Event
Lockheed Martin Robotics Seminar: Mark Campbell, "Distributed Data Fusion"
Friday, February 19, 2016
1:30 p.m.
2216 JM Patterson
Ania Picard
301 405 4358
appicard@umd.edu
Lockheed Martin Robotics Seminar
Distributed Data Fusion: Neighbors, Rumors, and the Art of Collective Knowledge
Mark Campbell
S.C. Thomas Sze Director of the Sibley School of Mechanical and Aerospace Engineering John A. Mellowes '60 Professor of Mechanical Engineering
Cornell University
Host
Derek Paley
Abstract
Distributed data fusion is the process whereby a group of agents sense their local environment, communicate with other agents, and collectively try to infer knowledge about a particular process. The applications are many: cooperative robots mapping a room; cooperative UAVs tracking a moving object on the ground or searching for survivors; a distributed formation of space telescopes; a game of hide and seek with kids; a group of people discussing an interesting issue, either in person or on-line. This talk will use a paradigm called Bayesian Distributed Data Fusion (DDF) to explore the challenges, solutions and applications related to a group of agents working together sensing, communicating, and inferring processes in their environment. These challenges include: how to build a scalable solution; how to maintain consistent estimates and avoid rumor propagation; how to handle complex information types (such as semantic human inputs); how to handle varying network topologies. The theoretical underpinnings of these solutions will be studied, along with a series of applications focused cooperating UAVs, robots, and people.
Biography
Mark Campbell is the John A. Mellowes '60 Professor and the S. C. Thomas Sze Director of the Sibley School of Mechanical and Aerospace Engineering at Cornell University. He received his B.S. in Mechanical Engineering from Carnegie Mellon, and his M.S. and Ph.D. in Control and Estimation from MIT. His research interests are in the areas of autonomous systems in air, ground or space; human-robotic information sharing and collaboration; and estimation theory.
