ALBATROS: adaptive line-based sampling trajectories for sequential measurements
Abstract Measurements in 2D or 3D spaces are ubiquitous among many fields of science and engineering. Often, data samples are gathered via autonomous robots or drones. The path through the measurement space and the location of the samples is traditionally determined upfront using a one-shot design o...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Van Steenkiste, Tom [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Ltd., part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Engineering with computers - London : Springer, 1985, 35(2018), 2 vom: 16. Mai, Seite 537-550 |
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| Übergeordnetes Werk: |
volume:35 ; year:2018 ; number:2 ; day:16 ; month:05 ; pages:537-550 |
| Links: |
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| DOI / URN: |
10.1007/s00366-018-0614-6 |
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| Katalog-ID: |
SPR004525213 |
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| 520 | |a Abstract Measurements in 2D or 3D spaces are ubiquitous among many fields of science and engineering. Often, data samples are gathered via autonomous robots or drones. The path through the measurement space and the location of the samples is traditionally determined upfront using a one-shot design of experiments. However, in certain cases, a sequential approach is preferred. For example, when dealing with a limited sampling budget or when a quick low-resolution overview is desired followed by a steady uniform increase in sampling density, instead of a slow high-resolution one-shot sampling. State-of-the-art sequential design of experiment methods are point-based and are often used to set up experiments both in virtual (simulation) as well as real-world (measurement) environments. In contrast to virtual experimentation, physical measurements require movement of a sensor probe through the measurement space. In these cases, the algorithm not only needs to optimize the sample locations and order but also the path to be traversed by sampling points along measurement lines. In this work, a sequential line-based sampling method is proposed which aims to gradually increase the sampling density across the entire measurement space while minimizing the overall path length. The algorithm is illustrated on a 2D and 3D unit space as well as a complex 3D space and the effectiveness is validated on an engineering measurement use-case. A computer code implementation of the algorithm is provided as an open-source toolbox. | ||
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| 650 | 4 | |a Area coverage |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Automated measurements |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Surrogate modeling |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a van der Herten, Joachim |4 aut | |
| 700 | 1 | |a Deschrijver, Dirk |4 aut | |
| 700 | 1 | |a Dhaene, Tom |4 aut | |
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10.1007/s00366-018-0614-6 doi (DE-627)SPR004525213 (SPR)s00366-018-0614-6-e DE-627 ger DE-627 rakwb eng Van Steenkiste, Tom verfasserin (orcid)0000-0002-3842-3151 aut ALBATROS: adaptive line-based sampling trajectories for sequential measurements 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Measurements in 2D or 3D spaces are ubiquitous among many fields of science and engineering. Often, data samples are gathered via autonomous robots or drones. The path through the measurement space and the location of the samples is traditionally determined upfront using a one-shot design of experiments. However, in certain cases, a sequential approach is preferred. For example, when dealing with a limited sampling budget or when a quick low-resolution overview is desired followed by a steady uniform increase in sampling density, instead of a slow high-resolution one-shot sampling. State-of-the-art sequential design of experiment methods are point-based and are often used to set up experiments both in virtual (simulation) as well as real-world (measurement) environments. In contrast to virtual experimentation, physical measurements require movement of a sensor probe through the measurement space. In these cases, the algorithm not only needs to optimize the sample locations and order but also the path to be traversed by sampling points along measurement lines. In this work, a sequential line-based sampling method is proposed which aims to gradually increase the sampling density across the entire measurement space while minimizing the overall path length. The algorithm is illustrated on a 2D and 3D unit space as well as a complex 3D space and the effectiveness is validated on an engineering measurement use-case. A computer code implementation of the algorithm is provided as an open-source toolbox. Line-based sampling (dpeaa)DE-He213 Sequential design (dpeaa)DE-He213 Design of experiments (dpeaa)DE-He213 Area coverage (dpeaa)DE-He213 Automated measurements (dpeaa)DE-He213 Surrogate modeling (dpeaa)DE-He213 van der Herten, Joachim aut Deschrijver, Dirk aut Dhaene, Tom aut Enthalten in Engineering with computers London : Springer, 1985 35(2018), 2 vom: 16. Mai, Seite 537-550 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:35 year:2018 number:2 day:16 month:05 pages:537-550 https://dx.doi.org/10.1007/s00366-018-0614-6 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_101 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_267 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2018 2 16 05 537-550 |
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10.1007/s00366-018-0614-6 doi (DE-627)SPR004525213 (SPR)s00366-018-0614-6-e DE-627 ger DE-627 rakwb eng Van Steenkiste, Tom verfasserin (orcid)0000-0002-3842-3151 aut ALBATROS: adaptive line-based sampling trajectories for sequential measurements 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Measurements in 2D or 3D spaces are ubiquitous among many fields of science and engineering. Often, data samples are gathered via autonomous robots or drones. The path through the measurement space and the location of the samples is traditionally determined upfront using a one-shot design of experiments. However, in certain cases, a sequential approach is preferred. For example, when dealing with a limited sampling budget or when a quick low-resolution overview is desired followed by a steady uniform increase in sampling density, instead of a slow high-resolution one-shot sampling. State-of-the-art sequential design of experiment methods are point-based and are often used to set up experiments both in virtual (simulation) as well as real-world (measurement) environments. In contrast to virtual experimentation, physical measurements require movement of a sensor probe through the measurement space. In these cases, the algorithm not only needs to optimize the sample locations and order but also the path to be traversed by sampling points along measurement lines. In this work, a sequential line-based sampling method is proposed which aims to gradually increase the sampling density across the entire measurement space while minimizing the overall path length. The algorithm is illustrated on a 2D and 3D unit space as well as a complex 3D space and the effectiveness is validated on an engineering measurement use-case. A computer code implementation of the algorithm is provided as an open-source toolbox. Line-based sampling (dpeaa)DE-He213 Sequential design (dpeaa)DE-He213 Design of experiments (dpeaa)DE-He213 Area coverage (dpeaa)DE-He213 Automated measurements (dpeaa)DE-He213 Surrogate modeling (dpeaa)DE-He213 van der Herten, Joachim aut Deschrijver, Dirk aut Dhaene, Tom aut Enthalten in Engineering with computers London : Springer, 1985 35(2018), 2 vom: 16. Mai, Seite 537-550 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:35 year:2018 number:2 day:16 month:05 pages:537-550 https://dx.doi.org/10.1007/s00366-018-0614-6 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_101 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_267 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2018 2 16 05 537-550 |
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10.1007/s00366-018-0614-6 doi (DE-627)SPR004525213 (SPR)s00366-018-0614-6-e DE-627 ger DE-627 rakwb eng Van Steenkiste, Tom verfasserin (orcid)0000-0002-3842-3151 aut ALBATROS: adaptive line-based sampling trajectories for sequential measurements 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Measurements in 2D or 3D spaces are ubiquitous among many fields of science and engineering. Often, data samples are gathered via autonomous robots or drones. The path through the measurement space and the location of the samples is traditionally determined upfront using a one-shot design of experiments. However, in certain cases, a sequential approach is preferred. For example, when dealing with a limited sampling budget or when a quick low-resolution overview is desired followed by a steady uniform increase in sampling density, instead of a slow high-resolution one-shot sampling. State-of-the-art sequential design of experiment methods are point-based and are often used to set up experiments both in virtual (simulation) as well as real-world (measurement) environments. In contrast to virtual experimentation, physical measurements require movement of a sensor probe through the measurement space. In these cases, the algorithm not only needs to optimize the sample locations and order but also the path to be traversed by sampling points along measurement lines. In this work, a sequential line-based sampling method is proposed which aims to gradually increase the sampling density across the entire measurement space while minimizing the overall path length. The algorithm is illustrated on a 2D and 3D unit space as well as a complex 3D space and the effectiveness is validated on an engineering measurement use-case. A computer code implementation of the algorithm is provided as an open-source toolbox. Line-based sampling (dpeaa)DE-He213 Sequential design (dpeaa)DE-He213 Design of experiments (dpeaa)DE-He213 Area coverage (dpeaa)DE-He213 Automated measurements (dpeaa)DE-He213 Surrogate modeling (dpeaa)DE-He213 van der Herten, Joachim aut Deschrijver, Dirk aut Dhaene, Tom aut Enthalten in Engineering with computers London : Springer, 1985 35(2018), 2 vom: 16. Mai, Seite 537-550 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:35 year:2018 number:2 day:16 month:05 pages:537-550 https://dx.doi.org/10.1007/s00366-018-0614-6 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_101 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_267 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2018 2 16 05 537-550 |
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10.1007/s00366-018-0614-6 doi (DE-627)SPR004525213 (SPR)s00366-018-0614-6-e DE-627 ger DE-627 rakwb eng Van Steenkiste, Tom verfasserin (orcid)0000-0002-3842-3151 aut ALBATROS: adaptive line-based sampling trajectories for sequential measurements 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Measurements in 2D or 3D spaces are ubiquitous among many fields of science and engineering. Often, data samples are gathered via autonomous robots or drones. The path through the measurement space and the location of the samples is traditionally determined upfront using a one-shot design of experiments. However, in certain cases, a sequential approach is preferred. For example, when dealing with a limited sampling budget or when a quick low-resolution overview is desired followed by a steady uniform increase in sampling density, instead of a slow high-resolution one-shot sampling. State-of-the-art sequential design of experiment methods are point-based and are often used to set up experiments both in virtual (simulation) as well as real-world (measurement) environments. In contrast to virtual experimentation, physical measurements require movement of a sensor probe through the measurement space. In these cases, the algorithm not only needs to optimize the sample locations and order but also the path to be traversed by sampling points along measurement lines. In this work, a sequential line-based sampling method is proposed which aims to gradually increase the sampling density across the entire measurement space while minimizing the overall path length. The algorithm is illustrated on a 2D and 3D unit space as well as a complex 3D space and the effectiveness is validated on an engineering measurement use-case. A computer code implementation of the algorithm is provided as an open-source toolbox. Line-based sampling (dpeaa)DE-He213 Sequential design (dpeaa)DE-He213 Design of experiments (dpeaa)DE-He213 Area coverage (dpeaa)DE-He213 Automated measurements (dpeaa)DE-He213 Surrogate modeling (dpeaa)DE-He213 van der Herten, Joachim aut Deschrijver, Dirk aut Dhaene, Tom aut Enthalten in Engineering with computers London : Springer, 1985 35(2018), 2 vom: 16. Mai, Seite 537-550 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:35 year:2018 number:2 day:16 month:05 pages:537-550 https://dx.doi.org/10.1007/s00366-018-0614-6 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_101 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_267 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2018 2 16 05 537-550 |
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10.1007/s00366-018-0614-6 doi (DE-627)SPR004525213 (SPR)s00366-018-0614-6-e DE-627 ger DE-627 rakwb eng Van Steenkiste, Tom verfasserin (orcid)0000-0002-3842-3151 aut ALBATROS: adaptive line-based sampling trajectories for sequential measurements 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Measurements in 2D or 3D spaces are ubiquitous among many fields of science and engineering. Often, data samples are gathered via autonomous robots or drones. The path through the measurement space and the location of the samples is traditionally determined upfront using a one-shot design of experiments. However, in certain cases, a sequential approach is preferred. For example, when dealing with a limited sampling budget or when a quick low-resolution overview is desired followed by a steady uniform increase in sampling density, instead of a slow high-resolution one-shot sampling. State-of-the-art sequential design of experiment methods are point-based and are often used to set up experiments both in virtual (simulation) as well as real-world (measurement) environments. In contrast to virtual experimentation, physical measurements require movement of a sensor probe through the measurement space. In these cases, the algorithm not only needs to optimize the sample locations and order but also the path to be traversed by sampling points along measurement lines. In this work, a sequential line-based sampling method is proposed which aims to gradually increase the sampling density across the entire measurement space while minimizing the overall path length. The algorithm is illustrated on a 2D and 3D unit space as well as a complex 3D space and the effectiveness is validated on an engineering measurement use-case. A computer code implementation of the algorithm is provided as an open-source toolbox. Line-based sampling (dpeaa)DE-He213 Sequential design (dpeaa)DE-He213 Design of experiments (dpeaa)DE-He213 Area coverage (dpeaa)DE-He213 Automated measurements (dpeaa)DE-He213 Surrogate modeling (dpeaa)DE-He213 van der Herten, Joachim aut Deschrijver, Dirk aut Dhaene, Tom aut Enthalten in Engineering with computers London : Springer, 1985 35(2018), 2 vom: 16. Mai, Seite 537-550 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:35 year:2018 number:2 day:16 month:05 pages:537-550 https://dx.doi.org/10.1007/s00366-018-0614-6 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_101 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_267 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2018 2 16 05 537-550 |
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albatros: adaptive line-based sampling trajectories for sequential measurements |
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ALBATROS: adaptive line-based sampling trajectories for sequential measurements |
| abstract |
Abstract Measurements in 2D or 3D spaces are ubiquitous among many fields of science and engineering. Often, data samples are gathered via autonomous robots or drones. The path through the measurement space and the location of the samples is traditionally determined upfront using a one-shot design of experiments. However, in certain cases, a sequential approach is preferred. For example, when dealing with a limited sampling budget or when a quick low-resolution overview is desired followed by a steady uniform increase in sampling density, instead of a slow high-resolution one-shot sampling. State-of-the-art sequential design of experiment methods are point-based and are often used to set up experiments both in virtual (simulation) as well as real-world (measurement) environments. In contrast to virtual experimentation, physical measurements require movement of a sensor probe through the measurement space. In these cases, the algorithm not only needs to optimize the sample locations and order but also the path to be traversed by sampling points along measurement lines. In this work, a sequential line-based sampling method is proposed which aims to gradually increase the sampling density across the entire measurement space while minimizing the overall path length. The algorithm is illustrated on a 2D and 3D unit space as well as a complex 3D space and the effectiveness is validated on an engineering measurement use-case. A computer code implementation of the algorithm is provided as an open-source toolbox. © Springer-Verlag London Ltd., part of Springer Nature 2018 |
| abstractGer |
Abstract Measurements in 2D or 3D spaces are ubiquitous among many fields of science and engineering. Often, data samples are gathered via autonomous robots or drones. The path through the measurement space and the location of the samples is traditionally determined upfront using a one-shot design of experiments. However, in certain cases, a sequential approach is preferred. For example, when dealing with a limited sampling budget or when a quick low-resolution overview is desired followed by a steady uniform increase in sampling density, instead of a slow high-resolution one-shot sampling. State-of-the-art sequential design of experiment methods are point-based and are often used to set up experiments both in virtual (simulation) as well as real-world (measurement) environments. In contrast to virtual experimentation, physical measurements require movement of a sensor probe through the measurement space. In these cases, the algorithm not only needs to optimize the sample locations and order but also the path to be traversed by sampling points along measurement lines. In this work, a sequential line-based sampling method is proposed which aims to gradually increase the sampling density across the entire measurement space while minimizing the overall path length. The algorithm is illustrated on a 2D and 3D unit space as well as a complex 3D space and the effectiveness is validated on an engineering measurement use-case. A computer code implementation of the algorithm is provided as an open-source toolbox. © Springer-Verlag London Ltd., part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Measurements in 2D or 3D spaces are ubiquitous among many fields of science and engineering. Often, data samples are gathered via autonomous robots or drones. The path through the measurement space and the location of the samples is traditionally determined upfront using a one-shot design of experiments. However, in certain cases, a sequential approach is preferred. For example, when dealing with a limited sampling budget or when a quick low-resolution overview is desired followed by a steady uniform increase in sampling density, instead of a slow high-resolution one-shot sampling. State-of-the-art sequential design of experiment methods are point-based and are often used to set up experiments both in virtual (simulation) as well as real-world (measurement) environments. In contrast to virtual experimentation, physical measurements require movement of a sensor probe through the measurement space. In these cases, the algorithm not only needs to optimize the sample locations and order but also the path to be traversed by sampling points along measurement lines. In this work, a sequential line-based sampling method is proposed which aims to gradually increase the sampling density across the entire measurement space while minimizing the overall path length. The algorithm is illustrated on a 2D and 3D unit space as well as a complex 3D space and the effectiveness is validated on an engineering measurement use-case. A computer code implementation of the algorithm is provided as an open-source toolbox. © Springer-Verlag London Ltd., part of Springer Nature 2018 |
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| title_short |
ALBATROS: adaptive line-based sampling trajectories for sequential measurements |
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https://dx.doi.org/10.1007/s00366-018-0614-6 |
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true |
| author2 |
van der Herten, Joachim Deschrijver, Dirk Dhaene, Tom |
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van der Herten, Joachim Deschrijver, Dirk Dhaene, Tom |
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| doi_str |
10.1007/s00366-018-0614-6 |
| up_date |
2024-07-04T01:27:37.771Z |
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|
| score |
7.40096 |