Adaptive obstacle avoidance in path planning of collaborative robots for dynamic manufacturing
Abstract Gaussian Mixture Model (GMM)/Gaussian Mixture Regression (GMR) is a paramount technology of learning from demonstrations to perform human–robotic collaboration. However, GMM/GMR is ineffective in supporting dynamic manufacturing where random obstacles in the applications generate potential...
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| Autor*in: |
Hu, Yudie [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of intelligent manufacturing - Springer US, 1990, 34(2021), 2 vom: 18. Aug., Seite 789-807 |
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| Übergeordnetes Werk: |
volume:34 ; year:2021 ; number:2 ; day:18 ; month:08 ; pages:789-807 |
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| DOI / URN: |
10.1007/s10845-021-01825-9 |
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| 520 | |a Abstract Gaussian Mixture Model (GMM)/Gaussian Mixture Regression (GMR) is a paramount technology of learning from demonstrations to perform human–robotic collaboration. However, GMM/GMR is ineffective in supporting dynamic manufacturing where random obstacles in the applications generate potential safety concerns. In this paper, an improved GMM/GMR-based approach for collaborative robots (cobots) path planning is designed to achieve adaptive obstacle avoidance in dynamic manufacturing. The approach is realised via three innovative steps: (i) new quality assessment criteria for a cobot’s paths produced by GMM/GMR are defined; (ii) based on the criteria, demonstrations and parameters of GMM/GMR are adaptively amended to eliminate collisions and safety issues between a cobot and obstacles; (iii) a fruit fly optimisation algorithm is incorporated into GMM/GMR to expedite the computational efficiency. Case studies with different complexities are used for approach validation in terms of feature retention from demonstrations, regression path smoothness and obstacle avoidance effectiveness. Results of the case studies and benchmarking analyses show that the approach is robust and efficient for dynamic manufacturing applications. | ||
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10.1007/s10845-021-01825-9 doi (DE-627)OLC2133449922 (DE-He213)s10845-021-01825-9-p DE-627 ger DE-627 rakwb eng 620 004 VZ Hu, Yudie verfasserin aut Adaptive obstacle avoidance in path planning of collaborative robots for dynamic manufacturing 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Gaussian Mixture Model (GMM)/Gaussian Mixture Regression (GMR) is a paramount technology of learning from demonstrations to perform human–robotic collaboration. However, GMM/GMR is ineffective in supporting dynamic manufacturing where random obstacles in the applications generate potential safety concerns. In this paper, an improved GMM/GMR-based approach for collaborative robots (cobots) path planning is designed to achieve adaptive obstacle avoidance in dynamic manufacturing. The approach is realised via three innovative steps: (i) new quality assessment criteria for a cobot’s paths produced by GMM/GMR are defined; (ii) based on the criteria, demonstrations and parameters of GMM/GMR are adaptively amended to eliminate collisions and safety issues between a cobot and obstacles; (iii) a fruit fly optimisation algorithm is incorporated into GMM/GMR to expedite the computational efficiency. Case studies with different complexities are used for approach validation in terms of feature retention from demonstrations, regression path smoothness and obstacle avoidance effectiveness. Results of the case studies and benchmarking analyses show that the approach is robust and efficient for dynamic manufacturing applications. Human–robotic collaboration Obstacle avoidance Gaussian mixture modelling Gaussian mixture regression Wang, Yuqi aut Hu, Kaixiong aut Li, Weidong (orcid)0000-0001-5559-7834 aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 34(2021), 2 vom: 18. Aug., Seite 789-807 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:34 year:2021 number:2 day:18 month:08 pages:789-807 https://doi.org/10.1007/s10845-021-01825-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 34 2021 2 18 08 789-807 |
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10.1007/s10845-021-01825-9 doi (DE-627)OLC2133449922 (DE-He213)s10845-021-01825-9-p DE-627 ger DE-627 rakwb eng 620 004 VZ Hu, Yudie verfasserin aut Adaptive obstacle avoidance in path planning of collaborative robots for dynamic manufacturing 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Gaussian Mixture Model (GMM)/Gaussian Mixture Regression (GMR) is a paramount technology of learning from demonstrations to perform human–robotic collaboration. However, GMM/GMR is ineffective in supporting dynamic manufacturing where random obstacles in the applications generate potential safety concerns. In this paper, an improved GMM/GMR-based approach for collaborative robots (cobots) path planning is designed to achieve adaptive obstacle avoidance in dynamic manufacturing. The approach is realised via three innovative steps: (i) new quality assessment criteria for a cobot’s paths produced by GMM/GMR are defined; (ii) based on the criteria, demonstrations and parameters of GMM/GMR are adaptively amended to eliminate collisions and safety issues between a cobot and obstacles; (iii) a fruit fly optimisation algorithm is incorporated into GMM/GMR to expedite the computational efficiency. Case studies with different complexities are used for approach validation in terms of feature retention from demonstrations, regression path smoothness and obstacle avoidance effectiveness. Results of the case studies and benchmarking analyses show that the approach is robust and efficient for dynamic manufacturing applications. Human–robotic collaboration Obstacle avoidance Gaussian mixture modelling Gaussian mixture regression Wang, Yuqi aut Hu, Kaixiong aut Li, Weidong (orcid)0000-0001-5559-7834 aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 34(2021), 2 vom: 18. Aug., Seite 789-807 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:34 year:2021 number:2 day:18 month:08 pages:789-807 https://doi.org/10.1007/s10845-021-01825-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 34 2021 2 18 08 789-807 |
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10.1007/s10845-021-01825-9 doi (DE-627)OLC2133449922 (DE-He213)s10845-021-01825-9-p DE-627 ger DE-627 rakwb eng 620 004 VZ Hu, Yudie verfasserin aut Adaptive obstacle avoidance in path planning of collaborative robots for dynamic manufacturing 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Gaussian Mixture Model (GMM)/Gaussian Mixture Regression (GMR) is a paramount technology of learning from demonstrations to perform human–robotic collaboration. However, GMM/GMR is ineffective in supporting dynamic manufacturing where random obstacles in the applications generate potential safety concerns. In this paper, an improved GMM/GMR-based approach for collaborative robots (cobots) path planning is designed to achieve adaptive obstacle avoidance in dynamic manufacturing. The approach is realised via three innovative steps: (i) new quality assessment criteria for a cobot’s paths produced by GMM/GMR are defined; (ii) based on the criteria, demonstrations and parameters of GMM/GMR are adaptively amended to eliminate collisions and safety issues between a cobot and obstacles; (iii) a fruit fly optimisation algorithm is incorporated into GMM/GMR to expedite the computational efficiency. Case studies with different complexities are used for approach validation in terms of feature retention from demonstrations, regression path smoothness and obstacle avoidance effectiveness. Results of the case studies and benchmarking analyses show that the approach is robust and efficient for dynamic manufacturing applications. Human–robotic collaboration Obstacle avoidance Gaussian mixture modelling Gaussian mixture regression Wang, Yuqi aut Hu, Kaixiong aut Li, Weidong (orcid)0000-0001-5559-7834 aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 34(2021), 2 vom: 18. Aug., Seite 789-807 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:34 year:2021 number:2 day:18 month:08 pages:789-807 https://doi.org/10.1007/s10845-021-01825-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 34 2021 2 18 08 789-807 |
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10.1007/s10845-021-01825-9 doi (DE-627)OLC2133449922 (DE-He213)s10845-021-01825-9-p DE-627 ger DE-627 rakwb eng 620 004 VZ Hu, Yudie verfasserin aut Adaptive obstacle avoidance in path planning of collaborative robots for dynamic manufacturing 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Gaussian Mixture Model (GMM)/Gaussian Mixture Regression (GMR) is a paramount technology of learning from demonstrations to perform human–robotic collaboration. However, GMM/GMR is ineffective in supporting dynamic manufacturing where random obstacles in the applications generate potential safety concerns. In this paper, an improved GMM/GMR-based approach for collaborative robots (cobots) path planning is designed to achieve adaptive obstacle avoidance in dynamic manufacturing. The approach is realised via three innovative steps: (i) new quality assessment criteria for a cobot’s paths produced by GMM/GMR are defined; (ii) based on the criteria, demonstrations and parameters of GMM/GMR are adaptively amended to eliminate collisions and safety issues between a cobot and obstacles; (iii) a fruit fly optimisation algorithm is incorporated into GMM/GMR to expedite the computational efficiency. Case studies with different complexities are used for approach validation in terms of feature retention from demonstrations, regression path smoothness and obstacle avoidance effectiveness. Results of the case studies and benchmarking analyses show that the approach is robust and efficient for dynamic manufacturing applications. Human–robotic collaboration Obstacle avoidance Gaussian mixture modelling Gaussian mixture regression Wang, Yuqi aut Hu, Kaixiong aut Li, Weidong (orcid)0000-0001-5559-7834 aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 34(2021), 2 vom: 18. Aug., Seite 789-807 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:34 year:2021 number:2 day:18 month:08 pages:789-807 https://doi.org/10.1007/s10845-021-01825-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 34 2021 2 18 08 789-807 |
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10.1007/s10845-021-01825-9 doi (DE-627)OLC2133449922 (DE-He213)s10845-021-01825-9-p DE-627 ger DE-627 rakwb eng 620 004 VZ Hu, Yudie verfasserin aut Adaptive obstacle avoidance in path planning of collaborative robots for dynamic manufacturing 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Gaussian Mixture Model (GMM)/Gaussian Mixture Regression (GMR) is a paramount technology of learning from demonstrations to perform human–robotic collaboration. However, GMM/GMR is ineffective in supporting dynamic manufacturing where random obstacles in the applications generate potential safety concerns. In this paper, an improved GMM/GMR-based approach for collaborative robots (cobots) path planning is designed to achieve adaptive obstacle avoidance in dynamic manufacturing. The approach is realised via three innovative steps: (i) new quality assessment criteria for a cobot’s paths produced by GMM/GMR are defined; (ii) based on the criteria, demonstrations and parameters of GMM/GMR are adaptively amended to eliminate collisions and safety issues between a cobot and obstacles; (iii) a fruit fly optimisation algorithm is incorporated into GMM/GMR to expedite the computational efficiency. Case studies with different complexities are used for approach validation in terms of feature retention from demonstrations, regression path smoothness and obstacle avoidance effectiveness. Results of the case studies and benchmarking analyses show that the approach is robust and efficient for dynamic manufacturing applications. Human–robotic collaboration Obstacle avoidance Gaussian mixture modelling Gaussian mixture regression Wang, Yuqi aut Hu, Kaixiong aut Li, Weidong (orcid)0000-0001-5559-7834 aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 34(2021), 2 vom: 18. Aug., Seite 789-807 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:34 year:2021 number:2 day:18 month:08 pages:789-807 https://doi.org/10.1007/s10845-021-01825-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 34 2021 2 18 08 789-807 |
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Abstract Gaussian Mixture Model (GMM)/Gaussian Mixture Regression (GMR) is a paramount technology of learning from demonstrations to perform human–robotic collaboration. However, GMM/GMR is ineffective in supporting dynamic manufacturing where random obstacles in the applications generate potential safety concerns. In this paper, an improved GMM/GMR-based approach for collaborative robots (cobots) path planning is designed to achieve adaptive obstacle avoidance in dynamic manufacturing. The approach is realised via three innovative steps: (i) new quality assessment criteria for a cobot’s paths produced by GMM/GMR are defined; (ii) based on the criteria, demonstrations and parameters of GMM/GMR are adaptively amended to eliminate collisions and safety issues between a cobot and obstacles; (iii) a fruit fly optimisation algorithm is incorporated into GMM/GMR to expedite the computational efficiency. Case studies with different complexities are used for approach validation in terms of feature retention from demonstrations, regression path smoothness and obstacle avoidance effectiveness. Results of the case studies and benchmarking analyses show that the approach is robust and efficient for dynamic manufacturing applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Abstract Gaussian Mixture Model (GMM)/Gaussian Mixture Regression (GMR) is a paramount technology of learning from demonstrations to perform human–robotic collaboration. However, GMM/GMR is ineffective in supporting dynamic manufacturing where random obstacles in the applications generate potential safety concerns. In this paper, an improved GMM/GMR-based approach for collaborative robots (cobots) path planning is designed to achieve adaptive obstacle avoidance in dynamic manufacturing. The approach is realised via three innovative steps: (i) new quality assessment criteria for a cobot’s paths produced by GMM/GMR are defined; (ii) based on the criteria, demonstrations and parameters of GMM/GMR are adaptively amended to eliminate collisions and safety issues between a cobot and obstacles; (iii) a fruit fly optimisation algorithm is incorporated into GMM/GMR to expedite the computational efficiency. Case studies with different complexities are used for approach validation in terms of feature retention from demonstrations, regression path smoothness and obstacle avoidance effectiveness. Results of the case studies and benchmarking analyses show that the approach is robust and efficient for dynamic manufacturing applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Gaussian Mixture Model (GMM)/Gaussian Mixture Regression (GMR) is a paramount technology of learning from demonstrations to perform human–robotic collaboration. However, GMM/GMR is ineffective in supporting dynamic manufacturing where random obstacles in the applications generate potential safety concerns. In this paper, an improved GMM/GMR-based approach for collaborative robots (cobots) path planning is designed to achieve adaptive obstacle avoidance in dynamic manufacturing. The approach is realised via three innovative steps: (i) new quality assessment criteria for a cobot’s paths produced by GMM/GMR are defined; (ii) based on the criteria, demonstrations and parameters of GMM/GMR are adaptively amended to eliminate collisions and safety issues between a cobot and obstacles; (iii) a fruit fly optimisation algorithm is incorporated into GMM/GMR to expedite the computational efficiency. Case studies with different complexities are used for approach validation in terms of feature retention from demonstrations, regression path smoothness and obstacle avoidance effectiveness. Results of the case studies and benchmarking analyses show that the approach is robust and efficient for dynamic manufacturing applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Adaptive obstacle avoidance in path planning of collaborative robots for dynamic manufacturing |
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Wang, Yuqi Hu, Kaixiong Li, Weidong |
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