Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture
Abstract This paper proposes a design of a state estimator for tracking and predicting the object trajectory for the manipulation using a dexterous multi-fingered Delta robot. The observations of the object state acquired from the cameras (Basler), in the real-time scenario. Initially, pixels are re...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kansal, Sachin [verfasserIn] Kumar, Rajesh [verfasserIn] Mukherjee, Sudipto [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 80(2021), 8 vom: 07. Jan., Seite 11865-11886 |
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| Übergeordnetes Werk: |
volume:80 ; year:2021 ; number:8 ; day:07 ; month:01 ; pages:11865-11886 |
| Links: |
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| DOI / URN: |
10.1007/s11042-020-09937-9 |
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| Katalog-ID: |
SPR04378190X |
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| 245 | 1 | 0 | |a Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture |
| 264 | 1 | |c 2021 | |
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| 520 | |a Abstract This paper proposes a design of a state estimator for tracking and predicting the object trajectory for the manipulation using a dexterous multi-fingered Delta robot. The observations of the object state acquired from the cameras (Basler), in the real-time scenario. Initially, pixels are removed that corresponds to the background pixels using a mixture of Gaussian algorithms. Secondly, the color invariant approach is implemented as a Hough transform. The same is used for the tracking of the object. This results in the color invariant thresholding to filter the region of interest. As the successive frames have some noise, morphological operations have also performed in to remove if any present outlier. After removing the noise from the frame, estimating the object center followed by velocity estimation done using the k-means clustering. Kalman predictor is used for the prediction of the future state(s) using the current state and known system dynamics. The catching strategy of the object using the Delta robot-based multi-fingered architecture is also discussed. Different trajectories and objects are provided for the catching of the object. | ||
| 650 | 4 | |a RGB sensor |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Kalman predictor |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Delta robots |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a K-means clustering |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a ArUco library |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Catching |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Visual tracking |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Feature tracking |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Calibration |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Kumar, Rajesh |e verfasserin |4 aut | |
| 700 | 1 | |a Mukherjee, Sudipto |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Multimedia tools and applications |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 |g 80(2021), 8 vom: 07. Jan., Seite 11865-11886 |w (DE-627)27135030X |w (DE-600)1479928-5 |x 1573-7721 |7 nnns |
| 773 | 1 | 8 | |g volume:80 |g year:2021 |g number:8 |g day:07 |g month:01 |g pages:11865-11886 |
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10.1007/s11042-020-09937-9 doi (DE-627)SPR04378190X (DE-599)SPRs11042-020-09937-9-e (SPR)s11042-020-09937-9-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Kansal, Sachin verfasserin aut Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes a design of a state estimator for tracking and predicting the object trajectory for the manipulation using a dexterous multi-fingered Delta robot. The observations of the object state acquired from the cameras (Basler), in the real-time scenario. Initially, pixels are removed that corresponds to the background pixels using a mixture of Gaussian algorithms. Secondly, the color invariant approach is implemented as a Hough transform. The same is used for the tracking of the object. This results in the color invariant thresholding to filter the region of interest. As the successive frames have some noise, morphological operations have also performed in to remove if any present outlier. After removing the noise from the frame, estimating the object center followed by velocity estimation done using the k-means clustering. Kalman predictor is used for the prediction of the future state(s) using the current state and known system dynamics. The catching strategy of the object using the Delta robot-based multi-fingered architecture is also discussed. Different trajectories and objects are provided for the catching of the object. RGB sensor (dpeaa)DE-He213 Kalman predictor (dpeaa)DE-He213 Delta robots (dpeaa)DE-He213 K-means clustering (dpeaa)DE-He213 ArUco library (dpeaa)DE-He213 Catching (dpeaa)DE-He213 Visual tracking (dpeaa)DE-He213 Feature tracking (dpeaa)DE-He213 Calibration (dpeaa)DE-He213 Kumar, Rajesh verfasserin aut Mukherjee, Sudipto verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2021), 8 vom: 07. Jan., Seite 11865-11886 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2021 number:8 day:07 month:01 pages:11865-11886 https://dx.doi.org/10.1007/s11042-020-09937-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_2008 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_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_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 54.87 ASE AR 80 2021 8 07 01 11865-11886 |
| spelling |
10.1007/s11042-020-09937-9 doi (DE-627)SPR04378190X (DE-599)SPRs11042-020-09937-9-e (SPR)s11042-020-09937-9-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Kansal, Sachin verfasserin aut Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes a design of a state estimator for tracking and predicting the object trajectory for the manipulation using a dexterous multi-fingered Delta robot. The observations of the object state acquired from the cameras (Basler), in the real-time scenario. Initially, pixels are removed that corresponds to the background pixels using a mixture of Gaussian algorithms. Secondly, the color invariant approach is implemented as a Hough transform. The same is used for the tracking of the object. This results in the color invariant thresholding to filter the region of interest. As the successive frames have some noise, morphological operations have also performed in to remove if any present outlier. After removing the noise from the frame, estimating the object center followed by velocity estimation done using the k-means clustering. Kalman predictor is used for the prediction of the future state(s) using the current state and known system dynamics. The catching strategy of the object using the Delta robot-based multi-fingered architecture is also discussed. Different trajectories and objects are provided for the catching of the object. RGB sensor (dpeaa)DE-He213 Kalman predictor (dpeaa)DE-He213 Delta robots (dpeaa)DE-He213 K-means clustering (dpeaa)DE-He213 ArUco library (dpeaa)DE-He213 Catching (dpeaa)DE-He213 Visual tracking (dpeaa)DE-He213 Feature tracking (dpeaa)DE-He213 Calibration (dpeaa)DE-He213 Kumar, Rajesh verfasserin aut Mukherjee, Sudipto verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2021), 8 vom: 07. Jan., Seite 11865-11886 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2021 number:8 day:07 month:01 pages:11865-11886 https://dx.doi.org/10.1007/s11042-020-09937-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_2008 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_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_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 54.87 ASE AR 80 2021 8 07 01 11865-11886 |
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10.1007/s11042-020-09937-9 doi (DE-627)SPR04378190X (DE-599)SPRs11042-020-09937-9-e (SPR)s11042-020-09937-9-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Kansal, Sachin verfasserin aut Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes a design of a state estimator for tracking and predicting the object trajectory for the manipulation using a dexterous multi-fingered Delta robot. The observations of the object state acquired from the cameras (Basler), in the real-time scenario. Initially, pixels are removed that corresponds to the background pixels using a mixture of Gaussian algorithms. Secondly, the color invariant approach is implemented as a Hough transform. The same is used for the tracking of the object. This results in the color invariant thresholding to filter the region of interest. As the successive frames have some noise, morphological operations have also performed in to remove if any present outlier. After removing the noise from the frame, estimating the object center followed by velocity estimation done using the k-means clustering. Kalman predictor is used for the prediction of the future state(s) using the current state and known system dynamics. The catching strategy of the object using the Delta robot-based multi-fingered architecture is also discussed. Different trajectories and objects are provided for the catching of the object. RGB sensor (dpeaa)DE-He213 Kalman predictor (dpeaa)DE-He213 Delta robots (dpeaa)DE-He213 K-means clustering (dpeaa)DE-He213 ArUco library (dpeaa)DE-He213 Catching (dpeaa)DE-He213 Visual tracking (dpeaa)DE-He213 Feature tracking (dpeaa)DE-He213 Calibration (dpeaa)DE-He213 Kumar, Rajesh verfasserin aut Mukherjee, Sudipto verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2021), 8 vom: 07. Jan., Seite 11865-11886 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2021 number:8 day:07 month:01 pages:11865-11886 https://dx.doi.org/10.1007/s11042-020-09937-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_2008 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_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_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 54.87 ASE AR 80 2021 8 07 01 11865-11886 |
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10.1007/s11042-020-09937-9 doi (DE-627)SPR04378190X (DE-599)SPRs11042-020-09937-9-e (SPR)s11042-020-09937-9-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Kansal, Sachin verfasserin aut Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes a design of a state estimator for tracking and predicting the object trajectory for the manipulation using a dexterous multi-fingered Delta robot. The observations of the object state acquired from the cameras (Basler), in the real-time scenario. Initially, pixels are removed that corresponds to the background pixels using a mixture of Gaussian algorithms. Secondly, the color invariant approach is implemented as a Hough transform. The same is used for the tracking of the object. This results in the color invariant thresholding to filter the region of interest. As the successive frames have some noise, morphological operations have also performed in to remove if any present outlier. After removing the noise from the frame, estimating the object center followed by velocity estimation done using the k-means clustering. Kalman predictor is used for the prediction of the future state(s) using the current state and known system dynamics. The catching strategy of the object using the Delta robot-based multi-fingered architecture is also discussed. Different trajectories and objects are provided for the catching of the object. RGB sensor (dpeaa)DE-He213 Kalman predictor (dpeaa)DE-He213 Delta robots (dpeaa)DE-He213 K-means clustering (dpeaa)DE-He213 ArUco library (dpeaa)DE-He213 Catching (dpeaa)DE-He213 Visual tracking (dpeaa)DE-He213 Feature tracking (dpeaa)DE-He213 Calibration (dpeaa)DE-He213 Kumar, Rajesh verfasserin aut Mukherjee, Sudipto verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2021), 8 vom: 07. Jan., Seite 11865-11886 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2021 number:8 day:07 month:01 pages:11865-11886 https://dx.doi.org/10.1007/s11042-020-09937-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_2008 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_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_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 54.87 ASE AR 80 2021 8 07 01 11865-11886 |
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10.1007/s11042-020-09937-9 doi (DE-627)SPR04378190X (DE-599)SPRs11042-020-09937-9-e (SPR)s11042-020-09937-9-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Kansal, Sachin verfasserin aut Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes a design of a state estimator for tracking and predicting the object trajectory for the manipulation using a dexterous multi-fingered Delta robot. The observations of the object state acquired from the cameras (Basler), in the real-time scenario. Initially, pixels are removed that corresponds to the background pixels using a mixture of Gaussian algorithms. Secondly, the color invariant approach is implemented as a Hough transform. The same is used for the tracking of the object. This results in the color invariant thresholding to filter the region of interest. As the successive frames have some noise, morphological operations have also performed in to remove if any present outlier. After removing the noise from the frame, estimating the object center followed by velocity estimation done using the k-means clustering. Kalman predictor is used for the prediction of the future state(s) using the current state and known system dynamics. The catching strategy of the object using the Delta robot-based multi-fingered architecture is also discussed. Different trajectories and objects are provided for the catching of the object. RGB sensor (dpeaa)DE-He213 Kalman predictor (dpeaa)DE-He213 Delta robots (dpeaa)DE-He213 K-means clustering (dpeaa)DE-He213 ArUco library (dpeaa)DE-He213 Catching (dpeaa)DE-He213 Visual tracking (dpeaa)DE-He213 Feature tracking (dpeaa)DE-He213 Calibration (dpeaa)DE-He213 Kumar, Rajesh verfasserin aut Mukherjee, Sudipto verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2021), 8 vom: 07. Jan., Seite 11865-11886 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2021 number:8 day:07 month:01 pages:11865-11886 https://dx.doi.org/10.1007/s11042-020-09937-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_2008 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_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_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 54.87 ASE AR 80 2021 8 07 01 11865-11886 |
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Enthalten in Multimedia tools and applications 80(2021), 8 vom: 07. Jan., Seite 11865-11886 volume:80 year:2021 number:8 day:07 month:01 pages:11865-11886 |
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RGB sensor Kalman predictor Delta robots K-means clustering ArUco library Catching Visual tracking Feature tracking Calibration |
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Multimedia tools and applications |
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Kansal, Sachin @@aut@@ Kumar, Rajesh @@aut@@ Mukherjee, Sudipto @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR04378190X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111024809.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210417s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-020-09937-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR04378190X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)SPRs11042-020-09937-9-e</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11042-020-09937-9-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.87</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kansal, Sachin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper proposes a design of a state estimator for tracking and predicting the object trajectory for the manipulation using a dexterous multi-fingered Delta robot. The observations of the object state acquired from the cameras (Basler), in the real-time scenario. Initially, pixels are removed that corresponds to the background pixels using a mixture of Gaussian algorithms. Secondly, the color invariant approach is implemented as a Hough transform. The same is used for the tracking of the object. This results in the color invariant thresholding to filter the region of interest. As the successive frames have some noise, morphological operations have also performed in to remove if any present outlier. After removing the noise from the frame, estimating the object center followed by velocity estimation done using the k-means clustering. Kalman predictor is used for the prediction of the future state(s) using the current state and known system dynamics. The catching strategy of the object using the Delta robot-based multi-fingered architecture is also discussed. Different trajectories and objects are provided for the catching of the object.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">RGB sensor</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Kalman predictor</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Delta robots</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">K-means clustering</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ArUco library</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Catching</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Visual tracking</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature tracking</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Calibration</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kumar, Rajesh</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mukherjee, Sudipto</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995</subfield><subfield code="g">80(2021), 8 vom: 07. 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Kansal, Sachin |
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Kansal, Sachin ddc 070 bkl 54.87 misc RGB sensor misc Kalman predictor misc Delta robots misc K-means clustering misc ArUco library misc Catching misc Visual tracking misc Feature tracking misc Calibration Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture |
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070 004 ASE 54.87 bkl Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture RGB sensor (dpeaa)DE-He213 Kalman predictor (dpeaa)DE-He213 Delta robots (dpeaa)DE-He213 K-means clustering (dpeaa)DE-He213 ArUco library (dpeaa)DE-He213 Catching (dpeaa)DE-He213 Visual tracking (dpeaa)DE-He213 Feature tracking (dpeaa)DE-He213 Calibration (dpeaa)DE-He213 |
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ddc 070 bkl 54.87 misc RGB sensor misc Kalman predictor misc Delta robots misc K-means clustering misc ArUco library misc Catching misc Visual tracking misc Feature tracking misc Calibration |
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Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture |
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Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture |
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color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture |
| title_auth |
Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture |
| abstract |
Abstract This paper proposes a design of a state estimator for tracking and predicting the object trajectory for the manipulation using a dexterous multi-fingered Delta robot. The observations of the object state acquired from the cameras (Basler), in the real-time scenario. Initially, pixels are removed that corresponds to the background pixels using a mixture of Gaussian algorithms. Secondly, the color invariant approach is implemented as a Hough transform. The same is used for the tracking of the object. This results in the color invariant thresholding to filter the region of interest. As the successive frames have some noise, morphological operations have also performed in to remove if any present outlier. After removing the noise from the frame, estimating the object center followed by velocity estimation done using the k-means clustering. Kalman predictor is used for the prediction of the future state(s) using the current state and known system dynamics. The catching strategy of the object using the Delta robot-based multi-fingered architecture is also discussed. Different trajectories and objects are provided for the catching of the object. |
| abstractGer |
Abstract This paper proposes a design of a state estimator for tracking and predicting the object trajectory for the manipulation using a dexterous multi-fingered Delta robot. The observations of the object state acquired from the cameras (Basler), in the real-time scenario. Initially, pixels are removed that corresponds to the background pixels using a mixture of Gaussian algorithms. Secondly, the color invariant approach is implemented as a Hough transform. The same is used for the tracking of the object. This results in the color invariant thresholding to filter the region of interest. As the successive frames have some noise, morphological operations have also performed in to remove if any present outlier. After removing the noise from the frame, estimating the object center followed by velocity estimation done using the k-means clustering. Kalman predictor is used for the prediction of the future state(s) using the current state and known system dynamics. The catching strategy of the object using the Delta robot-based multi-fingered architecture is also discussed. Different trajectories and objects are provided for the catching of the object. |
| abstract_unstemmed |
Abstract This paper proposes a design of a state estimator for tracking and predicting the object trajectory for the manipulation using a dexterous multi-fingered Delta robot. The observations of the object state acquired from the cameras (Basler), in the real-time scenario. Initially, pixels are removed that corresponds to the background pixels using a mixture of Gaussian algorithms. Secondly, the color invariant approach is implemented as a Hough transform. The same is used for the tracking of the object. This results in the color invariant thresholding to filter the region of interest. As the successive frames have some noise, morphological operations have also performed in to remove if any present outlier. After removing the noise from the frame, estimating the object center followed by velocity estimation done using the k-means clustering. Kalman predictor is used for the prediction of the future state(s) using the current state and known system dynamics. The catching strategy of the object using the Delta robot-based multi-fingered architecture is also discussed. Different trajectories and objects are provided for the catching of the object. |
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Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture |
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https://dx.doi.org/10.1007/s11042-020-09937-9 |
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Kumar, Rajesh Mukherjee, Sudipto |
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10.1007/s11042-020-09937-9 |
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2024-07-03T20:51:22.896Z |
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| score |
7.403097 |