Robust range-based localization and motion planning under uncertainty using ultra-wideband radio

The work presented in this thesis addresses two problems: accurately localizing a mobile robot using ultra-wideband (UWB) radio signals in GPS-denied environments; and planning robot trajectories that incorporate belief uncertainty using probabilistic state estimates. Addressing the former, we improve upon traditional approaches to range-based localization by capturing non-linear sensor dynamics using a Monte Carlo method for hidden bias estimation. For the latter, we overcome current limitations of scalable belief space planning by adapting the Probabilistic Roadmap algorithm to enable trajectory search in belief space for minimal uncertainty paths. We contribute a novel solution motivated by linear least-squares estimation and the Riccati equation that provides linear belief updates, allowing us to combine several prediction and measurement steps into one efficient update. This reduces the time required to compute a plan by over two orders of magnitude, leading to a tractable belief space planning method which we call the Belief Roadmap (BRM) algorithm.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2007

Author dc:creator

• Prentice, Samuel J. (Samuel James)

Advisor dc:contributor.advisor

• Nicholas Roy.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/45645

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/45645