Abstract
Nowadays, companies face new challenges and benefits with the incorporation of technologies associated with Industry 4.0 into product manufacturing. The new collaborative environments have to be capable of adapting efficiently to different levels of production as well as safely collaborating in the process with human operators. In this paper, a model for predicting human actions involved in a manufacturing process is proposed. The model uses Manufacturing Description Language combined with a statistical state graph able to provide the probability of a new action to be performed by the operator. Among other benefits, the model is able to improve the production, reducing waste in movement, time, or use of additional actions.
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References
Ragusa, F., Furnari, A., Livatino, S., Farinella, G.M.: The MECCANO dataset: Understanding human-object interactions fromTepic, Nayarit, México egocentric videos in an industrial-like domain. arXiv (2020)
Nguyen, A., Do, T.-T., Reid, I., Caldwell, D.G., Tsagarakis, N.G.: V2CNet: a Deep Learning Framework to Translate Videos to Commands for Robotic Manipulation. J. Vibration Control, page 107754631982824, mar (2019)
Mees, O., Burgard, W.: Composing pick-and-place tasks by grounding language. In: Siciliano, B., Laschi, C., Khatib, O., (eds.) Experimental Robotics, pp. 491–501. Springer, Cham (2021)
Yizhak, B.-S.: The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose. arXiv (2020)
Alati, E., Mauro, L., Ntouskos, V., Pirri, F.: Anticipating next goal for robot plan prediction. In: Bi, Y., Bhatia, R., Kapoor, S., (eds.) Intelligent Systems and Applications, pp. 792–809. Springer, Cham (2020)
Park, J., Manocha, D.: HMPO: human motion prediction in occluded environments for safe motion planning. In: Robotics: Science and Systems XVI. Robotics: Science and Systems Foundation, July 2020
Xiangchun, Yu., Zhang, Z., Lei, W., Pang, W., Chen, H., Zhezhou, Yu., Li, B.: Deep ensemble learning for human action recognition in still images. Complexity 1–23, 2020 (2020)
Wörgötter, F., Ziaeetabar, F., Pfeiffer, S., Kaya, O., Kulvicius, T., Tamosiunaite, M.: Humans predict action using grammar-like structures. Sci. Rep. 10(1), 3999 (2020)
Gesnouin, J., Pechberti, S., Bresson, G., Stanciulescu, B., Moutarde, F.: Predicting intentions of pedestrians from 2D skeletal pose sequences with a representation-focused multi-branch deep learning network. Algorithms 13(12), 331 (2020)
Serpush, F., Rezaei, M.: Complex human action recognition using a hierarchical feature reduction and deep learning-based method. SN Comput. Sci. 2(2), 1–15 (2021). https://doi.org/10.1007/s42979-021-00484-0
Oprea, S., et al.: A review on deep learning techniques for video prediction. IEEE Transactions on PAMI, 1 (2020)
Castrejon, L., Ballas, N., Courville, A.: Improved conditional VRNNs for video prediction. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), vol. 2019-Octob, pp. 7607–7616. IEEE, October 2019
Zamora-Hernández, M.-A., Ceciliano, J.A.C., Granados, A.V., Vargas, J.A.C., Garcia-Rodriguez, J., Azorín-López, J.: Manufacturing description language for process control in industry 4.0. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds.) SOCO 2020. AISC, vol. 1268, pp. 790–799. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-57802-2_76
Zamora-Hernández, M.-A.: Deep learning-based visual control assistant for assembly in industry 4.0. In: Computers in Industry. Elsevier (2021)
Hopp, W.: Factory Physics. Waveland Press, Long Grove (2011)
Socconini, L.: Lean Manufacturing: paso a paso. Marge Books, Barcelona (2019)
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Zamora-Hernandez, MA., Ceciliano, J.A.C., Granados, A.V., Garcia-Rodriguez, J., Azorin-Lopez, J. (2022). Predicting Human Actions in the Assembly Process for Industry 4.0. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_38
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DOI: https://doi.org/10.1007/978-3-030-87869-6_38
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