Coordinated guidance of autonomous UAVs via nominal belief-state optimization

Authors:
Scott A. Miller;Zachary A. Harris;Edwin K. P. Chong
Affiliations:
Numerica Corporation, Loveland, CO;Numerica Corporation, Loveland, CO;Dept. of ECE, Colorado State University, Fort Collins, CO
Venue:
ACC'09 Proceedings of the 2009 conference on American Control Conference
Year:
2009

Citing 5
Cited 0

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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 longterm mean-squared-error tracking performance in the presence of occlusions and dynamic constraints.