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2017 | OriginalPaper | Chapter

Machine Learning Improves Human-Robot Interaction in Productive Environments: A Review

Authors : Mauricio Zamora, Eldon Caldwell, Jose Garcia-Rodriguez, Jorge Azorin-Lopez, Miguel Cazorla

Published in: Advances in Computational Intelligence

Publisher: Springer International Publishing

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Abstract

In the new generation of industries, including all the advances introduced by Industry 4.0, human robot interaction (HRI), by means of automatic learning and computer vision, become an important element to accomplish. HRI allows to create collaborative environments between people and robots, avoiding the latter generating a risk of occupational safety. In addition to the automatic systems, the interaction by mean of automated learning processes provides necessary information to increase productivity and minimize delivery response times by helping to optimize complex production planning processes. In this paper, it is presented a review of the technologies necessary to be considered as basic elements in all processes of industry 4.0 as a crucial linking element between humans, robots, intelligent and traditional machines.

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Literature
1.
Kang, H.S., Lee, J.Y., Choi, S., Kim, H., Park, J.H., Son, J.Y., Kim, B.H., Noh, S.D.: Smart manufacturing: Past research, present findings, and future directions. Int. J. Precis. Eng. Manuf. - Green Technol. 3(1), 111–128 (2016)CrossRef
2.
Drath, R., Horch, A.: Industrie 4.0: hit or hype? [Industry Forum]. IEEE Ind. Electron. Mag. 8(2), 56–58 (2014)CrossRef
3.
Hermann, M., Pentek, T., Otto, B.: Design principles for industrie 4.0 scenarios. In: Proceedings of the Annual Hawaii International Conference on System Sciences, pp. 3928–3937, March 2016
4.
Lee, J., Bagheri, B., Kao, H.A.: Recent advances and trends of cyber-physical systems and big data analytics in industrial informatics. In: International Conference on Industrial Informatics (INDIN) 2014, November 2015 (2014)
5.
Hedelind, M., Jackson, M.: How to improve the use of industrial robots in lean manufacturing systems. J. Manuf. Technol. Manag. 22(7), 891–905 (2011)CrossRef
6.
Siddique, N.H., Mitchell, R., O’Grady, M., Jahankhani, H.: Cybernetic approaches to robotics. Paladyn 2(3), 109–110 (2011)
7.
Meisner, E., Isler, V., Trinkle, J.: Controller design for human-robot interaction. Auton. Rob. 24(2), 123–134 (2008)CrossRef
8.
Goodrich, M.A., Schultz, A.C.: Human-robot interaction: a survey. Found. Trends® Hum. Comput. Interact. 1(3), 203–275 (2007)CrossRefMATH
9.
Santoro, M., Marino, D., Tamburrini, G.: Learning robots interacting with humans: from epistemic risk to responsibility. AI Soc. 22(3), 301–314 (2008)CrossRef
10.
Ferreiro, S., Sierra, B.: Comparison of machine learning algorithms for optimization and improvement of process quality in conventional metallic materials. Int. J. Adv. Manuf. Technol. 60(1–4), 237–249 (2012)CrossRef
11.
Fast-Berglund, Å., Fässberg, T., Hellman, F., Davidsson, A., Stahre, J.: Relations between complexity, quality and cognitive automation in mixed-model assembly. J. Manuf. Syst. 32(3), 449–455 (2013)CrossRef
12.
Lee, J., Bagheri, B., Jin, C.: Introduction to cyber manufacturing. Manuf. Lett. 8, 11–15 (2016)CrossRef
13.
Wang, L., Schmidt, B., Nee, A.Y.C.: Vision-guided active collision avoidance for human-robot collaborations. Manuf. Lett. 1(1), 5–8 (2013)CrossRef
14.
Hornung, A., Bennewitz, M., Strasdat, H.: Efficient vision-based navigation. Auton. Rob. 29(2), 137–149 (2010)CrossRef
15.
Makris, S., Karagiannis, P., Koukas, S., Matthaiakis, A.S.: Augmented reality system for operator support in human–robot collaborative assembly. CIRP Ann. Manuf. Technol. 65(1), 61–64 (2016)CrossRef
16.
Ericson, G., Franks, L., Rohrer, B.: How to choose algorithms for Microsoft Azure Machine Learning (2016)
17.
Xiao, S., Wang, Z., Folkesson, J.: Unsupervised robot learning to predict person motion. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 691–696 (2015)
18.
Erdin, M.E., Atmaca, A.: Implementation of an overall design of a flexible manufacturing system. Procedia Technol. 19, 185–192 (2015)CrossRef
19.
Puik, E., Telgen, D., Moergestel, L., Ceglarek, D.: Assessment of reconfiguration schemes for Reconfigurable Manufacturing Systems based on resources and lead time. Robot. Comput.-Integr. Manuf. 43, 30–38 (2017)CrossRef
20.
Monostori, L.: AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing. IFAC Proc. Volumes (IFAC-Papers Online) 15(1), 119–130 (2002)
21.
Leng, J., Jiang, P.: A deep learning approach for relationship extraction from interaction context in social manufacturing paradigm. Knowl.-Based Syst. 100, 188–199 (2015)CrossRef
22.
Cheng, Y., Tao, F., Zhao, D., Zhang, L.: Modeling of manufacturing service supply-demand matching hypernetwork in service-oriented manufacturing systems. Robot. Comput.-Integr. Manuf. 45, 59–72 (2015)CrossRef
23.
Priore, P., Fuente, D., Puente, J., Parreño, J.: A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Eng. Appl. Artif. Intell. 19(3), 247–255 (2006)CrossRef
24.
Gao, W., Zhang, Y., Ramanujan, D., Ramani, K., Chen, Y., Williams, C.B., Wang, C.C., Shin, Y.C., Zhang, S., Zavattieri, P.D.: The status, challenges, and future of additive manufacturing in engineering. Comput.-Aided Des. 69, 65–89 (2015)CrossRef
25.
Tsai, T.I., Li, D.C.: Utilize bootstrap in small data set learning for pilot run modeling of manufacturing systems. Expert Syst. Appl. 35(3), 1293–1300 (2008)CrossRef
26.
Penas, O., Plateaux, R., Patalano, S., Hammadi, M.: Multi-scale approach from mechatronic to Cyber-Physical Systems for the design of manufacturing systems. Comput. Ind. (2016)
27.
Tatic, D., Tesic, B.: The application of augmented reality technologies for the improvement of occupational safety in an industrial environment. Comput. Ind. 85, 1–10 (2017)CrossRef
28.
Leo, M., Medioni, G., Trivedi, M., Kanade, T., Farinella, G.M.: Computer vision for assistive technologies. Comput. Vis. Image Underst. 154, 1–15 (2015)CrossRef
29.
Mehlmann, G., Häring, M., Janowski, K., Baur, T., Gebhard, P., André, E.: Exploring a model of gaze for grounding in multimodal HRI. In: Proceedings of the 16th International Conference on Multimodal Interaction - ICMI 2014, pp. 247–254 (2014)
30.
Rani, P., Liu, C., Sarkar, N., Vanman, E.: An empirical study of machine learning techniques for affect recognition in human-robot interaction. Pattern Anal. Appl. 9(1), 58–69 (2006)CrossRef
31.
Panait, L., Panait, L., Luke, S.: Cooperative multi-agent learning: the state of the art. Auton. Agents Multi-Agent Syst. 3(11), 387–434 (2005)CrossRef
32.
Mohammad, Y., Nishida, T.: Toward combining autonomy and interactivity for social robots. AI Soc. 24(1), 35–49 (2009)CrossRef
33.
Ramík, D.M., Madani, K., Sabourin, C.: A Soft-Computing basis for robots’ cognitive autonomous learning. Soft Comput. 19(9), 2407–2421 (2014)CrossRef
34.
Vlassis, N., Toussaint, M., Kontes, G., Piperidis, S.: Learning model-free robot control by a Monte Carlo em algorithm. Auton. Rob. 27(2), 123–130 (2009)CrossRef
35.
Guo, L., Hao, J.H., Liu, M.: An incremental extreme learning machine for online sequential learning problems. Neurocomputing 128, 50–58 (2014)CrossRef
36.
Li, D.C., Yeh, C.W.: A non-parametric learning algorithm for small manufacturing data sets. Expert Syst. Appl. 34(1), 391–398 (2008)CrossRef
37.
Lee, J.H., Ha, S.H.: Recognizing yield patterns through hybrid applications of machine learning techniques. Inf. Sci. 179(6), 844–850 (2009)CrossRef
38.
Sudha, L., Dillibabu, R., Srivatsa Srinivas, S., Annamalai, A.: Optimization of process parameters in feed manufacturing using artificial neural network. Comput. Electr. Agric. 120, 1–6 (2016)CrossRef
Metadata
Title
Machine Learning Improves Human-Robot Interaction in Productive Environments: A Review
Authors
Mauricio Zamora
Eldon Caldwell
Jose Garcia-Rodriguez
Jorge Azorin-Lopez
Miguel Cazorla
Copyright Year
2017
Book
Advances in Computational Intelligence

Print ISBN: 978-3-319-59146-9

Electronic ISBN: 978-3-319-59147-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59147-6_25

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