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Artificial potential biased probabilistic roadmap method
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.ORCID iD: 0000-0003-2965-2953
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
2004 (English)In: 2004 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1- 5, PROCEEDINGS, 2004, 461-466 p.Conference paper, Published paper (Refereed)
Abstract [en]

Probabilistic roadmap methods (PRMs) have been successfully used to solve difficult path planning problems but their efficiency is limited when the free space contains narrow passages through which the robot must pass. This paper presents a new sampling scheme that aims to increase the probability of finding paths through narrow passages. Here, a biased sampling scheme is used to increase the distribution of nodes in narrow regions of the free space. A partial computation of the artificial potential field is used to bias the distribution of nodes.

Place, publisher, year, edition, pages
2004. 461-466 p.
Series
IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, ISSN 1050-4729
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-44357DOI: 10.1109/ROBOT.2004.1307192ISI: 000221794800073Scopus ID: 2-s2.0-3042621783ISBN: 0-7803-8232-3 (print)OAI: oai:DiVA.org:kth-44357DiVA: diva2:450899
Conference
IEEE International Conference on Robotics and Automation Location: New Orleans, LA Date: APR 26-MAY 01, 2004
Note
QC 20111024Available from: 2011-10-24 Created: 2011-10-20 Last updated: 2012-01-24Bibliographically approved

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Aarno, DanielKragic, DanicaChristensen, Henrik
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