential opportunities are further discussed to enable the readiness towards Proactive HRC.
KW - Cognitive manufacturing
KW - Human–robot collaboration
KW - Industrial Internet-of-Things
KW - Mass personalization
KW - Smart manufacturing
UR - http://www.scopus.com/inward/record.url?scp=85110684073&partnerID=8YFLogxK
U2 - 10.1016/j.jmsy.2021.07.017
DO - 10.1016/j.jmsy.2021.07.017
M3 - Journal article
AN - SCOPUS:85110684073
SN - 0278-6125
VL - 60
SP - 547
EP - 552
JO - Journal of Manufacturing Systems
JF - Journal of Manufacturing Systems
ER -