16 Jul, 2018 (Mon)Time:
SpeakerDr. Tiantian Shen
Hunan Normal University
Products of different shapes or shape variations in production lines motivate applications of visual servoing, which relies on visual information to guide the movement of industrial robot arms. Typically, it is getting an arbitrarily positioned robotic system approach the observed target, while crossing a cluttered environment. Technical bottlenecks of VS include: feature exploration of targets in various shapes, multidimensional constraints in large displacement of a visual servo system, adaptation to shape variations or other dynamic attributes of the observed target. This report focuses on the research progress surrounding this topic, starting from features concerning virtual points, lines, rectangles, spheres, circles, cylinders or any projected contours, to path planning techniques, and to future research on system dynamic adaptability.
Biography of the speaker:
Tiantian Shen received the B.S. degree in Mechatronic Engineering from China Agricultural University in 2006, the M.E. degree in Photogrammetry and Remote Sensing from Peking University in 2009 and the Ph.D. in Information Engineering from the University of Hong Kong in 2013. She was a visiting scholar at the Collaborative Advanced Robotics and Intelligent Systems Lab at the University of British Columbia in 2012. She joined Hunan Normal University in 2014. Her research interests include robotics, visual servoing, cooperative control, and optimal control.
All are welcome.
OrganizerProf. G. Chesi
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