Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network

The paper proposes a novel approach for shape sensing of hyper-redundant robots based on an AHRS IMU sensor network embedded into the structure of the robot. The proposed approach uses the data from the sensor network to directly calculate the kinematic parameters of the robot in modules operational...
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Autor*in:	Ciprian Lapusan [verfasserIn] Olimpiu Hancu [verfasserIn] Ciprian Rad [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	sensor network IMU sensor robot shape sensing

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 22(2022), 1, p 373
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:1, p 373

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s22010373

Katalog-ID:	DOAJ030399505

Internformat


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allfields	10.3390/s22010373 doi (DE-627)DOAJ030399505 (DE-599)DOAJc807ecceb70040e0aab45aa43e7f22ad DE-627 ger DE-627 rakwb eng TP1-1185 Ciprian Lapusan verfasserin aut Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The paper proposes a novel approach for shape sensing of hyper-redundant robots based on an AHRS IMU sensor network embedded into the structure of the robot. The proposed approach uses the data from the sensor network to directly calculate the kinematic parameters of the robot in modules operational space reducing thus the computational time and facilitating implementation of advanced real-time feedback system for shape sensing. In the paper the method is applied for shape sensing and pose estimation of an articulated joint-based hyper-redundant robot with identical 2-DoF modules serially connected. Using a testing method based on HIL techniques the authors validate the computed kinematic model and the computed shape of the robot prototype. A second testing method is used to validate the end effector pose using an external sensory system. The experimental results obtained demonstrate the feasibility of using this type of sensor network and the effectiveness of the proposed shape sensing approach for hyper-redundant robots. sensor network IMU sensor robot shape sensing Chemical technology Olimpiu Hancu verfasserin aut Ciprian Rad verfasserin aut In Sensors MDPI AG, 2003 22(2022), 1, p 373 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:1, p 373 https://doi.org/10.3390/s22010373 kostenfrei https://doaj.org/article/c807ecceb70040e0aab45aa43e7f22ad kostenfrei https://www.mdpi.com/1424-8220/22/1/373 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 1, p 373
spelling	10.3390/s22010373 doi (DE-627)DOAJ030399505 (DE-599)DOAJc807ecceb70040e0aab45aa43e7f22ad DE-627 ger DE-627 rakwb eng TP1-1185 Ciprian Lapusan verfasserin aut Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The paper proposes a novel approach for shape sensing of hyper-redundant robots based on an AHRS IMU sensor network embedded into the structure of the robot. The proposed approach uses the data from the sensor network to directly calculate the kinematic parameters of the robot in modules operational space reducing thus the computational time and facilitating implementation of advanced real-time feedback system for shape sensing. In the paper the method is applied for shape sensing and pose estimation of an articulated joint-based hyper-redundant robot with identical 2-DoF modules serially connected. Using a testing method based on HIL techniques the authors validate the computed kinematic model and the computed shape of the robot prototype. A second testing method is used to validate the end effector pose using an external sensory system. The experimental results obtained demonstrate the feasibility of using this type of sensor network and the effectiveness of the proposed shape sensing approach for hyper-redundant robots. sensor network IMU sensor robot shape sensing Chemical technology Olimpiu Hancu verfasserin aut Ciprian Rad verfasserin aut In Sensors MDPI AG, 2003 22(2022), 1, p 373 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:1, p 373 https://doi.org/10.3390/s22010373 kostenfrei https://doaj.org/article/c807ecceb70040e0aab45aa43e7f22ad kostenfrei https://www.mdpi.com/1424-8220/22/1/373 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 1, p 373
allfields_unstemmed	10.3390/s22010373 doi (DE-627)DOAJ030399505 (DE-599)DOAJc807ecceb70040e0aab45aa43e7f22ad DE-627 ger DE-627 rakwb eng TP1-1185 Ciprian Lapusan verfasserin aut Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The paper proposes a novel approach for shape sensing of hyper-redundant robots based on an AHRS IMU sensor network embedded into the structure of the robot. The proposed approach uses the data from the sensor network to directly calculate the kinematic parameters of the robot in modules operational space reducing thus the computational time and facilitating implementation of advanced real-time feedback system for shape sensing. In the paper the method is applied for shape sensing and pose estimation of an articulated joint-based hyper-redundant robot with identical 2-DoF modules serially connected. Using a testing method based on HIL techniques the authors validate the computed kinematic model and the computed shape of the robot prototype. A second testing method is used to validate the end effector pose using an external sensory system. The experimental results obtained demonstrate the feasibility of using this type of sensor network and the effectiveness of the proposed shape sensing approach for hyper-redundant robots. sensor network IMU sensor robot shape sensing Chemical technology Olimpiu Hancu verfasserin aut Ciprian Rad verfasserin aut In Sensors MDPI AG, 2003 22(2022), 1, p 373 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:1, p 373 https://doi.org/10.3390/s22010373 kostenfrei https://doaj.org/article/c807ecceb70040e0aab45aa43e7f22ad kostenfrei https://www.mdpi.com/1424-8220/22/1/373 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 1, p 373
allfieldsGer	10.3390/s22010373 doi (DE-627)DOAJ030399505 (DE-599)DOAJc807ecceb70040e0aab45aa43e7f22ad DE-627 ger DE-627 rakwb eng TP1-1185 Ciprian Lapusan verfasserin aut Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The paper proposes a novel approach for shape sensing of hyper-redundant robots based on an AHRS IMU sensor network embedded into the structure of the robot. The proposed approach uses the data from the sensor network to directly calculate the kinematic parameters of the robot in modules operational space reducing thus the computational time and facilitating implementation of advanced real-time feedback system for shape sensing. In the paper the method is applied for shape sensing and pose estimation of an articulated joint-based hyper-redundant robot with identical 2-DoF modules serially connected. Using a testing method based on HIL techniques the authors validate the computed kinematic model and the computed shape of the robot prototype. A second testing method is used to validate the end effector pose using an external sensory system. The experimental results obtained demonstrate the feasibility of using this type of sensor network and the effectiveness of the proposed shape sensing approach for hyper-redundant robots. sensor network IMU sensor robot shape sensing Chemical technology Olimpiu Hancu verfasserin aut Ciprian Rad verfasserin aut In Sensors MDPI AG, 2003 22(2022), 1, p 373 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:1, p 373 https://doi.org/10.3390/s22010373 kostenfrei https://doaj.org/article/c807ecceb70040e0aab45aa43e7f22ad kostenfrei https://www.mdpi.com/1424-8220/22/1/373 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 1, p 373
allfieldsSound	10.3390/s22010373 doi (DE-627)DOAJ030399505 (DE-599)DOAJc807ecceb70040e0aab45aa43e7f22ad DE-627 ger DE-627 rakwb eng TP1-1185 Ciprian Lapusan verfasserin aut Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The paper proposes a novel approach for shape sensing of hyper-redundant robots based on an AHRS IMU sensor network embedded into the structure of the robot. The proposed approach uses the data from the sensor network to directly calculate the kinematic parameters of the robot in modules operational space reducing thus the computational time and facilitating implementation of advanced real-time feedback system for shape sensing. In the paper the method is applied for shape sensing and pose estimation of an articulated joint-based hyper-redundant robot with identical 2-DoF modules serially connected. Using a testing method based on HIL techniques the authors validate the computed kinematic model and the computed shape of the robot prototype. A second testing method is used to validate the end effector pose using an external sensory system. The experimental results obtained demonstrate the feasibility of using this type of sensor network and the effectiveness of the proposed shape sensing approach for hyper-redundant robots. sensor network IMU sensor robot shape sensing Chemical technology Olimpiu Hancu verfasserin aut Ciprian Rad verfasserin aut In Sensors MDPI AG, 2003 22(2022), 1, p 373 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:1, p 373 https://doi.org/10.3390/s22010373 kostenfrei https://doaj.org/article/c807ecceb70040e0aab45aa43e7f22ad kostenfrei https://www.mdpi.com/1424-8220/22/1/373 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 1, p 373
language	English
source	In Sensors 22(2022), 1, p 373 volume:22 year:2022 number:1, p 373
sourceStr	In Sensors 22(2022), 1, p 373 volume:22 year:2022 number:1, p 373
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institution	findex.gbv.de
topic_facet	sensor network IMU sensor robot shape sensing Chemical technology
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authorswithroles_txt_mv	Ciprian Lapusan @@aut@@ Olimpiu Hancu @@aut@@ Ciprian Rad @@aut@@
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callnumber-first	T - Technology
author	Ciprian Lapusan
spellingShingle	Ciprian Lapusan misc TP1-1185 misc sensor network misc IMU sensor misc robot shape sensing misc Chemical technology Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network
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topic_title	TP1-1185 Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network sensor network IMU sensor robot shape sensing
topic	misc TP1-1185 misc sensor network misc IMU sensor misc robot shape sensing misc Chemical technology
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title	Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network
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title_sort	shape sensing of hyper-redundant robots using an ahrs imu sensor network
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title_auth	Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network
abstract	The paper proposes a novel approach for shape sensing of hyper-redundant robots based on an AHRS IMU sensor network embedded into the structure of the robot. The proposed approach uses the data from the sensor network to directly calculate the kinematic parameters of the robot in modules operational space reducing thus the computational time and facilitating implementation of advanced real-time feedback system for shape sensing. In the paper the method is applied for shape sensing and pose estimation of an articulated joint-based hyper-redundant robot with identical 2-DoF modules serially connected. Using a testing method based on HIL techniques the authors validate the computed kinematic model and the computed shape of the robot prototype. A second testing method is used to validate the end effector pose using an external sensory system. The experimental results obtained demonstrate the feasibility of using this type of sensor network and the effectiveness of the proposed shape sensing approach for hyper-redundant robots.
abstractGer	The paper proposes a novel approach for shape sensing of hyper-redundant robots based on an AHRS IMU sensor network embedded into the structure of the robot. The proposed approach uses the data from the sensor network to directly calculate the kinematic parameters of the robot in modules operational space reducing thus the computational time and facilitating implementation of advanced real-time feedback system for shape sensing. In the paper the method is applied for shape sensing and pose estimation of an articulated joint-based hyper-redundant robot with identical 2-DoF modules serially connected. Using a testing method based on HIL techniques the authors validate the computed kinematic model and the computed shape of the robot prototype. A second testing method is used to validate the end effector pose using an external sensory system. The experimental results obtained demonstrate the feasibility of using this type of sensor network and the effectiveness of the proposed shape sensing approach for hyper-redundant robots.
abstract_unstemmed	The paper proposes a novel approach for shape sensing of hyper-redundant robots based on an AHRS IMU sensor network embedded into the structure of the robot. The proposed approach uses the data from the sensor network to directly calculate the kinematic parameters of the robot in modules operational space reducing thus the computational time and facilitating implementation of advanced real-time feedback system for shape sensing. In the paper the method is applied for shape sensing and pose estimation of an articulated joint-based hyper-redundant robot with identical 2-DoF modules serially connected. Using a testing method based on HIL techniques the authors validate the computed kinematic model and the computed shape of the robot prototype. A second testing method is used to validate the end effector pose using an external sensory system. The experimental results obtained demonstrate the feasibility of using this type of sensor network and the effectiveness of the proposed shape sensing approach for hyper-redundant robots.
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container_issue	1, p 373
title_short	Shape Sensing of Hyper-Redundant Robots Using an AHRS IMU Sensor Network
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