Synthetic data-augmented learning pipelines for cobotic packing work cells
Safe human-robot interaction has consistently been one of the main concerns behind industrial robot applications. This is particularly true with the emerging trends in collaborative robotics and their use in quick, relatively inexpensive automation of warehousing and distribution tasks. As such, the...
- Autores:
-
Martínez Franco, Juan Camilo
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2023
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/68615
- Acceso en línea:
- http://hdl.handle.net/1992/68615
- Palabra clave:
- Automated packing systems
Cobots
Machine vision
Motion planning
Stable packing pattern
Ingeniería
- Rights
- openAccess
- License
- Atribución 4.0 Internacional
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Synthetic data-augmented learning pipelines for cobotic packing work cells |
| title |
Synthetic data-augmented learning pipelines for cobotic packing work cells |
| spellingShingle |
Synthetic data-augmented learning pipelines for cobotic packing work cells Automated packing systems Cobots Machine vision Motion planning Stable packing pattern Ingeniería |
| title_short |
Synthetic data-augmented learning pipelines for cobotic packing work cells |
| title_full |
Synthetic data-augmented learning pipelines for cobotic packing work cells |
| title_fullStr |
Synthetic data-augmented learning pipelines for cobotic packing work cells |
| title_full_unstemmed |
Synthetic data-augmented learning pipelines for cobotic packing work cells |
| title_sort |
Synthetic data-augmented learning pipelines for cobotic packing work cells |
| dc.creator.fl_str_mv |
Martínez Franco, Juan Camilo |
| dc.contributor.advisor.none.fl_str_mv |
Álvarez Martínez, David |
| dc.contributor.author.none.fl_str_mv |
Martínez Franco, Juan Camilo |
| dc.contributor.jury.none.fl_str_mv |
Tabares Pozos, Alejandra |
| dc.contributor.researchgroup.es_CO.fl_str_mv |
Centro para la Optimización y Probabilidad Aplicada Producción y Logística |
| dc.subject.keyword.none.fl_str_mv |
Automated packing systems Cobots Machine vision Motion planning Stable packing pattern |
| topic |
Automated packing systems Cobots Machine vision Motion planning Stable packing pattern Ingeniería |
| dc.subject.themes.es_CO.fl_str_mv |
Ingeniería |
| description |
Safe human-robot interaction has consistently been one of the main concerns behind industrial robot applications. This is particularly true with the emerging trends in collaborative robotics and their use in quick, relatively inexpensive automation of warehousing and distribution tasks. As such, there is an increasing need for safety features in response to dynamic workspace conditions that were not present in industrial environments in the past. This thesis aims to introduce novel methodologies that allow for the generation of dynamically stable packing pattens, more accurate, comprehensive understanding of 3D scenes from data captured with RGB-D sensors, as well as more energy-efficient and collision free trajectories in collaborative manipulators. The first contribution is based on dynamic stability studies of cutting and packing problems, the next contribution is focused on a new procedure for hand-eye calibration that is not dependent on printed grid patterns. The next addition to the state of the art is related to domain randomization, where approaches towards synthetic data generation and training procedures are proposed. Lastly, a reinforcement learning scheme making use of proximal policy optimization and engineered rewards aiming to reduce inefficient movements in collision avoidant path planning is presented. The mentioned contributions were implemented via a case study in an automated packing operation. |
| publishDate |
2023 |
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2023-07-21T14:25:45Z |
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2023-07-21T14:25:45Z |
| dc.date.issued.none.fl_str_mv |
2023-06-08 |
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Trabajo de grado - Doctorado |
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info:eu-repo/semantics/doctoralThesis |
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eng |
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eng |
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H. Zhao and K. Xu, "Learning efficient online 3d bin packing on packing configuration trees," in International Conference on Learning Representations, 2022. S. Ali, A. G. Ramos, M. A. Carravilla, and J. F. Oliveira, "On-line three-dimensional packing problems: A review of off-line and on-line solution approaches," Computers Industrial Engineering, vol. 168, p. 108 122, 2022, issn: 0360-8352. J. Sun, S. Wu, C. Shao, F. Guo, and Y. Su, "Application research of logistics warehousing system based on Internet of Things and artificial intelligence," in International Conference on Cloud Computing, Internet of Things, and Computer Applications (CICA 2022), W. Powell and A. Tolba, Eds., International Society for Optics and Photonics, vol. 12303, SPIE, 2022, p. 123031C. J. E. Colgate and M. A. Peshkin, "Cobots," pat. 266 793, Sep. 14, 1999. A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, "Deep learning for computer vision: A brief review," Computational intelligence and neuroscience, vol. 2018, 2018. A. Bonci, P. D. Cen Cheng, M. Indri, G. Nabissi, and F. Sibona, "Human-robot perception in industrial environments: A survey," Sensors, vol. 21, no. 5, p. 1571, 2021. S. K. Saha, "Control de trayectoria," in Introducción a la Robótica, Mc Graw Hill, 2010, p. 30. V. L. Siciliano Bruno Sciavicco Lorenzo and O. Guiseppe, "Trayectory planning," p. 161, 2010. D. Alvarez Martinez, R. Álvarez-Valdés, and F. Parreño, "A grasp algorithm for the container loading problem with multi-drop constraints," Pesquisa Operacional, vol. 35, p. 1, Feb. 2015. J. Martínez-Franco, E. Céspedes-Sabogal, and D. Álvarez-Martínez, "Packagecargo: A decision support tool for the container loading problem with stability," SoftwareX, vol. 12, p. 100 601, 2020, issn: 2352-7110. doi: https://doi.org/10./j.softx.2020.100601.com/science/article/pii/S2352711020303149. J. C. Pachón, J. Martínez-Franco, and D. Álvarez-Martínez, "Sic: An intelligent packing system with industry-grade features," SoftwareX, vol. 20, p. 101 241, 2022, issn: 2352-7110. S. Giancola, M. Valenti, and R. Sala, A survey on 3D cameras: Metrological comparison of time-of-flight, structured-light and active stereoscopy technologies. Springer, 2018. J. Redmon and A. Farhadi, Yolov3: An incremental improvement, 2018. doi: 10.0/ARXIV.1804.02767. J. Blumenkamp, A. S. Baude, and T. Laue, "Closing the reality gap with unsupervised sim-to-real image translation for semantic segmentation in robot soccer," in Robot Soccer World Cup, 2019. X. Xia, Q. Lu, and X. Gu, "Exploring an easy way for imbalanced data sets in semantic image segmentation," Journal of Physics: Conference Series, vol. 1213, p. 022 003, Jun. 2019. doi: 10.1088/1742-6596/1213/2/022003. J. Martinez-Franco, N. Sacchi, A. Ferrara, and D. Alvarez-Martinez, "3d segmentation based obstacle detection for collision avoidance in collaborative robots," IEEE Robotics and Automation Letters (under review), L. Sciavicco and B. Siciliano, Modelling and control of robot manipulators. Springer Science & Business Media, 2001. B. Siciliano and O. Khatib, Springer Handbook of Robotics (Springer Handbooks). J. Denavit and R. S. Hartenberg, "A kinematic notation for lower-pair mechanisms based on matrices," 1955. M. Brandstötter, A. Angerer, and M. Hofbaur, "An analytical solution of the inverse kinematics problem of industrial serial manipulators with an ortho-parallel basis and a spherical wrist," May 2014. R. Diankov and J. Kuffner, "Openrave: A planning architecture for autonomous robotics," Apr. 2011. T. Lillicrap, J. Hunt, A. Pritzel, et al., "Continuous control with deep reinforcement learning," CoRR, Sep. 2015. V. Kumar, D. Hoeller, B. Sundaralingam, J. Tremblay, and S. Birchfield, Joint e control via deep reinforcement learning, 2021. arXiv: 2011.06332 T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, "Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor," Jan. 2018. T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, "Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor," Jan. 2018. J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, "Proximal policy optimization algorithms," arXiv preprint arXiv:1707.06347, 2017. T. Schaul, J. Quan, I. Antonoglou, and D. Silver, Prioritized experience replay, . arXiv: 1511.05952 [cs.LG]. M. Andrychowicz, F. Wolski, A. Ray, et al., "Hindsight experience replay," in Advances in Neural Information Processing Systems, I. Guyon, U. V. Luxburg, engio, et al., Eds., vol. 30, Curran Associates, Inc., 2017. P. S. Parra, O. L. Calleros, and A. Ramirez-Serrano, "Human-robot collaboration systems: Components and applications," in Int. Conf. Control. Dyn. Syst. Robot, vol. 150, 2020, pp. 1-9 J. E. Michaelis, A. Siebert-Evenstone, D. W. Shaffer, and B. Mutlu, "Collaborative or simply uncaged understanding human-cobot interactions in automation," in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020, pp. 1-12. H. Zhao, C. Zhu, X. Xu, H. Huang, and K. Xu, "Learning practically feasible policies for online 3d bin packing," Science China Information Sciences, vol. 65, no. 1, pp. 1-17, 2022. K. A. Demir, G. Döven, and B. Sezen, Industry 5.0 and human-robot co-working, Procedia computer science, vol. 158, pp. 688-695, 2019. Z. Li and S. Li, "An l-norm based optimization method for sparse redundancy resolution of robotic manipulators," IEEE transactions on circuits and systems II: Express briefs, vol. 69, no. 2, pp. 469-473, 2021. M. Sundermeyer, Z.-C. Marton, M. Durner, M. Brucker, and R. Triebel, "Implicit 3d orientation learning for 6d object detection from rgb images," in ECCV, 2018. A. G. Ramos, J. F. Oliveira, J. F. Gonçalves, and M. P. Lopes, "Dynamic stability metrics for the container loading problem," Transportation Research Part C: Emerging Technologies, vol. 60, pp. 480-497, 2015, issn: 0968-090X. doi: https: i.org/10.1016/j.trc.2015.09.012. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0968090X15003459. "Robots and robotic devices - collaborative robots," International Organization for Standardization, Geneva, CH, Standard, Feb. 2016. G. Wäscher, H. Haußner, and H. Schumann, "An improved typology of cutting and packing problems," European Journal of Operational Research, vol. 183, no. 3, pp. 1109-1130, 2007, issn: 0377-2217. V. D. Luong, F. Abbes, B. Abbès, et al., "Finite element simulation of the strength of corrugated board boxes under impact dynamics," in Feb. 2018, pp. 369-380, isbn: 978-981-10-7148-5. doi: 10.1007/978-981-10-7149-2_25. J. J. Collins, S. Chand, A. Vanderkop, and D. Howard, "A review of physics simulators for robotic applications," IEEE Access, vol. 9, pp. 51 416-431, 2021. J. C. Martínez, D. Cuellar, and D. Álvarez-Martínez, "Review of dynamic stability metrics and a mechanical model integrated with open source tools for the container loading problem," Electronic Notes in Discrete Mathematics, vol. 69, pp. 325-332, 2018, Joint EURO/ALIO International Conference 2018 on Applied Combinatorial Optimization (EURO/ALIO 2018), issn: 1571-0653. doi: https://doi.org/10./j.endm.2018.07.042. J. C. Martínez-Franco and D. Álvarez-Martínez, "Physx as a middleware for dynamic simulations in the container loading problem," in 2018 Winter Simulation Conference (WSC), 2018, pp. 2933-2940. doi: 10.1109/WSC.2018.8632469. K. Patel, "A review: Machine vision and its applications.," IOSR Journal of Electronics and Communication Engineering, vol. 7, pp. 72-77, 2013. T.Y. Lin, M. Maire, S. Belongie, et al., "Microsoft coco: Common objects in context," in Computer Vision-ECCV 2014, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds., Cham: Springer International Publishing, 2014, pp. 740-755, isbn: 978-3-319-10602-1. J. C. Martínez-Franco and D. Álvarez-Martínez, "Machine vision for collaborative robotics using synthetic data-driven learning," in Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future: Proceedings of SOHOMA LATIN AMERICA 2021, Springer, 2021, pp. 69-81. J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, "Domain randomization for transferring deep neural networks from simulation to the real world," in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017, pp. 23-30. doi: 10.1109/IROS.2017.8202133. M. Granja, N. Chang, V. Granja, M. Duque, and F. Llulluna, "Comparison between standard and modified denavit-hartenberg methods in robotics modelling," in Proceedings of the World Congress on mechanical, chemical, and material engineering, Citeseer, vol. 1, 2016, pp. 1-10. D. Kubus, R. Rayyes, and J. J. Steil, "Learning forward and inverse kinematics maps efficiently," in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp. 5133-5140. L. E. van Dyck, R. Kwitt, S. J. Denzler, and W. R. Gruber, "Comparing object recognition in humans and deep convolutional neural networks-an eye tracking study," Frontiers in Neuroscience, vol. 15, 2021. A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb, "Learning from simulated and unsupervised images through adversarial training," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2242-2251. doi: 10.1109/CVPR.2017.241. R. Horaud and F. Dornaika, "Hand-eye calibration," The international journal of robotics research, vol. 14, no. 3, pp. 195-210, 1995. T. Höfer, F. Shamsafar, N. Benbarka, and A. Zell, "Object detection and autoencoderbased 6d pose estimation for highly cluttered bin picking," in 2021 IEEE International Conference on Image Processing (ICIP), 2021, pp. 704-708. doi: 10.1109/ ICIP42928.2021.9506304. J. Langlois, H. Mouchère, N. Normand, and C. Viard-Gaudin, "3d orientation estimation of industrial parts from 2d images using neural networks," Jan. 2018, pp. 409-416. doi: 10.5220/0006597604090416. L. P. Kaelbling, M. L. Littman, and A. W. Moore, "Reinforcement learning: A survey," J. Artif. Int. Res., vol. 4, no. 1, pp. 237-285, May 1996, issn: 1076-9757. R. J. Williams, "Simple statistical gradient-following algorithms for connectionist reinforcement learning," Reinforcement learning, pp. 5-32, 1992. T. Sadamoto, A. Chakrabortty, and J.-i. Imura, "Fast online reinforcement learning control using state-space dimensionality reduction," IEEE Transactions on Control of Network Systems, vol. 8, no. 1, pp. 342-353, 2021. L.-J. Lin, "Self-improving reactive agents based on reinforcement learning, planning and teaching," Machine learning, vol. 8, pp. 293-321, 1992. G. Peng, J. Yang, X. Li, and M. O. Khyam, "Deep reinforcement learning with a stage incentive mechanism of dense reward for robotic trajectory planning," IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-8, 2022. doi: 10.1109/TSMC.2022.3228901. O. Khatib, "Real-time obstacle avoidance for manipulators and mobile robots," in Autonomous robot vehicles, Springer, 1986, pp. 396-404. D. Kubus, R. Rayyes, and J. J. Steil, "Learning forward and inverse kinematics maps efficiently," in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp. 5133-5140. L.-N. Truc, N. Quyen, and N. Phung Quang, "Dynamic model with a new formulation of coriolis/centrifugal matrix for robot manipulators," Journal of Computer Science and Cybernetics, vol. 36, pp. 89-104, Feb. 2020. doi: 10.15625/18139663/1/1/14557 A. Bortfeldt and G. Wäscher, "Constraints in container loading - a state-of-the-art review," European Journal of Operational Research, vol. 229, no. 1, pp. 1-20, 2013, issn: 0377-2217. doi: https://doi.org/10.1016/j.ejor.2012.12.006. L. Faina, "A survey on the cutting and packing problems," Bollettino dell Unione Matematica Italiana, vol. 13, no. 4, pp. 567-572, 2020. O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," in Medical Image Computing and ComputerAssisted Intervention - MICCAI 2015, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, Eds., Cham: Springer International Publishing, 2015, pp. 234-241, isbn: 978-3-319-24574-4. S. Bitzer, M. Howard, and S. Vijayakumar, "Using dimensionality reduction to exploit constraints in reinforcement learning," in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010, pp. 3219-3225. |
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Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Álvarez Martínez, Davidvirtual::11173-1Martínez Franco, Juan Camilobbfe8a4c-40fe-4d38-a8e8-ce36a38c6f38600Tabares Pozos, AlejandraCentro para la Optimización y Probabilidad AplicadaProducción y Logística2023-07-21T14:25:45Z2023-07-21T14:25:45Z2023-06-08http://hdl.handle.net/1992/6861510.57784/1992/68615instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Safe human-robot interaction has consistently been one of the main concerns behind industrial robot applications. This is particularly true with the emerging trends in collaborative robotics and their use in quick, relatively inexpensive automation of warehousing and distribution tasks. As such, there is an increasing need for safety features in response to dynamic workspace conditions that were not present in industrial environments in the past. This thesis aims to introduce novel methodologies that allow for the generation of dynamically stable packing pattens, more accurate, comprehensive understanding of 3D scenes from data captured with RGB-D sensors, as well as more energy-efficient and collision free trajectories in collaborative manipulators. The first contribution is based on dynamic stability studies of cutting and packing problems, the next contribution is focused on a new procedure for hand-eye calibration that is not dependent on printed grid patterns. The next addition to the state of the art is related to domain randomization, where approaches towards synthetic data generation and training procedures are proposed. Lastly, a reinforcement learning scheme making use of proximal policy optimization and engineered rewards aiming to reduce inefficient movements in collision avoidant path planning is presented. The mentioned contributions were implemented via a case study in an automated packing operation.Doctor en IngenieríaDoctorado129 páginasapplication/pdfengUniversidad de los AndesDoctorado en IngenieríaFacultad de IngenieríaSynthetic data-augmented learning pipelines for cobotic packing work cellsTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttps://purl.org/redcol/resource_type/TDAutomated packing systemsCobotsMachine visionMotion planningStable packing patternIngenieríaH. Zhao and K. Xu, "Learning efficient online 3d bin packing on packing configuration trees," in International Conference on Learning Representations, 2022.S. Ali, A. G. Ramos, M. A. Carravilla, and J. F. Oliveira, "On-line three-dimensional packing problems: A review of off-line and on-line solution approaches," Computers Industrial Engineering, vol. 168, p. 108 122, 2022, issn: 0360-8352.J. Sun, S. Wu, C. Shao, F. Guo, and Y. Su, "Application research of logistics warehousing system based on Internet of Things and artificial intelligence," in International Conference on Cloud Computing, Internet of Things, and Computer Applications (CICA 2022), W. Powell and A. Tolba, Eds., International Society for Optics and Photonics, vol. 12303, SPIE, 2022, p. 123031C.J. E. Colgate and M. A. Peshkin, "Cobots," pat. 266 793, Sep. 14, 1999.A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, "Deep learning for computer vision: A brief review," Computational intelligence and neuroscience, vol. 2018, 2018.A. Bonci, P. D. Cen Cheng, M. Indri, G. Nabissi, and F. Sibona, "Human-robot perception in industrial environments: A survey," Sensors, vol. 21, no. 5, p. 1571, 2021.S. K. Saha, "Control de trayectoria," in Introducción a la Robótica, Mc Graw Hill, 2010, p. 30.V. L. Siciliano Bruno Sciavicco Lorenzo and O. Guiseppe, "Trayectory planning," p. 161, 2010.D. Alvarez Martinez, R. Álvarez-Valdés, and F. Parreño, "A grasp algorithm for the container loading problem with multi-drop constraints," Pesquisa Operacional, vol. 35, p. 1, Feb. 2015.J. Martínez-Franco, E. Céspedes-Sabogal, and D. Álvarez-Martínez, "Packagecargo: A decision support tool for the container loading problem with stability," SoftwareX, vol. 12, p. 100 601, 2020, issn: 2352-7110. doi: https://doi.org/10./j.softx.2020.100601.com/science/article/pii/S2352711020303149.J. C. Pachón, J. Martínez-Franco, and D. Álvarez-Martínez, "Sic: An intelligent packing system with industry-grade features," SoftwareX, vol. 20, p. 101 241, 2022, issn: 2352-7110.S. Giancola, M. Valenti, and R. 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