Active object perception using Bayesian classifiers and haptic exploration

Abstract To recognise objects using only tactile sensing, humans employ various haptic exploratory procedures (EPs). Because the time, effort, and information acquisition costs of different EPs vary, choosing the best EP for accurate and efficient perception is usually based on prior knowledge or ex...
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Autor*in:	Sun, Teng [verfasserIn] Liu, Hongbin Miao, Zhonghua

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Haptic exploration Gaussian mixture modal Bayesian exploration Active object perception

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Autonomous robots - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 47(2022), 1 vom: 07. Okt., Seite 19-36
Übergeordnetes Werk:	volume:47 ; year:2022 ; number:1 ; day:07 ; month:10 ; pages:19-36

Links:	Volltext

DOI / URN:	10.1007/s10514-022-10065-6

Katalog-ID:	SPR049020811

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allfields	10.1007/s10514-022-10065-6 doi (DE-627)SPR049020811 (SPR)s10514-022-10065-6-e DE-627 ger DE-627 rakwb eng Sun, Teng verfasserin (orcid)0000-0002-9425-4284 aut Active object perception using Bayesian classifiers and haptic exploration 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract To recognise objects using only tactile sensing, humans employ various haptic exploratory procedures (EPs). Because the time, effort, and information acquisition costs of different EPs vary, choosing the best EP for accurate and efficient perception is usually based on prior knowledge or experience, also known as active exploration. An active EP selection algorithm based on a Gaussian mixture modal and Bayesian classifier has been developed to empower robots with similar intelligence. To choose the best EP for the next perception iteration, the information gain and total time cost of all actions required to identify the object are both considered. Six EPs were realised using a designed robotic arm platform, allowing eight features representing the object’s surface and geometric properties to be extracted. To evaluate the algorithm, offline data and real-world experiments were used, with the random method as a comparison. According to the results, the active method outperformed the random method with higher accuracy and in significantly less time. It had an average of weighted information gain of 132.6 and a time cost ratio (spent/total time) of only 0.3. Haptic exploration (dpeaa)DE-He213 Gaussian mixture modal (dpeaa)DE-He213 Bayesian exploration (dpeaa)DE-He213 Active object perception (dpeaa)DE-He213 Liu, Hongbin aut Miao, Zhonghua aut Enthalten in Autonomous robots Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 47(2022), 1 vom: 07. Okt., Seite 19-36 (DE-627)271175591 (DE-600)1478962-0 1573-7527 nnns volume:47 year:2022 number:1 day:07 month:10 pages:19-36 https://dx.doi.org/10.1007/s10514-022-10065-6 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 47 2022 1 07 10 19-36
spelling	10.1007/s10514-022-10065-6 doi (DE-627)SPR049020811 (SPR)s10514-022-10065-6-e DE-627 ger DE-627 rakwb eng Sun, Teng verfasserin (orcid)0000-0002-9425-4284 aut Active object perception using Bayesian classifiers and haptic exploration 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract To recognise objects using only tactile sensing, humans employ various haptic exploratory procedures (EPs). Because the time, effort, and information acquisition costs of different EPs vary, choosing the best EP for accurate and efficient perception is usually based on prior knowledge or experience, also known as active exploration. An active EP selection algorithm based on a Gaussian mixture modal and Bayesian classifier has been developed to empower robots with similar intelligence. To choose the best EP for the next perception iteration, the information gain and total time cost of all actions required to identify the object are both considered. Six EPs were realised using a designed robotic arm platform, allowing eight features representing the object’s surface and geometric properties to be extracted. To evaluate the algorithm, offline data and real-world experiments were used, with the random method as a comparison. According to the results, the active method outperformed the random method with higher accuracy and in significantly less time. It had an average of weighted information gain of 132.6 and a time cost ratio (spent/total time) of only 0.3. Haptic exploration (dpeaa)DE-He213 Gaussian mixture modal (dpeaa)DE-He213 Bayesian exploration (dpeaa)DE-He213 Active object perception (dpeaa)DE-He213 Liu, Hongbin aut Miao, Zhonghua aut Enthalten in Autonomous robots Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 47(2022), 1 vom: 07. Okt., Seite 19-36 (DE-627)271175591 (DE-600)1478962-0 1573-7527 nnns volume:47 year:2022 number:1 day:07 month:10 pages:19-36 https://dx.doi.org/10.1007/s10514-022-10065-6 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 47 2022 1 07 10 19-36
allfields_unstemmed	10.1007/s10514-022-10065-6 doi (DE-627)SPR049020811 (SPR)s10514-022-10065-6-e DE-627 ger DE-627 rakwb eng Sun, Teng verfasserin (orcid)0000-0002-9425-4284 aut Active object perception using Bayesian classifiers and haptic exploration 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract To recognise objects using only tactile sensing, humans employ various haptic exploratory procedures (EPs). Because the time, effort, and information acquisition costs of different EPs vary, choosing the best EP for accurate and efficient perception is usually based on prior knowledge or experience, also known as active exploration. An active EP selection algorithm based on a Gaussian mixture modal and Bayesian classifier has been developed to empower robots with similar intelligence. To choose the best EP for the next perception iteration, the information gain and total time cost of all actions required to identify the object are both considered. Six EPs were realised using a designed robotic arm platform, allowing eight features representing the object’s surface and geometric properties to be extracted. To evaluate the algorithm, offline data and real-world experiments were used, with the random method as a comparison. According to the results, the active method outperformed the random method with higher accuracy and in significantly less time. It had an average of weighted information gain of 132.6 and a time cost ratio (spent/total time) of only 0.3. Haptic exploration (dpeaa)DE-He213 Gaussian mixture modal (dpeaa)DE-He213 Bayesian exploration (dpeaa)DE-He213 Active object perception (dpeaa)DE-He213 Liu, Hongbin aut Miao, Zhonghua aut Enthalten in Autonomous robots Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 47(2022), 1 vom: 07. Okt., Seite 19-36 (DE-627)271175591 (DE-600)1478962-0 1573-7527 nnns volume:47 year:2022 number:1 day:07 month:10 pages:19-36 https://dx.doi.org/10.1007/s10514-022-10065-6 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 47 2022 1 07 10 19-36
allfieldsGer	10.1007/s10514-022-10065-6 doi (DE-627)SPR049020811 (SPR)s10514-022-10065-6-e DE-627 ger DE-627 rakwb eng Sun, Teng verfasserin (orcid)0000-0002-9425-4284 aut Active object perception using Bayesian classifiers and haptic exploration 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract To recognise objects using only tactile sensing, humans employ various haptic exploratory procedures (EPs). Because the time, effort, and information acquisition costs of different EPs vary, choosing the best EP for accurate and efficient perception is usually based on prior knowledge or experience, also known as active exploration. An active EP selection algorithm based on a Gaussian mixture modal and Bayesian classifier has been developed to empower robots with similar intelligence. To choose the best EP for the next perception iteration, the information gain and total time cost of all actions required to identify the object are both considered. Six EPs were realised using a designed robotic arm platform, allowing eight features representing the object’s surface and geometric properties to be extracted. To evaluate the algorithm, offline data and real-world experiments were used, with the random method as a comparison. According to the results, the active method outperformed the random method with higher accuracy and in significantly less time. It had an average of weighted information gain of 132.6 and a time cost ratio (spent/total time) of only 0.3. Haptic exploration (dpeaa)DE-He213 Gaussian mixture modal (dpeaa)DE-He213 Bayesian exploration (dpeaa)DE-He213 Active object perception (dpeaa)DE-He213 Liu, Hongbin aut Miao, Zhonghua aut Enthalten in Autonomous robots Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 47(2022), 1 vom: 07. Okt., Seite 19-36 (DE-627)271175591 (DE-600)1478962-0 1573-7527 nnns volume:47 year:2022 number:1 day:07 month:10 pages:19-36 https://dx.doi.org/10.1007/s10514-022-10065-6 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 47 2022 1 07 10 19-36
allfieldsSound	10.1007/s10514-022-10065-6 doi (DE-627)SPR049020811 (SPR)s10514-022-10065-6-e DE-627 ger DE-627 rakwb eng Sun, Teng verfasserin (orcid)0000-0002-9425-4284 aut Active object perception using Bayesian classifiers and haptic exploration 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract To recognise objects using only tactile sensing, humans employ various haptic exploratory procedures (EPs). Because the time, effort, and information acquisition costs of different EPs vary, choosing the best EP for accurate and efficient perception is usually based on prior knowledge or experience, also known as active exploration. An active EP selection algorithm based on a Gaussian mixture modal and Bayesian classifier has been developed to empower robots with similar intelligence. To choose the best EP for the next perception iteration, the information gain and total time cost of all actions required to identify the object are both considered. Six EPs were realised using a designed robotic arm platform, allowing eight features representing the object’s surface and geometric properties to be extracted. To evaluate the algorithm, offline data and real-world experiments were used, with the random method as a comparison. According to the results, the active method outperformed the random method with higher accuracy and in significantly less time. It had an average of weighted information gain of 132.6 and a time cost ratio (spent/total time) of only 0.3. Haptic exploration (dpeaa)DE-He213 Gaussian mixture modal (dpeaa)DE-He213 Bayesian exploration (dpeaa)DE-He213 Active object perception (dpeaa)DE-He213 Liu, Hongbin aut Miao, Zhonghua aut Enthalten in Autonomous robots Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 47(2022), 1 vom: 07. Okt., Seite 19-36 (DE-627)271175591 (DE-600)1478962-0 1573-7527 nnns volume:47 year:2022 number:1 day:07 month:10 pages:19-36 https://dx.doi.org/10.1007/s10514-022-10065-6 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 47 2022 1 07 10 19-36
language	English
source	Enthalten in Autonomous robots 47(2022), 1 vom: 07. Okt., Seite 19-36 volume:47 year:2022 number:1 day:07 month:10 pages:19-36
sourceStr	Enthalten in Autonomous robots 47(2022), 1 vom: 07. Okt., Seite 19-36 volume:47 year:2022 number:1 day:07 month:10 pages:19-36
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Haptic exploration Gaussian mixture modal Bayesian exploration Active object perception
isfreeaccess_bool	false
container_title	Autonomous robots
authorswithroles_txt_mv	Sun, Teng @@aut@@ Liu, Hongbin @@aut@@ Miao, Zhonghua @@aut@@
publishDateDaySort_date	2022-10-07T00:00:00Z
hierarchy_top_id	271175591
id	SPR049020811
language_de	englisch
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author	Sun, Teng
spellingShingle	Sun, Teng misc Haptic exploration misc Gaussian mixture modal misc Bayesian exploration misc Active object perception Active object perception using Bayesian classifiers and haptic exploration
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topic_title	Active object perception using Bayesian classifiers and haptic exploration Haptic exploration (dpeaa)DE-He213 Gaussian mixture modal (dpeaa)DE-He213 Bayesian exploration (dpeaa)DE-He213 Active object perception (dpeaa)DE-He213
topic	misc Haptic exploration misc Gaussian mixture modal misc Bayesian exploration misc Active object perception
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title_sort	active object perception using bayesian classifiers and haptic exploration
title_auth	Active object perception using Bayesian classifiers and haptic exploration
abstract	Abstract To recognise objects using only tactile sensing, humans employ various haptic exploratory procedures (EPs). Because the time, effort, and information acquisition costs of different EPs vary, choosing the best EP for accurate and efficient perception is usually based on prior knowledge or experience, also known as active exploration. An active EP selection algorithm based on a Gaussian mixture modal and Bayesian classifier has been developed to empower robots with similar intelligence. To choose the best EP for the next perception iteration, the information gain and total time cost of all actions required to identify the object are both considered. Six EPs were realised using a designed robotic arm platform, allowing eight features representing the object’s surface and geometric properties to be extracted. To evaluate the algorithm, offline data and real-world experiments were used, with the random method as a comparison. According to the results, the active method outperformed the random method with higher accuracy and in significantly less time. It had an average of weighted information gain of 132.6 and a time cost ratio (spent/total time) of only 0.3. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract To recognise objects using only tactile sensing, humans employ various haptic exploratory procedures (EPs). Because the time, effort, and information acquisition costs of different EPs vary, choosing the best EP for accurate and efficient perception is usually based on prior knowledge or experience, also known as active exploration. An active EP selection algorithm based on a Gaussian mixture modal and Bayesian classifier has been developed to empower robots with similar intelligence. To choose the best EP for the next perception iteration, the information gain and total time cost of all actions required to identify the object are both considered. Six EPs were realised using a designed robotic arm platform, allowing eight features representing the object’s surface and geometric properties to be extracted. To evaluate the algorithm, offline data and real-world experiments were used, with the random method as a comparison. According to the results, the active method outperformed the random method with higher accuracy and in significantly less time. It had an average of weighted information gain of 132.6 and a time cost ratio (spent/total time) of only 0.3. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract To recognise objects using only tactile sensing, humans employ various haptic exploratory procedures (EPs). Because the time, effort, and information acquisition costs of different EPs vary, choosing the best EP for accurate and efficient perception is usually based on prior knowledge or experience, also known as active exploration. An active EP selection algorithm based on a Gaussian mixture modal and Bayesian classifier has been developed to empower robots with similar intelligence. To choose the best EP for the next perception iteration, the information gain and total time cost of all actions required to identify the object are both considered. Six EPs were realised using a designed robotic arm platform, allowing eight features representing the object’s surface and geometric properties to be extracted. To evaluate the algorithm, offline data and real-world experiments were used, with the random method as a comparison. According to the results, the active method outperformed the random method with higher accuracy and in significantly less time. It had an average of weighted information gain of 132.6 and a time cost ratio (spent/total time) of only 0.3. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Active object perception using Bayesian classifiers and haptic exploration
url	https://dx.doi.org/10.1007/s10514-022-10065-6
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author2	Liu, Hongbin Miao, Zhonghua
author2Str	Liu, Hongbin Miao, Zhonghua
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doi_str	10.1007/s10514-022-10065-6
up_date	2024-07-03T22:51:54.471Z
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Active object perception using Bayesian classifiers and haptic exploration

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