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. http://resolver.caltech.edu/CaltechAUTHORS:20120213-095642723
Full text is not posted in this repository. Consult Related URLs below.
Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechAUTHORS:20120213-095642723
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.|
|Subject Keywords:||Aerial robotics; motion planning; estimation; search and rescue robots|
|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Tony Diaz|
|Deposited On:||13 Mar 2012 18:44|
|Last Modified:||23 Aug 2016 10:09|
Repository Staff Only: item control page