Randomized belief-space replanning in partially-observable continuous spaces


Conference Paper

We present a sample-based replanning strategy for driving partially-observable, high-dimensional robotic systems to a desired goal. At each time step, it uses forward simulation of randomly-sampled open-loop controls to construct a belief-space search tree rooted at its current belief state. Then, it executes the action at the root that leads to the best node in the tree. As a node quality metric we use Monte Carlo simulation to estimate the likelihood of success under the QMDP belief-space feedback policy, which encourages the robot to take information-gathering actions as needed to reach the goal. The technique is demonstrated on target-finding and localization examples in up to 5D state spacess. © 2010 Springer-Verlag Berlin Heidelberg.

Full Text

Duke Authors

Cited Authors

  • Hauser, K

Published Date

  • December 20, 2010

Published In

Volume / Issue

  • 68 / STAR

Start / End Page

  • 193 - 209

Electronic International Standard Serial Number (EISSN)

  • 1610-742X

International Standard Serial Number (ISSN)

  • 1610-7438

International Standard Book Number 13 (ISBN-13)

  • 9783642174513

Digital Object Identifier (DOI)

  • 10.1007/978-3-642-17452-0_12

Citation Source

  • Scopus