2000 character limit reached
RoboGrind: Intuitive and Interactive Surface Treatment with Industrial Robots (2402.16542v2)
Published 26 Feb 2024 in cs.RO and cs.AI
Abstract: Surface treatment tasks such as grinding, sanding or polishing are a vital step of the value chain in many industries, but are notoriously challenging to automate. We present RoboGrind, an integrated system for the intuitive, interactive automation of surface treatment tasks with industrial robots. It combines a sophisticated 3D perception pipeline for surface scanning and automatic defect identification, an interactive voice-controlled wizard system for the AI-assisted bootstrapping and parameterization of robot programs, and an automatic planning and execution pipeline for force-controlled robotic surface treatment. RoboGrind is evaluated both under laboratory and real-world conditions in the context of refabricating fiberglass wind turbine blades.
- J. Li, T. Yuan, W. Wu, H. Zhu, C. Zhang, and J. Xie, “Automatic programming system for grinding robot of chsr rail,” Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics, 2018.
- J. Oyekan, M. Farnsworth, W. Hutabarat, D. Miller, and A. Tiwari, “Applying a 6 DoF robotic arm and digital twin to automate fan-blade reconditioning for aerospace maintenance, repair, and overhaul,” Sensors, vol. 20, no. 16, p. 4637, 2020. [Online]. Available: https://www.mdpi.com/1424-8220/20/16/4637
- C. R. Garrett, R. Chitnis, R. Holladay, B. Kim, T. Silver, L. P. Kaelbling, and T. Lozano-Pérez, “Integrated Task and Motion Planning,” arXiv:2010.01083 [cs], Oct. 2020, arXiv: 2010.01083. [Online]. Available: http://arxiv.org/abs/2010.01083
- L. P. Kaelbling and T. Lozano-Perez, “Hierarchical task and motion planning in the now,” in ICRA, May 2011, pp. 1470–1477. [Online]. Available: http://ieeexplore.ieee.org/document/5980391/
- M. Beetz, L. Mösenlechner, and M. Tenorth, “CRAM — A Cognitive Robot Abstract Machine for everyday manipulation in human environments,” in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct. 2010, pp. 1012–1017, iSSN: 2153-0866.
- M. Beetz, D. Bessler, A. Haidu, M. Pomarlan, A. K. Bozcuoglu, and G. Bartels, “Know Rob 2.0 — A 2nd Generation Knowledge Processing Framework for Cognition-Enabled Robotic Agents,” in ICRA, May 2018, pp. 512–519. [Online]. Available: https://ieeexplore.ieee.org/document/8460964/
- S. Koralewski, G. Kazhoyan, and M. Beetz, “Self-Specialization of General Robot Plans Based on Experience,” IEEE Robotics and Automation Letters, 2019.
- B. Alt, F. K. Kenfack, A. Haidu, D. Katic, R. Jäkel, and M. Beetz, “Knowledge-Driven Robot Program Synthesis from Human VR Demonstrations,” Rhodes, Greece, Sep. 2023, arXiv:2306.02739 [cs]. [Online]. Available: http://arxiv.org/abs/2306.02739
- J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng, “Code as Policies: Language Model Programs for Embodied Control,” in 2023 IEEE International Conference on Robotics and Automation (ICRA), May 2023, pp. 9493–9500.
- I. Singh, V. Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and A. Garg, “ProgPrompt: Generating Situated Robot Task Plans using Large Language Models,” Sep. 2022, arXiv:2209.11302 [cs]. [Online]. Available: http://arxiv.org/abs/2209.11302
- J. Wu, R. Antonova, A. Kan, M. Lepert, A. Zeng, S. Song, J. Bohg, S. Rusinkiewicz, and T. Funkhouser, “TidyBot: Personalized Robot Assistance with Large Language Models,” May 2023, arXiv:2305.05658 [cs]. [Online]. Available: http://arxiv.org/abs/2305.05658
- D. Beßler, R. Porzel, M. Pomarlan, A. Vyas, S. Höffner, M. Beetz, R. Malaka, and J. Bateman, “Foundations of the Socio-physical Model of Activities (SOMA) for Autonomous Robotic Agents,” arXiv:2011.11972 [cs], Nov. 2020, arXiv: 2011.11972. [Online]. Available: http://arxiv.org/abs/2011.11972
- “Speech-to-Text: Automatic Speech Recognition.” [Online]. Available: https://cloud.google.com/speech-to-text
- M. Honnibal and I. Montani, “spaCy: Industrial-strength Natural Language Processing (NLP) in Python.” [Online]. Available: https://github.com/explosion/spaCy
- J. Raible, C. Braun, and M. Huber, “Automatic Path Planning for Robotic Grinding and Polishing Tasks based on Point Cloud Slicing,” in ISR Europe 2023; 56th International Symposium on Robotics, 2023, pp. 382–389.
- X. Zeng, G. Zhu, Z. Gao, R. Ji, J. Ansari, and C. Lu, “Surface polishing by industrial robots: a review,” The International Journal of Advanced Manufacturing Technology, vol. 125, no. 9-10, pp. 3981–4012, 2023.