学术活动发布与订制系统

报告摘要：The field of mobile robots has caught a great deal of attention in rent years due to the advent of new thresholds in technologies of sensing, computing, and other critical robotic components. The scope of applications is expanding at a pace never seen before. Technically mobile robots are either legged or wheeled. Legged mobile robots are complicated in structure and balance, while being able to negotiate more difficult terrains. Wheeled robots on the other hand present a more cost-effective solution to our day-to-day applications. In this talk, we will first introduce the historical development of mobile robots and then focus of two separate topics each on legged and wheeled mobile robots.

In the legged mobile robot, we introduce a so-called ski-type walking. By adding two canes held by hands, the supporting region and stability margin of a humanoid robot are enlarged in comparison with biped walking. We first study the mechanism of cane-assisted walking by human beings. Based on the study, we develop two ski-type gaits, Crawl_1 and Crawl_2, respectively for the humanoid robot Hubo. The stability performance for the two configurations is compared, which leads to the adoption of the Crawl_2 gait. Furthermore the length of the canes is selected to support a feasible while stable Crawl_2 gait. Simulation and experiments are performed to verify the new ski-type gait.

In the wheeled mobile robot, we propose a novel foot tracking approach for human-robot interaction. It is observed that the displacement of the human feet during the walking cycle is a pseudo periodic waveform. We hence propose an adaptive model for the human walking pattern, which divides the motion of the feet into local and global portions. The local motion is modeled by a modified cosine wave that updates along the time, while the global motion is estimated by a first order estimation. The new model is integrated into particle filtering to guide the tracking. Experiments are performed and the algorithm is evaluated against the generic particle filtering method. Results show significant improvement over the existing approaches.

报告人简介：Dr. Yuan F. Zheng received the MS and Ph.D. degrees in Electrical Engineering from The Ohio State University, in Columbus, Ohio in 1980 and 1984, respectively. His BS degree was received at Tsinghua University in Beijing, China in 1970. From1984 to 1989, he was with the Department of Electrical and Computer Engineering at Clemson University, in Clemson, South Carolina. In that period, Professor Zheng received the Presidential Young Investigator Award from the U.S. President Ronald Reagan in 1986 and was promoted to Associate Professor in 1997. Since August 1989, he has been with The Ohio State University, where he is Winbigler Designated Chair Professor in Electrical and Computer Engineering. Professor Zheng served as the Department Chair between 1993 and 2004, and was elected to IEEE Fellow in 1997. He was appointed as Dean of the School of Electronic, Information and Electrical Engineering at the Shanghai Jiao Tong University, on part-time basis, in 2004-2008. Professor Zheng has served IEEE Robotics and Automation Society in different capacities for many years including an AdCom member, the Vice President for Technical Affairs of the Robotics and Automation Society, and Program Chairs of IEEE International Conference on Robotics and Automation in 1999 and 2011, respectively.