Research

AI is Driving Our CPS Innovation

Innovating machine learning networks and approaches
Realizing CPS secure and intelligent internet of things (IIoT)
Utilizing big data analytics and process modelling
Focusing AI paradigms on autonomous driving techniques and smart manufacturing systems

Run-to-Run (R2R) Control: aiming at discovering process problems and causes in terms of the techniques of system identification and "digital twin". R2R control can be utilized to dynamically adjust process parameters to meet production and quality criteria with the IIoT based sensor data acquisition. Potential industrial applications are precision machinery and electronic manufacturing industries.
Prediction Capability: aiming at providing solutions on self-diagnosis, preventive maintenance and proactive maintenance.
Human-machine collaboration: aiming at developing collaborative robot design and control approach to improve the ergonomics of operation, efficiency and safety.
Intelligent Material Handling System: aiming at providing low cost and efficient localization, mapping, navigation and multi-AGV system management and coordination for LiDar-based SLAM AGV. Robotic manipulators are integrated with SLAM AGV to perform mobile manipulation in shop floors and plants. Spatial coordinate positioning and image serving are employed to realize autonomous and mobile grasping in factories.
Automatic Optical Inspection: aiming at utilizing AI solution for helping the optical inspection capabilities in addition to consider conventional image process approaches only.
Interactive Human-machine Interface: aiming at utilizing UI/ UX/ AR/ VR/ MR approaches to improve the ergonomics of system operation.