The speed graph method: pseudo time optimal navigation among obstacles subject to uniform braking safety constraints

Abstract This paper considers the synthesis of pseudo time optimal paths for a mobile robot navigating among obstacles subject to uniform braking safety constraints. The classical Brachistochrone problem studies the time optimal path of a particle moving in an obstacle free environment subject to a...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Manor, Gil [verfasserIn] Rimon, Elon [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Mobile robot time optimal navigation High speed navigation Mobile robot safety

Übergeordnetes Werk:	Enthalten in: Autonomous robots - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 41(2016), 2 vom: 12. Feb., Seite 385-400
Übergeordnetes Werk:	volume:41 ; year:2016 ; number:2 ; day:12 ; month:02 ; pages:385-400

Links:	Volltext

DOI / URN:	10.1007/s10514-015-9538-9

Katalog-ID:	SPR010692940

Internformat


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520			\|a Abstract This paper considers the synthesis of pseudo time optimal paths for a mobile robot navigating among obstacles subject to uniform braking safety constraints. The classical Brachistochrone problem studies the time optimal path of a particle moving in an obstacle free environment subject to a constant force field. By encoding the mobile robot’s braking safety constraint as a force field surrounding each obstacle, the paper generalizes the Brachistochrone problem into safe time optimal navigation of a mobile robot in environments populated by polygonal obstacles. Convexity of the safe travel time functional, a path dependent function, allows efficient construction of a speed graph for the environment. The speed graph consists of safe time optimal arcs computed as convex optimization problems in %$O(n^3 \log (1/\epsilon ))%$ total time, where n is the number of obstacle features in the environment and %$\epsilon %$ is the desired solution accuracy. Once the speed graph is constructed for a given environment, pseudo time optimal paths between any start and target robot positions can be computed along the speed graph in %$O(n^2\log n)%$ time. The results are illustrated with examples and described as a readily implementable procedure.
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author_variant	g m gm e r er
matchkey_str	article:15737527:2016----::hsedrpmtopedtmotmlaiainmnosalsujctuio
hierarchy_sort_str	2016
bklnumber	50.25 54.72
publishDate	2016
allfields	10.1007/s10514-015-9538-9 doi (DE-627)SPR010692940 (SPR)s10514-015-9538-9-e DE-627 ger DE-627 rakwb eng 620 ASE 50.25 bkl 54.72 bkl Manor, Gil verfasserin aut The speed graph method: pseudo time optimal navigation among obstacles subject to uniform braking safety constraints 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper considers the synthesis of pseudo time optimal paths for a mobile robot navigating among obstacles subject to uniform braking safety constraints. The classical Brachistochrone problem studies the time optimal path of a particle moving in an obstacle free environment subject to a constant force field. By encoding the mobile robot’s braking safety constraint as a force field surrounding each obstacle, the paper generalizes the Brachistochrone problem into safe time optimal navigation of a mobile robot in environments populated by polygonal obstacles. Convexity of the safe travel time functional, a path dependent function, allows efficient construction of a speed graph for the environment. The speed graph consists of safe time optimal arcs computed as convex optimization problems in %$O(n^3 \log (1/\epsilon ))%$ total time, where n is the number of obstacle features in the environment and %$\epsilon %$ is the desired solution accuracy. Once the speed graph is constructed for a given environment, pseudo time optimal paths between any start and target robot positions can be computed along the speed graph in %$O(n^2\log n)%$ time. The results are illustrated with examples and described as a readily implementable procedure. Mobile robot time optimal navigation (dpeaa)DE-He213 High speed navigation (dpeaa)DE-He213 Mobile robot safety (dpeaa)DE-He213 Rimon, Elon verfasserin aut Enthalten in Autonomous robots Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 41(2016), 2 vom: 12. Feb., Seite 385-400 (DE-627)271175591 (DE-600)1478962-0 1573-7527 nnns volume:41 year:2016 number:2 day:12 month:02 pages:385-400 https://dx.doi.org/10.1007/s10514-015-9538-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.25 ASE 54.72 ASE AR 41 2016 2 12 02 385-400
spelling	10.1007/s10514-015-9538-9 doi (DE-627)SPR010692940 (SPR)s10514-015-9538-9-e DE-627 ger DE-627 rakwb eng 620 ASE 50.25 bkl 54.72 bkl Manor, Gil verfasserin aut The speed graph method: pseudo time optimal navigation among obstacles subject to uniform braking safety constraints 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper considers the synthesis of pseudo time optimal paths for a mobile robot navigating among obstacles subject to uniform braking safety constraints. The classical Brachistochrone problem studies the time optimal path of a particle moving in an obstacle free environment subject to a constant force field. By encoding the mobile robot’s braking safety constraint as a force field surrounding each obstacle, the paper generalizes the Brachistochrone problem into safe time optimal navigation of a mobile robot in environments populated by polygonal obstacles. Convexity of the safe travel time functional, a path dependent function, allows efficient construction of a speed graph for the environment. The speed graph consists of safe time optimal arcs computed as convex optimization problems in %$O(n^3 \log (1/\epsilon ))%$ total time, where n is the number of obstacle features in the environment and %$\epsilon %$ is the desired solution accuracy. Once the speed graph is constructed for a given environment, pseudo time optimal paths between any start and target robot positions can be computed along the speed graph in %$O(n^2\log n)%$ time. The results are illustrated with examples and described as a readily implementable procedure. Mobile robot time optimal navigation (dpeaa)DE-He213 High speed navigation (dpeaa)DE-He213 Mobile robot safety (dpeaa)DE-He213 Rimon, Elon verfasserin aut Enthalten in Autonomous robots Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 41(2016), 2 vom: 12. Feb., Seite 385-400 (DE-627)271175591 (DE-600)1478962-0 1573-7527 nnns volume:41 year:2016 number:2 day:12 month:02 pages:385-400 https://dx.doi.org/10.1007/s10514-015-9538-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.25 ASE 54.72 ASE AR 41 2016 2 12 02 385-400
allfields_unstemmed	10.1007/s10514-015-9538-9 doi (DE-627)SPR010692940 (SPR)s10514-015-9538-9-e DE-627 ger DE-627 rakwb eng 620 ASE 50.25 bkl 54.72 bkl Manor, Gil verfasserin aut The speed graph method: pseudo time optimal navigation among obstacles subject to uniform braking safety constraints 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper considers the synthesis of pseudo time optimal paths for a mobile robot navigating among obstacles subject to uniform braking safety constraints. The classical Brachistochrone problem studies the time optimal path of a particle moving in an obstacle free environment subject to a constant force field. By encoding the mobile robot’s braking safety constraint as a force field surrounding each obstacle, the paper generalizes the Brachistochrone problem into safe time optimal navigation of a mobile robot in environments populated by polygonal obstacles. Convexity of the safe travel time functional, a path dependent function, allows efficient construction of a speed graph for the environment. The speed graph consists of safe time optimal arcs computed as convex optimization problems in %$O(n^3 \log (1/\epsilon ))%$ total time, where n is the number of obstacle features in the environment and %$\epsilon %$ is the desired solution accuracy. Once the speed graph is constructed for a given environment, pseudo time optimal paths between any start and target robot positions can be computed along the speed graph in %$O(n^2\log n)%$ time. The results are illustrated with examples and described as a readily implementable procedure. Mobile robot time optimal navigation (dpeaa)DE-He213 High speed navigation (dpeaa)DE-He213 Mobile robot safety (dpeaa)DE-He213 Rimon, Elon verfasserin aut Enthalten in Autonomous robots Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 41(2016), 2 vom: 12. Feb., Seite 385-400 (DE-627)271175591 (DE-600)1478962-0 1573-7527 nnns volume:41 year:2016 number:2 day:12 month:02 pages:385-400 https://dx.doi.org/10.1007/s10514-015-9538-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.25 ASE 54.72 ASE AR 41 2016 2 12 02 385-400
allfieldsGer	10.1007/s10514-015-9538-9 doi (DE-627)SPR010692940 (SPR)s10514-015-9538-9-e DE-627 ger DE-627 rakwb eng 620 ASE 50.25 bkl 54.72 bkl Manor, Gil verfasserin aut The speed graph method: pseudo time optimal navigation among obstacles subject to uniform braking safety constraints 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper considers the synthesis of pseudo time optimal paths for a mobile robot navigating among obstacles subject to uniform braking safety constraints. The classical Brachistochrone problem studies the time optimal path of a particle moving in an obstacle free environment subject to a constant force field. By encoding the mobile robot’s braking safety constraint as a force field surrounding each obstacle, the paper generalizes the Brachistochrone problem into safe time optimal navigation of a mobile robot in environments populated by polygonal obstacles. Convexity of the safe travel time functional, a path dependent function, allows efficient construction of a speed graph for the environment. The speed graph consists of safe time optimal arcs computed as convex optimization problems in %$O(n^3 \log (1/\epsilon ))%$ total time, where n is the number of obstacle features in the environment and %$\epsilon %$ is the desired solution accuracy. Once the speed graph is constructed for a given environment, pseudo time optimal paths between any start and target robot positions can be computed along the speed graph in %$O(n^2\log n)%$ time. The results are illustrated with examples and described as a readily implementable procedure. Mobile robot time optimal navigation (dpeaa)DE-He213 High speed navigation (dpeaa)DE-He213 Mobile robot safety (dpeaa)DE-He213 Rimon, Elon verfasserin aut Enthalten in Autonomous robots Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 41(2016), 2 vom: 12. Feb., Seite 385-400 (DE-627)271175591 (DE-600)1478962-0 1573-7527 nnns volume:41 year:2016 number:2 day:12 month:02 pages:385-400 https://dx.doi.org/10.1007/s10514-015-9538-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.25 ASE 54.72 ASE AR 41 2016 2 12 02 385-400
allfieldsSound	10.1007/s10514-015-9538-9 doi (DE-627)SPR010692940 (SPR)s10514-015-9538-9-e DE-627 ger DE-627 rakwb eng 620 ASE 50.25 bkl 54.72 bkl Manor, Gil verfasserin aut The speed graph method: pseudo time optimal navigation among obstacles subject to uniform braking safety constraints 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper considers the synthesis of pseudo time optimal paths for a mobile robot navigating among obstacles subject to uniform braking safety constraints. The classical Brachistochrone problem studies the time optimal path of a particle moving in an obstacle free environment subject to a constant force field. By encoding the mobile robot’s braking safety constraint as a force field surrounding each obstacle, the paper generalizes the Brachistochrone problem into safe time optimal navigation of a mobile robot in environments populated by polygonal obstacles. Convexity of the safe travel time functional, a path dependent function, allows efficient construction of a speed graph for the environment. The speed graph consists of safe time optimal arcs computed as convex optimization problems in %$O(n^3 \log (1/\epsilon ))%$ total time, where n is the number of obstacle features in the environment and %$\epsilon %$ is the desired solution accuracy. Once the speed graph is constructed for a given environment, pseudo time optimal paths between any start and target robot positions can be computed along the speed graph in %$O(n^2\log n)%$ time. The results are illustrated with examples and described as a readily implementable procedure. Mobile robot time optimal navigation (dpeaa)DE-He213 High speed navigation (dpeaa)DE-He213 Mobile robot safety (dpeaa)DE-He213 Rimon, Elon verfasserin aut Enthalten in Autonomous robots Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 41(2016), 2 vom: 12. Feb., Seite 385-400 (DE-627)271175591 (DE-600)1478962-0 1573-7527 nnns volume:41 year:2016 number:2 day:12 month:02 pages:385-400 https://dx.doi.org/10.1007/s10514-015-9538-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.25 ASE 54.72 ASE AR 41 2016 2 12 02 385-400
language	English
source	Enthalten in Autonomous robots 41(2016), 2 vom: 12. Feb., Seite 385-400 volume:41 year:2016 number:2 day:12 month:02 pages:385-400
sourceStr	Enthalten in Autonomous robots 41(2016), 2 vom: 12. Feb., Seite 385-400 volume:41 year:2016 number:2 day:12 month:02 pages:385-400
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Mobile robot time optimal navigation High speed navigation Mobile robot safety
dewey-raw	620
isfreeaccess_bool	false
container_title	Autonomous robots
authorswithroles_txt_mv	Manor, Gil @@aut@@ Rimon, Elon @@aut@@
publishDateDaySort_date	2016-02-12T00:00:00Z
hierarchy_top_id	271175591
dewey-sort	3620
id	SPR010692940
language_de	englisch
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The classical Brachistochrone problem studies the time optimal path of a particle moving in an obstacle free environment subject to a constant force field. By encoding the mobile robot’s braking safety constraint as a force field surrounding each obstacle, the paper generalizes the Brachistochrone problem into safe time optimal navigation of a mobile robot in environments populated by polygonal obstacles. Convexity of the safe travel time functional, a path dependent function, allows efficient construction of a speed graph for the environment. The speed graph consists of safe time optimal arcs computed as convex optimization problems in %$O(n^3 \log (1/\epsilon ))%$ total time, where n is the number of obstacle features in the environment and %$\epsilon %$ is the desired solution accuracy. Once the speed graph is constructed for a given environment, pseudo time optimal paths between any start and target robot positions can be computed along the speed graph in %$O(n^2\log n)%$ time. 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author	Manor, Gil
spellingShingle	Manor, Gil ddc 620 bkl 50.25 bkl 54.72 misc Mobile robot time optimal navigation misc High speed navigation misc Mobile robot safety The speed graph method: pseudo time optimal navigation among obstacles subject to uniform braking safety constraints
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topic_title	620 ASE 50.25 bkl 54.72 bkl The speed graph method: pseudo time optimal navigation among obstacles subject to uniform braking safety constraints Mobile robot time optimal navigation (dpeaa)DE-He213 High speed navigation (dpeaa)DE-He213 Mobile robot safety (dpeaa)DE-He213
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title	The speed graph method: pseudo time optimal navigation among obstacles subject to uniform braking safety constraints
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title_full	The speed graph method: pseudo time optimal navigation among obstacles subject to uniform braking safety constraints
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title_sort	speed graph method: pseudo time optimal navigation among obstacles subject to uniform braking safety constraints
title_auth	The speed graph method: pseudo time optimal navigation among obstacles subject to uniform braking safety constraints
abstract	Abstract This paper considers the synthesis of pseudo time optimal paths for a mobile robot navigating among obstacles subject to uniform braking safety constraints. The classical Brachistochrone problem studies the time optimal path of a particle moving in an obstacle free environment subject to a constant force field. By encoding the mobile robot’s braking safety constraint as a force field surrounding each obstacle, the paper generalizes the Brachistochrone problem into safe time optimal navigation of a mobile robot in environments populated by polygonal obstacles. Convexity of the safe travel time functional, a path dependent function, allows efficient construction of a speed graph for the environment. The speed graph consists of safe time optimal arcs computed as convex optimization problems in %$O(n^3 \log (1/\epsilon ))%$ total time, where n is the number of obstacle features in the environment and %$\epsilon %$ is the desired solution accuracy. Once the speed graph is constructed for a given environment, pseudo time optimal paths between any start and target robot positions can be computed along the speed graph in %$O(n^2\log n)%$ time. The results are illustrated with examples and described as a readily implementable procedure.
abstractGer	Abstract This paper considers the synthesis of pseudo time optimal paths for a mobile robot navigating among obstacles subject to uniform braking safety constraints. The classical Brachistochrone problem studies the time optimal path of a particle moving in an obstacle free environment subject to a constant force field. By encoding the mobile robot’s braking safety constraint as a force field surrounding each obstacle, the paper generalizes the Brachistochrone problem into safe time optimal navigation of a mobile robot in environments populated by polygonal obstacles. Convexity of the safe travel time functional, a path dependent function, allows efficient construction of a speed graph for the environment. The speed graph consists of safe time optimal arcs computed as convex optimization problems in %$O(n^3 \log (1/\epsilon ))%$ total time, where n is the number of obstacle features in the environment and %$\epsilon %$ is the desired solution accuracy. Once the speed graph is constructed for a given environment, pseudo time optimal paths between any start and target robot positions can be computed along the speed graph in %$O(n^2\log n)%$ time. The results are illustrated with examples and described as a readily implementable procedure.
abstract_unstemmed	Abstract This paper considers the synthesis of pseudo time optimal paths for a mobile robot navigating among obstacles subject to uniform braking safety constraints. The classical Brachistochrone problem studies the time optimal path of a particle moving in an obstacle free environment subject to a constant force field. By encoding the mobile robot’s braking safety constraint as a force field surrounding each obstacle, the paper generalizes the Brachistochrone problem into safe time optimal navigation of a mobile robot in environments populated by polygonal obstacles. Convexity of the safe travel time functional, a path dependent function, allows efficient construction of a speed graph for the environment. The speed graph consists of safe time optimal arcs computed as convex optimization problems in %$O(n^3 \log (1/\epsilon ))%$ total time, where n is the number of obstacle features in the environment and %$\epsilon %$ is the desired solution accuracy. Once the speed graph is constructed for a given environment, pseudo time optimal paths between any start and target robot positions can be computed along the speed graph in %$O(n^2\log n)%$ time. The results are illustrated with examples and described as a readily implementable procedure.
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title_short	The speed graph method: pseudo time optimal navigation among obstacles subject to uniform braking safety constraints
url	https://dx.doi.org/10.1007/s10514-015-9538-9
remote_bool	true
author2	Rimon, Elon
author2Str	Rimon, Elon
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doi_str	10.1007/s10514-015-9538-9
up_date	2024-07-03T17:42:15.407Z
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The speed graph method: pseudo time optimal navigation among obstacles subject to uniform braking safety constraints

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