报告题目：Human-Robot Collaborative Learning of Human Welder Intelligences
报告摘要：A major challenge in learning human welder intelligence arises from the specular nature of pool surface that disqualifies diffuse reflection-based laser triangulation methods. To overcome this issue, the mirror surface is advantageously used to reflect a laser pattern away from the arc, simultaneously eliminating the arc illumination problem. To allow welders to freely demonstrate their skills, a human-robot collaborative system has been established where a welder carries a virtual torch, similarly as operating an actual one, without a sensor. The movement is measured at the virtual system and then followed by a robot which carries the sensor and performs the actual welding. The measured weld pool is displayed to the operator at the virtual site such that the welder can observe the change in the operation result to adjust his/her torch movement and other parameters. The true intelligence of the welder is thus contained in and can thus be extracted from the resultant data. To further extend the ability to unconfined environments, an ultra-compact inertial measurement unit (IMU) has been attached to a manually operated torch to monitor its movement and orientation. To ensure the ultra-high precision needed, a foundation has been established to self adaptively cross calibrate the sensors.
报告人简历：YuMing Zhang has been with the University of Kentucky, Lexington, Kentucky, USA since 1991 where he is currently Professor of Electrical Engineering and College of Engineering’s Director of International Partnerships. He received his BS and MS degrees in control engineering from Harbin Institute of Technology and later finished his PhD degree in welding engineering. His research in innovative welding processes, sensing and control of welding processes, and intelligent and robotic welding has brought him 190 peer-reviewed journal publications, 10 US patents, and recognitions from The Institution of Mechanical Engineers of the United Kingdom, International Federation of Automatic Control (IFAC), American Welding Society (AWS), and University of Kentucky College of Engineering. Four of his PhD students received the Henry Granjon Prize from the International Institute of Welding (IIW). Dr. Zhang is currently the Chair of the AWS Technical Papers Committee and a Lead Principal Reviewer of the Welding Journal. He is also a Fellow of the AWS, ASME, and SME.