讲座嘉宾：Xiaofeng Wang

时间：2018年5月21日（周一）下午14:30
地点：东院综合楼306教室

嘉宾简介

Received BS and MS degree in Mathematics from East China Normal University in 2000 and 2003 respectively.Received PhD degree in Electrical Engineering from the University of Notre Dame in 2009.

After graduation, he became a postdoctoral research associate in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign.Joined the Department of Electrical Engineering at the University of South Carolina in 2012.Research interests include robotics, human-robot interactions, autonomous systems, unmanned vehicles, multi-agent systems, networked and real-time control systems, fault-tolerant control, and optimization.

摘要：

 As digital techniques become sophisticated, embedded computers are increasingly integrated into the physical world.  Such systems, often called “cyber-physical systems (CPS)”, are ubiquitous throughout our national infrastructure including transportation, power systems, space systems, manufacturing, and healthcare.  Given the increasing demands on advanced functionalities of these systems, computation efficiency becomes extremely important.  As a typical class of CPS, robotic systems are also facing the same issue.  On one hand, advanced functionalities enhance autonomy, reliability, and adaptivity of robots.  On the other hand, they place a heavy computational burden on embedded computers.  This is particularly true when using Monte Carlo methods, which are often adopted in robot navigation and health management.  To break the computational bottleneck and speed up these processes, this talk will discuss how Lebesgue approximation technique can be used to approximate the robot motion and how it can be integrated into different tasks in robotics, such as simultaneous localization and mapping (SLAM), motion planning, and robot self-diagnosis/prognosis, for computational complexity reduction.

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