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dc.contributor.author
Phillips, Trevor
dc.contributor.author
Stölzle, Maximilian
dc.contributor.author
Turricelli, Erick
dc.contributor.author
Achermann, Florian
dc.contributor.author
Lawrance, Nicholas
dc.contributor.author
Siegwart, Roland
dc.contributor.author
Chung, Jen Jen
dc.date.accessioned
2021-11-18T08:43:06Z
dc.date.available
2021-11-18T08:33:18Z
dc.date.available
2021-11-18T08:43:06Z
dc.date.issued
2021
dc.identifier.isbn
978-1-7281-9077-8
en_US
dc.identifier.isbn
978-1-7281-9078-5
en_US
dc.identifier.other
10.1109/icra48506.2021.9560879
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/515766
dc.identifier.doi
10.3929/ethz-b-000515766
dc.description.abstract
For asymptotically optimal sampling-based path planners such as RRT*, path quality improves as the number of samples added to the motion tree increases. However, each additional sample requires a nearest-neighbor search. Calculating state transition costs can be particularly difficult in cases with complex dynamics such as aerial vehicles in non-isotropic cost fields like wind. Computationally costly nearest neighbor searches increase the time required to add new samples to the search tree, thereby reducing the likelihood of finding low-cost paths in a given computational time. In this paper, we propose the use of a lightweight neural network to approximate nearest neighbor cost calculations. The network approach uses a low-dimensional encoding of the cost space along with a start and goal query pair and returns an estimate of the path cost that can be used for nearest neighbor and path validity estimation. We demonstrate our method for a Dubins airplane model in a 3D wind field and show that the network method achieves equivalent path lengths as an existing iterative solver 32% faster and, when given the same search time, up to 10.8% shorter.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.title
Learn to Path: Using neural networks to predict Dubins path characteristics for aerial vehicles in wind
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2021-10-18
ethz.book.title
2021 IEEE International Conference on Robotics and Automation (ICRA)
en_US
ethz.pages.start
1073
en_US
ethz.pages.end
1079
en_US
ethz.size
7 p. accepted version
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
IEEE International Conference on Robotics and Automation (ICRA 2021)
en_US
ethz.event.location
Xi'an, China
en_US
ethz.event.date
May 30 – June 5, 2021
en_US
ethz.notes
Conference lecture held on June 1, 2021
en_US
ethz.identifier.wos
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02620 - Inst. f. Robotik u. Intelligente Systeme / Inst. Robotics and Intelligent Systems::03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02620 - Inst. f. Robotik u. Intelligente Systeme / Inst. Robotics and Intelligent Systems::03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
en_US
ethz.date.deposited
2021-11-18T08:33:25Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-11-18T08:43:14Z
ethz.rosetta.lastUpdated
2022-03-29T16:03:01Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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