UAV Guidance on GPUs by Nominal Belief-State Optimization

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$99,950.00
Award Year:
2010
Program:
STTR
Phase:
Phase I
Contract:
FA9550-10-C-0135
Agency Tracking Number:
F09B-T06-0329
Solicitation Year:
n/a
Solicitation Topic Code:
AF 09TT06
Solicitation Number:
n/a
Small Business Information
Apolent Corporation
3333 Bowers Avenue, Suite 130, Santa Clara, CA, 95054
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
790217728
Principal Investigator:
Sanjay Rajopadhye
Associate Professor
(970) 491-7323
Sanjay.Rajopadhye@colostate.edu
Business Contact:
Brijendra Sharma
Managing Partner
(408) 203-6828
bsharma@apolent.com
Research Institution:
Electrical & Computer Engineering
Edwin Chong
Colorado State University
Engineering Room B104
Fort Collins, CO, 80523
(970) 491-6600
Nonprofit college or university
Abstract
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

* information listed above is at the time of submission.

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