演讲人：Dr. David Navarro-Alarcon
题 目：Shape Servoing of Deformable Objects: Modelling, Online Estimation, and Control
时 间：2019年3月15日星期五 下午14:00
Over the past years, there has been an increasing interest in the design of sensor-guided methods for controlling the shape of deformable objects with robot manipulators. This shape control problem has many potential applications in growing fields such as surgical robotics, automated food processing, garment industry, home robotics, etc. I refer to these types of feedback tasks as (visual) shape servoing, an approach that contrasts with standard (eye-in-hand) visual servoing — viz. à la Chaumette — in that the servo-loop is formulated in terms of the object's deformable shape and not in terms of the rigid pose of the robot/object.
My aim in this talk is to present the basic formulation of this new type of sensor-guided manipulation tasks. To tackle this challenging (and still open) manipulation problem, in the past few years we have developed a new vision-based methodology that allow us to: characterise the infinite dimensional object's shape with a compact vector of feedback parameters, to online estimate/approximate the deformation properties of an unknown manipulated soft body, and to explicitly servo-control the shape/deformations of the object with active robot motions. I will introduce our recent work on this problem. Examples of our vision-based methods, algorithms and estimators will be demonstrated; open problems, challenges, and opportunities will also be discussed.
David is an Assistant Professor (Robotics) in the Department of Mechanical Engineering of The Hong Kong Polytechnic University (PolyU), and the Principal Investigator of the Robotics and Machine Intelligence Laboratory. Before joining PolyU in July 2017, he worked at the CUHK T Stone Robotics Institute from 2014–2017, first as a Postdoctoral Fellow and then as a Research Assistant Professor. His research interests include robotics science and engineering, machine intelligence, adaptive systems, and control theory. He received his PhD degree in January 2014 from The Chinese University of Hong Kong. David is a Member of the IEEE, the Robotics and Automation Society, and the Computational Intelligence Society.