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Kinodynamic Motion Planning on Gaussian Mixture Fields
Computer Science Department, University of Freiburg, Freiburg im Breisgau, Germany.
Örebro University, School of Science and Technology. (AASS MRO)ORCID iD: 0000-0002-9503-0602
Örebro University, School of Science and Technology. (AASS MRO)ORCID iD: 0000-0001-8658-2985
Örebro University, School of Science and Technology. (AASS MRO)ORCID iD: 0000-0003-0217-9326
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2017 (English)In: IEEE International Conference on Robotics and Automation (ICRA 2017), 2017Conference paper, Published paper (Refereed)
Abstract [en]

We present a mobile robot motion planning ap-proach under kinodynamic constraints that exploits learnedperception priors in the form of continuous Gaussian mixturefields. Our Gaussian mixture fields are statistical multi-modalmotion models of discrete objects or continuous media in theenvironment that encode e.g. the dynamics of air or pedestrianflows. We approach this task using a recently proposed circularlinear flow field map based on semi-wrapped GMMs whosemixture components guide sampling and rewiring in an RRT*algorithm using a steer function for non-holonomic mobilerobots. In our experiments with three alternative baselines,we show that this combination allows the planner to veryefficiently generate high-quality solutions in terms of pathsmoothness, path length as well as natural yet minimum controleffort motions through multi-modal representations of Gaussianmixture fields.

Place, publisher, year, edition, pages
2017.
National Category
Robotics
Identifiers
URN: urn:nbn:se:oru:diva-55177OAI: oai:DiVA.org:oru-55177DiVA, id: diva2:1070556
Conference
IEEE International Conference on Robotics and Automation (ICRA 2017), Singapore, May 29 - June 03, 2017
Available from: 2017-02-01 Created: 2017-02-01 Last updated: 2018-07-30Bibliographically approved

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Kucner, TomaszMagnusson, MartinLilienthal, Achim

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