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A Stochastic Response Surface Approach to Statistical Prediction of Mobile Robot Mobility
MIT, USA.
MIT, USA.
2008 (English)In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) / [ed] Raja Chatila, Alonzo Kelly & Jean-Pierre Merlet, Piscataway, N.J.: IEEE Press, 2008, p. 2234-2239Conference paper, Published paper (Refereed)
Abstract [en]

The ability of autonomous or semi-autonomous mobile robots to rapidly and accurately predict their mobility characteristics is an important requirement for their use in unstructured environments. Most methods for mobility prediction, however, assume precise knowledge of environmental (i.e. terrain) properties. In practical conditions, significant uncertainty is associated with terrain parameter estimation from robotic sensors, and this uncertainty must be considered in a mobility prediction algorithm. Here a method for efficient mobility prediction based on the stochastic response surface approach is presented that explicitly considers terrain parameter uncertainty. The method is compared to a Monte Carlo-based method and simulations show that the stochastic response surface approach can be used for efficient, accurate prediction of mobile robot mobility. ©2008 IEEE.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE Press, 2008. p. 2234-2239
Keywords [en]
Accurate prediction, Autonomous Mobile Robot, Mobility characteristics, Mobility prediction algorithm, Mobility predictions, MONTE CARLO, Parameter uncertainty, Robotic sensor, Statistical prediction, Stochastic response surfaces, Unstructured environments
National Category
Robotics
Identifiers
URN: urn:nbn:se:hh:diva-20768DOI: 10.1109/IROS.2008.4651187ISI: 000259998201137Scopus ID: 2-s2.0-69549084916OAI: oai:DiVA.org:hh-20768DiVA, id: diva2:586730
Conference
2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, Nice, France, 22-26 September, 2008
Available from: 2013-01-12 Created: 2013-01-12 Last updated: 2018-03-22Bibliographically approved

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Iagnemma, Karl

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf