Grounding rod hanging and removing robot with hand-eye self-calibration capability in substation
In this paper, a robot system for hanging and removing grounding rods is designed by integrating 3D recognition technology, hand-eye self-calibration technology, and automatic operation technology. Specifically, a novel hand-eye self-calibration algorithm is proposed that only uses common objects in...
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| Vydáno v: | Complex & intelligent systems Ročník 10; číslo 5; s. 6491 - 6507 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Springer International Publishing
01.10.2024
Springer Nature B.V Springer |
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| ISSN: | 2199-4536, 2198-6053 |
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| Abstract | In this paper, a robot system for hanging and removing grounding rods is designed by integrating 3D recognition technology, hand-eye self-calibration technology, and automatic operation technology. Specifically, a novel hand-eye self-calibration algorithm is proposed that only uses common objects in the actual scene, which differs from traditional robot hand-eye calibration in that it requires a dedicated calibration board to assist with offline completion. Addressing the problem that existing self-calibration methods cannot be optimized as a whole, resulting in low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Experimental verifications are conducted using both public dataset and practical robot system. In the dataset experiment, our method demonstrates superior accuracy and robustness compared to existing self-calibration methods. Furthermore, the practical robot platform experiment confirms the feasibility and efficacy of our approach across various wind speeds and lighting conditions. |
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| AbstractList | In this paper, a robot system for hanging and removing grounding rods is designed by integrating 3D recognition technology, hand-eye self-calibration technology, and automatic operation technology. Specifically, a novel hand-eye self-calibration algorithm is proposed that only uses common objects in the actual scene, which differs from traditional robot hand-eye calibration in that it requires a dedicated calibration board to assist with offline completion. Addressing the problem that existing self-calibration methods cannot be optimized as a whole, resulting in low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Experimental verifications are conducted using both public dataset and practical robot system. In the dataset experiment, our method demonstrates superior accuracy and robustness compared to existing self-calibration methods. Furthermore, the practical robot platform experiment confirms the feasibility and efficacy of our approach across various wind speeds and lighting conditions. Abstract In this paper, a robot system for hanging and removing grounding rods is designed by integrating 3D recognition technology, hand-eye self-calibration technology, and automatic operation technology. Specifically, a novel hand-eye self-calibration algorithm is proposed that only uses common objects in the actual scene, which differs from traditional robot hand-eye calibration in that it requires a dedicated calibration board to assist with offline completion. Addressing the problem that existing self-calibration methods cannot be optimized as a whole, resulting in low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Experimental verifications are conducted using both public dataset and practical robot system. In the dataset experiment, our method demonstrates superior accuracy and robustness compared to existing self-calibration methods. Furthermore, the practical robot platform experiment confirms the feasibility and efficacy of our approach across various wind speeds and lighting conditions. |
| Author | Liu, Kaibo Lin, Yunhan Wang, Jiahui Min, Huasong |
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| References | Nakashima M, Yano K, Maruyama Y, et al. (1995) The hot line work robot sytem "Phase II" and its human-robot interface “Mos” [J]. In: Proc of the IEEE/RSJ Inter Conf on Intelligent Robots and Systems, vol 2, pp. 116–123. Liu K, Zheng W, Min H, et al. Multi-stage objective function optimized hand-eye self-calibration of robot in autonomous environment[C]. In: International Conference on Bio-Inspired Computing: Theories and Applications. Singapore: Springer Nature Singapore, 144–157. https://doi.org/10.1007/978-981-99-1549-1_12 Tian Y, Xu W, Zou W, Wang Y, Lu S (2019) Development of automatic grounding wire working robot for substation. In: Knight K, Zhang C, Holmes G, Zhang ML (eds) Artificial intelligence. ICAI 2019. Communications in Computer and Information Science, vol 1001. Springer, Singapore. https://doi.org/10.1007/978-981-32-9298-7_21 ZhangYQiuZZhangXA simultaneous optimization method of calibration and measurement for a typical hand-eye positioning systemIEEE Trans Instrum Meas20217011110.1109/tim.2020.3013308 Yunhan L, Tong L, Yi Z, Shuangyuan L, Huasong M (2023) LA-Net: LSTM and attention based point cloud down-sampling and its application[J]. Measur Control: 1–17 Montambault S, Pouliot N (2014) Hydro-Québec's power line robotics program: 15 years of development, implementation and partnerships[C]. In: Proceedings of the 2014 3rd International Conference on Applied Robotics for the Power Industry, Foz do Iguacu, Brazil: IEEE, 1–6 Maruyama Y, Maki K, Mori H (1993) A hot-line manipulator remotely operated by the operator on the ground[C]. In: Proceedings of ESMO'93. IEEE 6th International Conference on Transmission and Distribution Construction and Live-Line Maintenance, Las Vegas, NV, USA: IEEE, pp. 437–444 Takaoka K, Yokoyama K, Wakisako H, et al. (2002) Development of the fully-automatic live-line maintenance robot-Phase III[C]. In: IEEE International Symposium on Assembly and Task Planning. IEEE. https://doi.org/10.1109/ISATP.2001.929071 Heller J, Havlena M, Pajdla T (2012) A branch-and-bound algorithm for globally optimal hand-eye calibration. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1608–1615. IEEE, New York. https://doi.org/10.1109/cvpr.2012.6247853 WangYSongHDuYA complete analytical solution to hand-eye calibration using quaternions and eigenvector-eigenvalue identityJ Intell Robot Syst20231095410.1007/0846-023-01983-3 Rublee E, Rabaud V, Konolige K, et al. (2011) ORB: an efficient alternative to SIFT or SURF. In: 2011 International Conference on Computer Vision, pp. 2564–2571. IEEE, New York. https://doi.org/10.1109/iccv.2011.6126544 Qingyu P, Xiaoyi S, Danyong L, Ruolan L, Li F (2022) Design of a robot for automatically hanging ground wires in substations. In: 2022 6th International Conference on Electronic Information Technology and Computer Engineering (EITCE 2022), October 21–23, 2022, Xiamen, China. ACM, New York, NY, USA, pp. 9. https://doi.org/10.1145/3573428.3573543 Andreff N, Horaud R, Espiau B (1999) On-line hand-eye calibration. In: Second IEEE International Conference on 3-D Digital Imaging and Modeling, pp. 430–436. IEEE, New York. https://doi.org/10.1109/im.1999.805374 PalliGPirozziSIndoviniMAutomatized switchgear wiring: an outline of the WIRES Experiment Results[M]2020ChamSpringer107123 BianJWLinWYLiuYGMS: grid-based motion statistics for fast, ultra-robust feature correspondence[J]Int J Comput Vision2020128615801593410239710.1007/s11263-019-01280-3 ShiuYCAhmadSCalibration of wrist-mounted robotic sensors by solving homogeneous transform equations of the form AX = XBIEEE Trans Robot Autom198951162910.1109/70.88014 Allan J F, Lavoie S, Reiher S, et al. (2010) Climbing and pole line hardware installation robot for construction of distribution lines[C]. In: 2010 1st International Conference on Applied Robotics for the Power Industry. IEEE, 1–5 DaniilidisKHand-eye calibration using dual quaternionsInt J Robot Res199918328629810.1177/02783649922066213 Zhi X, Schwertfeger S (2017) Simultaneous hand-eye calibration and reconstruction. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1470–1477. IEEE, New York. https://doi.org/10.1109/iros.2017.8205949l Li Q (2020) Research on improvement of grounding wire for outdoor line hook. In: IOP Conference Series: Earth and Environmental Science 440 CamposCElviraRRodríguezJJGOrb-sla: an accurate open-source library for visual, visual–inertial, and multimap slam[J]IEEE Trans Rob20213761874189010.1109/TRO.2021.3075644 1492_CR8 1492_CR9 1492_CR6 1492_CR4 1492_CR5 1492_CR2 1492_CR3 G Palli (1492_CR7) 2020 1492_CR15 Y Wang (1492_CR16) 2023; 109 1492_CR1 1492_CR13 1492_CR14 YC Shiu (1492_CR10) 1989; 5 K Daniilidis (1492_CR12) 1999; 18 Y Zhang (1492_CR11) 2021; 70 1492_CR19 C Campos (1492_CR20) 2021; 37 1492_CR17 1492_CR18 JW Bian (1492_CR21) 2020; 128 |
| References_xml | – reference: Nakashima M, Yano K, Maruyama Y, et al. (1995) The hot line work robot sytem "Phase II" and its human-robot interface “Mos” [J]. In: Proc of the IEEE/RSJ Inter Conf on Intelligent Robots and Systems, vol 2, pp. 116–123. – reference: Qingyu P, Xiaoyi S, Danyong L, Ruolan L, Li F (2022) Design of a robot for automatically hanging ground wires in substations. In: 2022 6th International Conference on Electronic Information Technology and Computer Engineering (EITCE 2022), October 21–23, 2022, Xiamen, China. ACM, New York, NY, USA, pp. 9. https://doi.org/10.1145/3573428.3573543 – reference: CamposCElviraRRodríguezJJGOrb-sla: an accurate open-source library for visual, visual–inertial, and multimap slam[J]IEEE Trans Rob20213761874189010.1109/TRO.2021.3075644 – reference: Li Q (2020) Research on improvement of grounding wire for outdoor line hook. In: IOP Conference Series: Earth and Environmental Science 440 – reference: ShiuYCAhmadSCalibration of wrist-mounted robotic sensors by solving homogeneous transform equations of the form AX = XBIEEE Trans Robot Autom198951162910.1109/70.88014 – reference: DaniilidisKHand-eye calibration using dual quaternionsInt J Robot Res199918328629810.1177/02783649922066213 – reference: Liu K, Zheng W, Min H, et al. Multi-stage objective function optimized hand-eye self-calibration of robot in autonomous environment[C]. In: International Conference on Bio-Inspired Computing: Theories and Applications. Singapore: Springer Nature Singapore, 144–157. https://doi.org/10.1007/978-981-99-1549-1_12 – reference: Maruyama Y, Maki K, Mori H (1993) A hot-line manipulator remotely operated by the operator on the ground[C]. In: Proceedings of ESMO'93. IEEE 6th International Conference on Transmission and Distribution Construction and Live-Line Maintenance, Las Vegas, NV, USA: IEEE, pp. 437–444 – reference: WangYSongHDuYA complete analytical solution to hand-eye calibration using quaternions and eigenvector-eigenvalue identityJ Intell Robot Syst20231095410.1007/0846-023-01983-3 – reference: PalliGPirozziSIndoviniMAutomatized switchgear wiring: an outline of the WIRES Experiment Results[M]2020ChamSpringer107123 – reference: Andreff N, Horaud R, Espiau B (1999) On-line hand-eye calibration. In: Second IEEE International Conference on 3-D Digital Imaging and Modeling, pp. 430–436. IEEE, New York. https://doi.org/10.1109/im.1999.805374 – reference: Allan J F, Lavoie S, Reiher S, et al. (2010) Climbing and pole line hardware installation robot for construction of distribution lines[C]. In: 2010 1st International Conference on Applied Robotics for the Power Industry. IEEE, 1–5 – reference: BianJWLinWYLiuYGMS: grid-based motion statistics for fast, ultra-robust feature correspondence[J]Int J Comput Vision2020128615801593410239710.1007/s11263-019-01280-3 – reference: Tian Y, Xu W, Zou W, Wang Y, Lu S (2019) Development of automatic grounding wire working robot for substation. In: Knight K, Zhang C, Holmes G, Zhang ML (eds) Artificial intelligence. ICAI 2019. Communications in Computer and Information Science, vol 1001. Springer, Singapore. https://doi.org/10.1007/978-981-32-9298-7_21 – reference: Takaoka K, Yokoyama K, Wakisako H, et al. (2002) Development of the fully-automatic live-line maintenance robot-Phase III[C]. In: IEEE International Symposium on Assembly and Task Planning. IEEE. https://doi.org/10.1109/ISATP.2001.929071 – reference: Montambault S, Pouliot N (2014) Hydro-Québec's power line robotics program: 15 years of development, implementation and partnerships[C]. In: Proceedings of the 2014 3rd International Conference on Applied Robotics for the Power Industry, Foz do Iguacu, Brazil: IEEE, 1–6 – reference: ZhangYQiuZZhangXA simultaneous optimization method of calibration and measurement for a typical hand-eye positioning systemIEEE Trans Instrum Meas20217011110.1109/tim.2020.3013308 – reference: Zhi X, Schwertfeger S (2017) Simultaneous hand-eye calibration and reconstruction. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1470–1477. IEEE, New York. https://doi.org/10.1109/iros.2017.8205949l – reference: Rublee E, Rabaud V, Konolige K, et al. (2011) ORB: an efficient alternative to SIFT or SURF. In: 2011 International Conference on Computer Vision, pp. 2564–2571. IEEE, New York. https://doi.org/10.1109/iccv.2011.6126544 – reference: Heller J, Havlena M, Pajdla T (2012) A branch-and-bound algorithm for globally optimal hand-eye calibration. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1608–1615. IEEE, New York. https://doi.org/10.1109/cvpr.2012.6247853 – reference: Yunhan L, Tong L, Yi Z, Shuangyuan L, Huasong M (2023) LA-Net: LSTM and attention based point cloud down-sampling and its application[J]. 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| Snippet | In this paper, a robot system for hanging and removing grounding rods is designed by integrating 3D recognition technology, hand-eye self-calibration... Abstract In this paper, a robot system for hanging and removing grounding rods is designed by integrating 3D recognition technology, hand-eye self-calibration... |
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| SubjectTerms | Algorithms Calibration Complexity Computational Intelligence Data Structures and Information Theory Datasets End effectors Engineering Error correction Feature extraction Grounding rod hanging and removing robot Hand-eye self-calibration Matching Multi-stage objective function optimization Optimization Original Article Robots Self calibration Substations The minimization of reprojection error Wind speed |
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| Title | Grounding rod hanging and removing robot with hand-eye self-calibration capability in substation |
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