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Global estimation in constrained environments

Kobilarov, Marin and Marsden, Jerrold E. and Sukhatme, Gaurav S. (2012) Global estimation in constrained environments. International Journal of Robotics Research, 31 (1). pp. 24-41. ISSN 0278-3649.

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This article considers the optimal estimation of the state of a dynamic observable using a mobile sensor. The main goal is to compute a sensor trajectory that minimizes the estimation error over a given time horizon taking into account uncertainties in the observable dynamics and sensing, and respecting the constraints of the workspace. The main contribution is a methodology for handling arbitrary dynamics, noise models, and environment constraints in a global optimization framework. It is based on sequential Monte Carlo methods and sampling-based motion planning. Three variance reduction techniques–utility sampling, shuffling, and pruning–based on importance sampling, are proposed to speed up convergence. The developed framework is applied to two typical scenarios: a simple vehicle operating in a planar polygonal obstacle environment and a simulated helicopter searching for a moving target in a 3-D terrain.

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Additional Information:© 2011 The Author(s). Published online before print October 21, 2011. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The authors thank the reviewers for their comprehensive review and advice for improving this paper.
Subject Keywords:Aerial robotics; motion planning; estimation; search and rescue robots
Record Number:CaltechAUTHORS:20120213-095642723
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:29255
Deposited By: Tony Diaz
Deposited On:13 Mar 2012 18:44
Last Modified:23 Aug 2016 10:09

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