Abstract
We consider the problem of planning the motion of unmanned aerial vehicles (UAVs) with on-board sensors, with the goal of tracking ground targets. We apply the theory of partially observable Markov decision processes (POMDPs) to this problem. While POMDPs are intractable to optimize exactly, principled approximation methods can be devised based on Bellman's principle. We show how application-specific approximations produces a practical design that coordinates the UAVs to achieve good long-term mean-squared-error tracking performance in the presence of occlusions and dynamic constraints.
Bio
Edwin K. P. Chong received the B.E.(Hons.) degree with First Class Honors from the University of Adelaide, South Australia, in 1987; and the M.A. and Ph.D. degrees in 1989 and 1991, respectively, both from Princeton University, where he held an IBM Fellowship. He joined the School of Electrical and Computer Engineering at Purdue University in 1991, where he was named a University Faculty Scholar in 1999, and was promoted to Professor in 2001. Since August 2001, he has been a Professor of Electrical and Computer Engineering and a Professor of Mathematics at Colorado State University. His current interests are in sensor networks and optimization methods. He coauthored the recent best-selling book, An Introduction to Optimization, 3rd Edition, Wiley-Interscience, 2008. He is an inaugural Senior Editor of the IEEE Transactions on Automatic Control, and is also on the editor board of Computer Networks and the Journal of Control Science and Engineering.
Professor Chong is a Fellow of the IEEE, and served as an IEEE Control Systems Society Distinguished Lecturer. He received the NSF CAREER Award in 1995 and the ASEE Frederick Emmons Terman Award in 1998. He was a co-recipient of the 2004 Best Paper Award for a paper in the journal Computer Networks. In 2010 he received the IEEE Distinguished Member Award from the Control Systems Society.
