GPU Robot Motion Planning Using Semi-Infinite Nonlinear Programming

We propose a many-core GPU implementation of robotic motion planning formulated as a semi-infinite optimization program. Our approach computes the constraints and their gradients in parallel, and feeds the result to a nonlinear optimization solver running on the CPU. To ensure the continuous satisfa...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on parallel and distributed systems Vol. 27; no. 10; pp. 2926 - 2939
Main Authors:	Chretien, Benjamin, Escande, Adrien, Kheddar, Abderrahmane
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.10.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Subjects:	Computer Science CUDA Distributed, Parallel, and Cluster Computing GPGPU Graphics processing units HPC Kinematics motion planning nonlinear optimization Optimization parallel computing Planning Robot kinematics Robotics Service robots CUDA motion planning robotics HPC parallel computing GPGPU nonlinear optimization
ISSN:	1045-9219, 1558-2183
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	We propose a many-core GPU implementation of robotic motion planning formulated as a semi-infinite optimization program. Our approach computes the constraints and their gradients in parallel, and feeds the result to a nonlinear optimization solver running on the CPU. To ensure the continuous satisfaction of our constraints, we use polynomial approximations over time intervals. Because each constraint and its gradient can be evaluated independently for each time interval, we end up with a highly parallelizable problem that can take advantage of many-core architectures. Classic robotic computations (geometry, kinematics, and dynamics) can also benefit from parallel processors, and we carefully study their implementation in our context. This results in having a full constraint evaluator running on the GPU. We present several optimization examples with a humanoid robot. They reveal substantial improvements in terms of computation performance compared to a parallel CPU version.
AbstractList	We propose a many-core GPU implementation of robotic motion planning formulated as a semi-infinite optimization program. Our approach computes the constraints and their gradients in parallel, and feeds the result to a nonlinear optimization solver running on the CPU. To ensure the continuous satisfaction of our constraints, we use polynomial approximations over time intervals. Because each constraint and its gradient can be evaluated independently for each time interval, we end up with a highly parallelizable problem that can take advantage of many-core architectures. Classic robotic computations (geometry, kinematics, and dynamics) can also benefit from parallel processors, and we carefully study their implementation in our context. This results in having a full constraint evaluator running on the GPU. We present several optimization examples with a humanoid robot. They reveal substantial improvements in terms of computation performance compared to a parallel CPU version.
Author	Chretien, Benjamin Escande, Adrien Kheddar, Abderrahmane
Author_xml	– sequence: 1 givenname: Benjamin surname: Chretien fullname: Chretien, Benjamin email: chretien.b@gmail.com organization: Interactive Digital Human Group, LIRMM, Montpellier, France – sequence: 2 givenname: Adrien surname: Escande fullname: Escande, Adrien email: adrien.escande@gmail.com organization: AIST JRL, Tsukuba, Japan – sequence: 3 givenname: Abderrahmane surname: Kheddar fullname: Kheddar, Abderrahmane email: kheddar@gmail.com organization: Interactive Digital Human Group, LIRMM, Montpellier, France
BackLink	https://hal.science/hal-01266581$$DView record in HAL
BookMark	eNp9kMFOAjEQhhuDiYA-gPGyiScPi512u22PBBVIUDcC56YsXSxZWuwuJr69uwE9ePAyM5l8_2Ty9VDHeWcQugY8AMDyfpE9zAcEQzogjADl9Ax1gTERExC008w4YbEkIC9Qr6q2GEPCcNJFo3G2jN78ytfRs6-td1FWaues20TLqq1zs7Px1BXW2dpEL96V1hkdoiz4TdC7XcNcovNCl5W5OvU-Wj49LkaTePY6no6GszinHNex5KkuuIFUUEqMzIECF4TiFTV8tdY80YLm6zSHdE1ZCqKQGCQT-coUhidM0j66O95916XaB7vT4Ut5bdVkOFPtDgNJUybgExr29sjug_84mKpWW38IrnlPNUKwTCRLkobiRyoPvqqCKVRua91qqIO2pQKsWruqtatau-pkt0nCn-TPQ_9lbo4Za4z55TmVmAhOvwHT5YTH
CODEN	ITDSEO
CitedBy_id	crossref_primary_10_1177_02783649231154674 crossref_primary_10_1109_TFUZZ_2023_3290124 crossref_primary_10_3390_jlpea7020010 crossref_primary_10_1109_TRO_2017_2654507 crossref_primary_10_3390_machines11070722 crossref_primary_10_1109_JOE_2022_3152514 crossref_primary_10_1109_LRA_2022_3149573 crossref_primary_10_1049_iet_cds_2017_0315 crossref_primary_10_1109_LRA_2017_2666544 crossref_primary_10_1007_s10586_018_2106_2 crossref_primary_10_2514_1_G007280 crossref_primary_10_1109_ACCESS_2019_2962840 crossref_primary_10_1177_0278364918765952 crossref_primary_10_1109_ACCESS_2018_2889533 crossref_primary_10_1016_j_advengsoft_2017_01_009
Cites_doi	10.1109/TAC.1982.1102922 10.1177/0278364914528132 10.1142/S0219843614410011 10.7210/jrsj.32.536 10.1177/02783649922066628 10.1007/s10107-004-0559-y 10.1007/978-90-481-9971-6_14 10.1109/IROS.2015.7353934 10.1109/TPDS.2012.336 10.1109/ISPASS.2010.5452013 10.1007/978-3-642-36803-5_37 10.1109/5326.704590 10.1177/02783649922066619 10.1016/j.mechmachtheory.2012.03.001 10.1007/BF02017348 10.1109/TPDS.2012.340 10.1177/0278364911429335 10.1109/ROBOT.2009.5152817 10.1016/j.robot.2013.01.008 10.1109/CCGrid.2014.16 10.1109/ROBIO.2006.340170 10.1109/ICHR.2007.4813886 10.1109/TPDS.2010.62 10.1177/0278364908090465 10.1007/978-3-642-31464-3_69 10.1007/978-3-642-28789-3_7 10.1177/0278364909102350 10.1177/0278364913478990
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016 Distributed under a Creative Commons Attribution 4.0 International License
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016 – notice: Distributed under a Creative Commons Attribution 4.0 International License
DBID	97E RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D 1XC VOOES
DOI	10.1109/TPDS.2016.2521373
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Hyper Article en Ligne (HAL) Hyper Article en Ligne (HAL) (Open Access)
DatabaseTitle	CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional
DatabaseTitleList	Technology Research Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Computer Science
EISSN	1558-2183
EndPage	2939
ExternalDocumentID	oai:HAL:hal-01266581v1 4223618341 10_1109_TPDS_2016_2521373 7390287
Genre	orig-research
GrantInformation_xml	– fundername: EU FP7 – fundername: Japan Society for Promotion of Science grantid: P13786 funderid: 10.13039/501100000646 – fundername: Grant-in-Aid for Scientific Research grantid: 25280096
GroupedDBID	--Z -~X .DC 0R~ 29I 4.4 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACIWK AENEX AGQYO AGSQL AHBIQ AKQYR ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD HZ~ IEDLZ IFIPE IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNS TN5 TWZ UHB AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D RIG 1XC 5VS ABFSI AETIX AI. AIBXA AKJIK ALLEH E.L H~9 ICLAB IFJZH RNI RZB VH1 VOOES
ID	FETCH-LOGICAL-c370t-976af7e168332e9c13178230b3e7bda74a83cd6c16d35618f901958cbefe74593
IEDL.DBID	RIE
ISICitedReferencesCount	19
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000384239300011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1045-9219
IngestDate	Tue Oct 14 20:46:26 EDT 2025 Sun Jun 29 13:38:45 EDT 2025 Sat Nov 29 03:36:09 EST 2025 Tue Nov 18 21:32:39 EST 2025 Wed Aug 27 02:52:19 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	10
Keywords	CUDA motion planning robotics HPC parallel computing GPGPU nonlinear optimization
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c370t-976af7e168332e9c13178230b3e7bda74a83cd6c16d35618f901958cbefe74593
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-9033-9742 0000-0003-3844-7986
OpenAccessLink	https://hal.science/hal-01266581
PQID	1830949544
PQPubID	85437
PageCount	14
ParticipantIDs	proquest_journals_1830949544 crossref_citationtrail_10_1109_TPDS_2016_2521373 ieee_primary_7390287 hal_primary_oai_HAL_hal_01266581v1 crossref_primary_10_1109_TPDS_2016_2521373
PublicationCentury	2000
PublicationDate	2016-Oct.-1 2016-10-1 20161001 2016-10
PublicationDateYYYYMMDD	2016-10-01
PublicationDate_xml	– month: 10 year: 2016 text: 2016-Oct.-1 day: 01
PublicationDecade	2010
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on parallel and distributed systems
PublicationTitleAbbrev	TPDS
PublicationYear	2016
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) – name: Institute of Electrical and Electronics Engineers
References	ref13 bell (ref26) 2008 ref12 ref14 ref31 ref30 ref11 volkov (ref29) 0; 10 ref32 ref2 ref1 ref17 ref19 ref18 ref24 ref23 ref25 ref20 ref22 ref21 e?ecio?lu (ref15) 1990; 30 yamane (ref10) 0 ref28 ref27 ref8 ref7 ref9 ref4 ref3 ref6 ref5 harris (ref16) 2007; 2
References_xml	– ident: ref27 doi: 10.1109/TAC.1982.1102922 – ident: ref32 doi: 10.1177/0278364914528132 – ident: ref20 doi: 10.1142/S0219843614410011 – start-page: 554 year: 0 ident: ref10 article-title: Parallel $\mathcal {O}(\log n)$ algorithm for dynamics simulation of humanoid robots publication-title: Proc IEEE-RAS Int Conf Humanoid Robot – ident: ref22 doi: 10.7210/jrsj.32.536 – ident: ref9 doi: 10.1177/02783649922066628 – ident: ref23 doi: 10.1007/s10107-004-0559-y – ident: ref12 doi: 10.1007/978-90-481-9971-6_14 – ident: ref3 doi: 10.1109/IROS.2015.7353934 – ident: ref17 doi: 10.1109/TPDS.2012.336 – ident: ref25 doi: 10.1109/ISPASS.2010.5452013 – volume: 2 year: 2007 ident: ref16 article-title: Optimizing parallel reduction in CUDA publication-title: NVIDIA Developer Technology – ident: ref6 doi: 10.1007/978-3-642-36803-5_37 – ident: ref14 doi: 10.1109/5326.704590 – ident: ref8 doi: 10.1177/02783649922066619 – ident: ref13 doi: 10.1016/j.mechmachtheory.2012.03.001 – volume: 30 start-page: 268 year: 1990 ident: ref15 article-title: A parallel method for fast and practical high-order newton interpolation publication-title: BIT doi: 10.1007/BF02017348 – volume: 10 start-page: 10 year: 0 ident: ref29 article-title: Better performance at lower occupancy publication-title: Proc GPU Technology Conf – ident: ref18 doi: 10.1109/TPDS.2012.340 – ident: ref19 doi: 10.1177/0278364911429335 – ident: ref21 doi: 10.1109/ROBOT.2009.5152817 – ident: ref31 doi: 10.1016/j.robot.2013.01.008 – ident: ref24 doi: 10.1109/CCGrid.2014.16 – ident: ref4 doi: 10.1109/ROBIO.2006.340170 – ident: ref5 doi: 10.1109/ICHR.2007.4813886 – ident: ref30 doi: 10.1109/TPDS.2010.62 – ident: ref1 doi: 10.1177/0278364908090465 – ident: ref7 doi: 10.1007/978-3-642-31464-3_69 – start-page: 1 year: 2008 ident: ref26 article-title: Efficient sparse matrix-vector multiplication on CUDA publication-title: Nvidia – ident: ref28 doi: 10.1007/978-3-642-28789-3_7 – ident: ref11 doi: 10.1177/0278364909102350 – ident: ref2 doi: 10.1177/0278364913478990
SSID	ssj0014504
Score	2.2927175
Snippet	We propose a many-core GPU implementation of robotic motion planning formulated as a semi-infinite optimization program. Our approach computes the constraints...
SourceID	hal proquest crossref ieee
SourceType	Open Access Repository Aggregation Database Enrichment Source Index Database Publisher
StartPage	2926
SubjectTerms	Computer Science CUDA Distributed, Parallel, and Cluster Computing GPGPU Graphics processing units HPC Kinematics motion planning nonlinear optimization Optimization parallel computing Planning Robot kinematics Robotics Service robots
Title	GPU Robot Motion Planning Using Semi-Infinite Nonlinear Programming
URI	https://ieeexplore.ieee.org/document/7390287 https://www.proquest.com/docview/1830949544 https://hal.science/hal-01266581
Volume	27
WOSCitedRecordID	wos000384239300011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-2183 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014504 issn: 1045-9219 databaseCode: RIE dateStart: 19900101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED828UEf_JridEoQn8Tq2qRN8jj8mqBjuA18K22S4sBtsq-_30vaFUURfCvlAqG_S-6u9_EDOEcbjmFCSj3bH-IxqqWXCo66rFE_tE59kTBHNsE7HfH6KrsVuCx7YYwxrvjMXNlHl8vXE7Wwv8quObWzRngVqpxHea9WmTFgoaMKxOgi9CQewyKD6Tfldb9727NFXNFVgMaKcvrNBlXfbAWko1b5cR87I3O__b_t7cBW4UySVo7-LlTMeA-2V0QNpDi3e7D5ZepgDW4eugPyMkknc_LsOHzIirmIuAIC0jOjofc4zobWHyWdfJhGMiXdvJZrhDL7MLi_69-0vYJLwVOUN-ceeh1Jxo0fCUoDI5WPfoPNsaXU8FQnnCWCKh0pP9IUXSqRSdtJKFRqMsNZKOkBrI0nY3MIhGVGBVIEOkgydMaoYGnAM4xGNfUTEYV1aK6-bqyKQeOW7-I9dgFHU8YWkNgCEheA1OGiXPKRT9n4S_gMISvl7Hzsdusptu_Q2kboUvlLvw41C1ApVWBTh8YK4bg4rLMYbzUMcmXI2NHvq45hw24gr-FrwNp8ujAnsK6W8-Fseur08BNwxdZS
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3da9swED_6BW0flvWLZms3UfY06ja2HEt6LOlHypIQmhT6JmxJZoE1KWnav793smI2NgZ9M-YEwr-T7s738QP4hjYcw4SCR9QfEqXcqqiQAnXZon5YW8QyTz3ZhBgM5MODGq7Aad0L45zzxWfujB59Lt_OzAv9KjsXnGaNiFVYJ-as0K1V5wzSticLxPiiHSk8iCGHGbfU-Xh4OaIyruwsQXPFBf_DCq3-pBpIT67y143szcx1430b_AgfgjvJLir8d2DFTXehsaRqYOHk7sL2b3MH96BzM7xnd7NitmB9z-LDltxFzJcQsJF7nES303JCHikbVOM08jkbVtVcjyizD_fXV-NONwpsCpHhorWI0O_IS-HiTHKeOGVi9Bwoy1ZwJwqbizSX3NjMxJnl6FTJUlEvoTSFK51I24ofwNp0NnWHwNLSmUTJxCZ5ie4Yl2mRiBLjUcvjXGbtJrSWX1ebMGqcGC9-aR9ytJQmQDQBogMgTfheL3mq5mz8T_gEIavlaEJ296Kn6R3a2wydqvg1bsIeAVRLBWyacLREWIfj-qzxXsMwV6FWffr3qq-w2R33e7p3O_jxGbZoM1VF3xGsLeYv7hg2zOti8jz_4nXyDcap2Zs
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=GPU+Robot+Motion+Planning+Using+Semi-Infinite+Nonlinear+Programming&rft.jtitle=IEEE+transactions+on+parallel+and+distributed+systems&rft.au=Chretien%2C+Benjamin&rft.au=Escande%2C+Adrien&rft.au=Kheddar%2C+Abderrahmane&rft.date=2016-10-01&rft.pub=IEEE&rft.issn=1045-9219&rft.volume=27&rft.issue=10&rft.spage=2926&rft.epage=2939&rft_id=info:doi/10.1109%2FTPDS.2016.2521373&rft.externalDocID=7390287
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1045-9219&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1045-9219&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1045-9219&client=summon