Learning covariance dynamics for path planning of UAV sensors in a large-scale dynamic environment
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How_Learning Covariance.pdf
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Author(s)
Park, Sooho
Choi, Han-Lim
Roy, Nicholas
How, Jonathan P.
Date Issued
August 2009
Journal
AIAA Guidance, Navigation, and Control Conference 2009.
Publisher
American Institute of Aeronautics and Astronautics
Citation
Park, Sooho et al. "Learning covariance dynamics for path planning of UAV sensors in a large-scale dynamic environment." AIAA Guidance, Navigation, and Control Conference, 10-13 August 2009, Chicago, Illinois 2009. AIAA 2009-6116.
Version
Original manuscript
Abstract
This work addresses the problem of trajectory planning for UAV sensors taking measurements of a large nonlinear system to improve estimation and prediction of such a system. The lack of perfect knowledge of the global system state typically requires probabilistic state estimation. The goal is therefore to find trajectories such that the measurements along each trajectory minimize the expected error of
the predicted state of the system some time into the future. The considerable
nonlinearity of the dynamics governing these systems necessitates the use of com-
putationally costly Monte-Carlo estimation techniques to update the state distri-
bution over time. This computational burden renders planning infeasible, since the
search process must calculate the covariance of the posterior state estimate for each
candidate path. To resolve this challenge, this work proposes to replace the com-
putationally intensive numerical prediction process with an approximate model of
the covariance dynamics learned using nonlinear time-series regression. The use of
autoregressive (AR) time-series features with the regularized least squares (RLS)
algorithm enables the learning of accurate and efficient parametric models. The
learned covariance dynamics are demonstrated to outperform other approximation
strategies such as linearization and partial ensemble propagation when used for trajectory optimization, in both terms of accuracy and speed, with examples of simpli ed weather forecasting.
Description
http://www.aiaa.org/agenda.cfm?lumeetingid=1998&viewcon=agenda&pageview=2&programSeeview=1&dateget=12-Aug-09&formatview=1
MIT Department
Massachusetts Institute of Technology. Aerospace Controls Laboratory
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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