UAV Guidance on GPUs by Nominal Belief-State Optimization

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9550-10-C-0135
Agency Tracking Number: F09B-T06-0329
Amount: $99,950.00
Phase: Phase I
Program: STTR
Awards Year: 2010
Solicitation Year: 2009
Solicitation Topic Code: AF09-BT06
Solicitation Number: 2009.B
Small Business Information
3333 Bowers Avenue, Suite 130, Santa Clara, CA, 95054
DUNS: 790217728
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: Y
Principal Investigator
 Sanjay Rajopadhye
 Associate Professor
 (970) 491-7323
Business Contact
 Brijendra Sharma
Title: Managing Partner
Phone: (408) 203-6828
Research Institution
 Electrical & Computer Engineering
 Edwin Chong
 Colorado State University
Engineering Room B104
Fort Collins, CO, 80523
 (970) 491-6600
 Nonprofit college or university
We apply the theory of partially observable Markov decision processes (POMDPs) to the design of guidance algorithms for controlling the motion of unmanned aerial vehicles (UAVs) with on-board sensors for tracking multiple ground targets. While POMDPs are intractable to optimize exactly, principled approximation methods can be devised based on Bellman’s principle. We introduce a new approximation method called nominal belief-state optimization (NBO). We show that NBO, combined with other application-specific approximations and techniques within the POMDP framework, 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. Although the POMDP/NBO combination exemplifies increased tracking performance, this performance gain can be hindered by computational complexity. Implementing computationally intense subroutines intrinsic to the POMDP/NBO approach in highly parallel graphics processing units (GPUs) will allow the realization of our approach on complex systems in near real time. BENEFIT: Improved UAV surveillance technique, Optimal sensor resource management, High Performance GPU library

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