Global estimation in constrained environments
Abstract
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.
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.Additional details
- Eprint ID
- 29255
- DOI
- 10.1177/0278364911423558
- Resolver ID
- CaltechAUTHORS:20120213-095642723
- Created
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2012-03-13Created from EPrint's datestamp field
- Updated
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2023-09-14Created from EPrint's last_modified field