Graduate Exam Abstract
January 27, 2014, 1:00 pm to 3:00 pm
ECE Conference Room (C101B)
Cooperative Control of Mobile Sensor Platforms in Dynamic Environments
Abstract: We develop guidance algorithms to control mobile
sensor platforms, for both centralized and
decentralized settings, in dynamic environments for
various applications. Specifically, we develop
control algorithms for---unmanned aerial vehicles
(UAVs) with on-board sensors for multitarget
tracking, autonomous amphibious vehicles for flood-
rescue operations, directional sensors (e.g.,
surveillance cameras) for maximizing an
information-gain-based objective function. The
following is a brief description of each of the
above-mentioned guidance control algorithms.
We develop both centralized and decentralized
control algorithms for UAVs based on the theories
of partially observable Markov decision process
(POMDP) and decentralized POMDP respectively. Both
POMDPs and Dec-POMDPs are intractable to solve
exactly; therefore we adopt an approximation method
called nominal belief-state optimization (NBO) to
solve (approximately) the control problems posed as
a POMDP or a Dec-POMDP.
We then address an amphibious vehicle guidance
problem for a flood rescue application. Here, the
goal is to control multiple amphibious vehicles
while minimizing the average rescue time of
multiple human targets stranded in a flood
situation. We again pose this problem as a POMDP,
and extend the above-mentioned NBO approximation
method to solve the guidance problem.
We also study the problem of controlling multiple
2-D directional sensors while maximizing an
objective function based on the information gain
corresponding to multiple target locations. This
problem is found to be a combinatorial optimization
problem, so we develop heuristic methods to solve
the problem approximately, and provide analytical
results on performance guarantees. We then improve
the performance of our heuristics by applying an
approximate dynamic programming approach called
Adviser: Edwin K. P. Chong
Non-ECE Member: Juliana Oprea, Mathematics
Member 3: Diego Krapf, ECE
Addional Members: Rockey Luo, ECE
S. Ragi, H. D. Mittelmann, E. K. P. Chong, "Directional sensor control: Heuristic Approaches," to be submitted.
S. Ragi and E. K. P. Chong, "UAV path planning in a dynamic environment via partially observable Markov decision process," IEEE Transactions on Aerospace and Electronic Systems, vol. 49, no. 4, pp. 2397--2412, October 2013.
S. Ragi and E. K. P. Chong, "Decentralized guidance control of UAVs with explicit optimization of communication," Journal of Intelligent & Robotic Systems, 2013.
S. Ragi, C. S. Tan, and E. K. P. Chong, "Guidance of autonomous amphibious vehicles for flood rescue support," Mathematical Problems in Engineering, vol. 2013, Article ID 528162, 9 pages, 2013.
S. Ragi, H. D. Mittelmann, and E. K. P. Chong, "Directional sensor control for maximizing information gain," in Proceedings of SPIE Optical Engineering + Applications, part of SPIE Optics + Photonics Symposium, San Diego, California, Aug 25--29, 2013, paper number 8857-19.
S. Ragi, C. S. Tan, and E. K. P. Chong, "Feasibility study of POMDP in autonomous amphibious vehicle guidance," in Proceedings of the 2013 IFAC Symposium on Intelligent Autonomous Vehicles (IAV 2013), Gold Coast, Australia, June 26--28, 2013, pp. 85--90.
S. Ragi and E. K. P. Chong, "Decentralized control of unmanned aerial vehicles for multitarget tracking," in Proceedings of the 2013 International Conference on Unmanned Aircraft Systems (ICUAS' 13), Atlanta, Georgia, May 28--31, 2013.
S. Ragi and E. K. P. Chong, "Dynamic UAV path planning for multitarget tracking," in Proceedings of the 2012 American Control Conference, Montreal, Canada, June 27--29, 2012, Paper ThC04.3, pp. 3845--3850.
S. Ragi and E. K. P. Chong, "UAV guidance algorithms via partially observable Markov decision processes," Chapter 59 in Handbook of Unmanned Aerial Vehicles, Dordrecht, Netherlands: Springer Science+Business Media, 2013, to appear.
Program of Study: