Data-Driven Human Cyber-Physical Systems Collaboration
PhD candidate Jayanti Das, PhD student Gregory Bales, MS student Farhad Ghadamli, Prof. Zhaodan Kong, Prof. Barbara Linke
Humans, in the role of either customers, designers, program managers, or workers, will always play a significant role in future manufacturing. However, the systems that the humans are collaborating with are becoming increasingly complicated especially with the rise of large-scale industrial cyber-physical systems (CPSs), also called Industrial Internet of Things (IIoT). Amazingly, we are still lacking an efficient and effective way of teaming up humans with CPSs, mainly due to adaptability and scalability issues. To mitigate these issues, this research is aimed at developing a data-driven LabVIEW-based management system (LaMS) that can manage data collection, communication between humans and the CPSs, and CPS control. This is a collaborated work with Zhaodan Kong, from the Department of Mechanical and Aerospace Engineering, UC Davis.
Figure: Data-driven LabVIEW-based management system. We use relevant manufacturing tasks (grinding and 3D printing) on a prototypic, modular machine tool system as a test bed and LabView as our main software to: (i) develop a way of inferring input-output relations and mining patterns from data collected from CPSs and humans (inference); (ii) develop a predictive scheme to improve the performance of the human-CPS team (predictive decision-making); (iii) Develop a monitoring, verification and synthesis method to formally guarantee and guide the team’s progress (online monitoring, formal synthesis and verification).
See human sensorimotor behavior in manual grinding here: Prof. Kong’s website