About the speaker
Silvia Ferrari is John Brancaccio Professor of Mechanical and Aerospace Engineering at Cornell University. Prior to that, she was Professor of Engineering and Computer Science at Duke University, and Founder and Director of the NSF Integrative Graduate Education and Research Traineeship (IGERT) and Fellowship program on Wireless Intelligent Sensor Networks (WISeNet). Currently, she is the Director of the Laboratory for Intelligent Systems and Controls (LISC) at Cornell University and the co-Director of the Věho Institute for Vehicle Intelligence at the Cornell Tech. Her principal research interests include active perception, robust adaptive control, learning and approximate dynamic programming, and control of multiscale dynamical systems. She is the author of the book “Information-driven Path Planning and Control,” MIT Press (2021), and of the TED talk “Do robots dreams of electric sheep?”. She received the B.S. degree from Embry–Riddle Aeronautical University and the M.A. and Ph.D. degrees from Princeton University. She is a senior member of the IEEE, and a member of ASME, SPIE, and AIAA. She is the recipient of the ONR young investigator award (2004), the NSF CAREER award (2005), and the Presidential Early Career Award for Scientists and Engineers (PECASE) award (2006).
Abstract
In recent decades, we have witnessed a paradigm shift by which autonomous vehicles and robots are being deployed to support sensing objectives, such as coverage, surveillance, tracking, and target recognition. Significant progress has been made in developing planning and control algorithms that maximize information gain, visibility, and coverage objectives, rather than merely supporting traditional vehicle guidance goals, such as avoiding obstacles and following a pre-defined trajectory. Despite the many methods developed to date for autonomous information-driven planning and sensing, there remain many important research frontiers. This talk presents new topics in the area of sensor planning that challenge the current state-of-the-art, namely, multiview planning, perception in action, and occlusion avoidance. The first part of the talk describes new problems associated with planning the motion of a sensor, such as a camera or sonar, that must obtain many looks prior to being able to properly characterize the target of interest. The second part of the talk presents new ideas from event-based sensing and perception-in-the-control loop that allow vision sensors to avoid occlusions and interact with people and objects in the scene in real time.