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Context-Aware Human-Robot Collaborative Assembly

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Abstract

In human-robot collaborative manufacturing, industrial robots would work alongside the human workers who jointly perform the assigned tasks. Recent research work revealed that recognised human motions could be used as input for industrial robots control. However, the human-robot collaboration team still cannot work symbiotically. In response to the requirement, this chapter explores the potential of establishing context awareness between a human worker and an industrial robot for human-robot collaborative assembly . The context awareness between the human worker and the industrial robot is established by applying gesture recognition , human motion recognition and Augmented Reality (AR) based worker instruction technologies. Such a system works in a cyber-physical environment and is demonstrated by case studies.

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References

  1. J. Krüger, T.K. Lien, A. Verl, Cooperation of human and machines in assembly lines. CIRP Ann. Technol. 58, 628–646 (2009)

    Article  Google Scholar 

  2. S.A. Green, M. Billinghurst, X. Chen, G.J. Chase, Human-robot collaboration: a literature review and augmented reality approach in design. Int. J. Adv. Robot. Syst. 1–18 (2008)

    Google Scholar 

  3. P.R. Cohen, H.J. Levesque, Teamwork. Nous 487–512 (1991)

    Google Scholar 

  4. L.S. Vygotsky, Mind in society: the development of higher psychological processes (Harvard University Press, 1980)

    Google Scholar 

  5. P.R. Cohen, H.J. Levesque, persistence, intention, and commitment. Reason. About Actions Plans 297–340 (1990)

    Google Scholar 

  6. C. Breazeal et al., Humanoid robots as cooperative partners for people. Int. J. Humanoid Robot. 1, 1–34 (2004)

    Article  Google Scholar 

  7. Z.M. Bi, L. Wang, Advances in 3D data acquisition and processing for industrial applications. Robot. Comput. Integr. Manuf. 26, 403–413 (2010)

    Article  Google Scholar 

  8. B. Schmidt, L. Wang, Depth camera based collision avoidance via active robot control. J. Manuf. Syst. 33, 711–718 (2014)

    Article  Google Scholar 

  9. H. Liu, L. Wang, Gesture recognition for human-robot collaboration: a review (J. Ind. Ergon, Int, 2017). doi:10.1016/j.ergon.2017.02.004

    Google Scholar 

  10. A. Bauer, D. Wollherr, M. Buss, Human–robot collaboration: a survey. Int. J. Humanoid Robot. 5, 47–66 (2008)

    Article  Google Scholar 

  11. S. Mitra, T. Acharya, Gesture recognition: a survey. IEEE Trans. Syst. Man, Cybern. Part C Appl. Rev. 37, 311–324 (2007)

    Article  Google Scholar 

  12. R. Parasuraman, T.B. Sheridan, C.D. Wickens, A model for types and levels of human interaction with automation. IEEE Trans. Syst. Man Cybern. Part A Syst. Humans. 30, 286–297 (2000)

    Article  Google Scholar 

  13. T.E. Starner, Visual Recognition of American Sign Language Using Hidden Markov Models (1995)

    Google Scholar 

  14. T. Starner, J. Weaver, A. Pentland, Real-time american sign language recognition using desk and wearable computer based video, in IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 20 (1998), pp. 1371–1375

    Google Scholar 

  15. N.R. Howe, M.E. Leventon, W.T. Freeman, Bayesian reconstruction of 3D human motion from single-camera video. NIPS 99, 820–826 (1999)

    Google Scholar 

  16. Y. Katsuki, Y. Yamakawa, M. Ishikawa, High-speed human/robot hand interaction system, in Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts (2015), pp. 117–118

    Google Scholar 

  17. M. Elmezain, A. Al-Hamadi, J. Appenrodt, B. Michaelis, A hidden markov model-based continuous gesture recognition system for hand motion trajectory, in 19th International Conference on Pattern Recognition (2008), pp. 1–4

    Google Scholar 

  18. Y. Matsumoto, A. Zelinsky, An algorithm for real-time stereo vision implementation of head pose and gaze direction measurement, in Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (2000), pp. 499–504

    Google Scholar 

  19. J.P. Wachs, M. Kölsch, H. Stern, Y. Edan, Vision-based hand-gesture applications. Commun. ACM 54, 60–71 (2011)

    Article  Google Scholar 

  20. J. Suarez, R.R. Murphy, Hand gesture recognition with depth images: a review. IEEE RO-MAN 411–417 (2012)

    Google Scholar 

  21. P. Doliotis, A. Stefan, C. McMurrough, D. Eckhard, V. Athitsos, Comparing gesture recognition accuracy using color and depth information, in Proceedings of the 4th International Conference on Pervasive Technologies Related to Assistive Environments (2011), p. 20

    Google Scholar 

  22. T. Sharp et al., Accurate, robust, and flexible real-time hand tracking, in Proceeding CHI (2015), p. 8

    Google Scholar 

  23. A. Erol, G. Bebis, M. Nicolescu, R.D. Boyle, X. Twombly, Vision-based hand pose estimation: a review. Comput. Vis. Image Underst. 108, 52–73 (2007)

    Article  Google Scholar 

  24. T. Labs, Myo (2015) https://www.myo.com/

  25. Y. Zhang, C. Harrison, Tomo: wearable, low-cost electrical impedance tomography for hand gesture recognition, in Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (2015), pp. 167–173

    Google Scholar 

  26. N. Haroon, A.N. Malik, Multiple hand gesture recognition using surface EMG signals. J. Biomed. Eng. Med. Imaging 3, 1 (2016)

    Article  Google Scholar 

  27. S. Roy, S. Ghosh, A. Barat, M. Chattopadhyay, D. Chowdhury, Artif. Intell. Evol. Comput. Engin. Syst. 357–364 (2016)

    Google Scholar 

  28. Google, Project soli (2015) https://www.google.com/atap/project-soli/

  29. J. Smith et al., Electric field sensing for graphical interfaces. Comput. Graph. Appl. IEEE 18, 54–60 (1998)

    Article  Google Scholar 

  30. F. Adib, C.-Y. Hsu, H. Mao, D. Katabi, F. Durand, Capturing the human figure through a wall. ACM Trans. Graph. 34, 219 (2015)

    Article  Google Scholar 

  31. F. Adib, D. Katabi, See through walls with WiFi! ACM. 43 (2013)

    Google Scholar 

  32. F. Adib, Z. Kabelac, D. Katabi, R.C. Miller, 3D tracking via body radio reflections. Usenix NSDI 14 (2014)

    Google Scholar 

  33. J. Letessier, F. Bérard, Visual tracking of bare fingers for interactive surfaces, in Proceedings of the 17th annual ACM symposium on User interface software and technology (2004), pp. 119–122

    Google Scholar 

  34. D. Weinland, R. Ronfard, E. Boyer, A survey of vision-based methods for action representation, segmentation and recognition. Comput. Vis. Image Underst. 115, 224–241 (2011)

    Article  Google Scholar 

  35. D.G. Lowe, Object recognition from local scale-invariant features, in Proceedings of 7th IEEE International Conference on Computer Vision, vol. 2 (1999), pp. 1150–1157

    Google Scholar 

  36. H. Bay, T. Tuytelaars, L. Van Gool, Computer vision—ECCV (2006), pp. 404–417

    Google Scholar 

  37. E. Rublee, V. Rabaud, K. Konolige, G. Bradski, ORB: an efficient alternative to SIFT or SURF, in IEEE International Conference on Computer Vision (ICCV) (2011), pp. 2564–2571

    Google Scholar 

  38. S. Belongie, J. Malik, J. Puzicha, Shape matching and object recognition using shape contexts. Pattern Anal. Mach. Intell. IEEE Trans. 24, 509–522 (2002)

    Article  Google Scholar 

  39. B. Allen, B. Curless, Z. Popović, Articulated body deformation from range scan data. ACM Trans. Graph. 21, 612–619 (2002)

    Article  Google Scholar 

  40. I. Oikonomidis, N. Kyriazis, A.A. Argyros, Efficient model-based 3D tracking of hand articulations using Kinect. BMVC 1, 3 (2011)

    Google Scholar 

  41. R. Cutler, M. Turk, View-Based Interpretation of Real-Time Optical Flow for Gesture Recognition (1998), p. 416

    Google Scholar 

  42. J.L. Barron, D.J. Fleet, S.S. Beauchemin, Performance of optical flow techniques. Int. J. Comput. Vis. 12, 43–77 (1994)

    Article  Google Scholar 

  43. C. Thurau, V. Hlaváč, Pose primitive based human action recognition in videos or still images, in IEEE Conference on Computer Vision and Pattern Recognition (2008), pp. 1–8

    Google Scholar 

  44. Q. Pu, S. Gupta, S. Gollakota, S. Patel, Whole-home gesture recognition using wireless signals, in Proceedings of the 19th Annual International Conference on Mobile Computing & Networking (2013), pp. 27–38

    Google Scholar 

  45. R. Ronfard, C. Schmid, B. Triggs, Computer Vision (2002), pp. 700–714

    Google Scholar 

  46. S.-J. Lee, C.-S. Ouyang, S.-H. Du, A neuro-fuzzy approach for segmentation of human objects in image sequences. Syst. Man Cybern. Part B Cybern. IEEE Trans. 33, 420–437 (2003)

    Article  Google Scholar 

  47. D. Tang, H.J. Chang, A. Tejani, T.-K. Kim, Latent regression forest: structured estimation of 3D articulated hand posture, in IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 3786–3793

    Google Scholar 

  48. J. Taylor, J. Shotton, T. Sharp, A. Fitzgibbon, The vitruvian manifold: inferring dense correspondences for one-shot human pose estimation, in IEEE Conference on Computer Vision and Pattern Recognition (2012), pp. 103–110

    Google Scholar 

  49. J. Han, L. Shao, D. Xu, J. Shotton, Enhanced computer vision with microsoft kinect sensor: a review. Cybern. IEEE Trans. 43, 1318–1334 (2013)

    Article  Google Scholar 

  50. Y. Li, Hand gesture recognition using Kinect, in IEEE 3rd International Conference on Software Engineering and Service Science (2012), pp. 196–199

    Google Scholar 

  51. D. Comaniciu, V. Ramesh, P. Meer, Real-time tracking of non-rigid objects using mean shift. IEEE Conf. Comput. Vis. Pattern Recognit. 2, 142–149 (2000)

    Google Scholar 

  52. S. Thrun, W. Burgard, D. Fox, Probabilistic Robotics (MIT Press, 2005)

    Google Scholar 

  53. R.E. Kalman, A new approach to linear filtering and prediction problems. J. Fluids Eng. 82, 35–45 (1960)

    Google Scholar 

  54. S. Haykin, Kalman Filtering and Neural Networks, vol. 47 (Wiley, 2004)

    Google Scholar 

  55. E. Wan, R. Van Der Merwe, The unscented Kalman filter for nonlinear estimation, in IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium (2000), pp. 153–158

    Google Scholar 

  56. K. Okuma, A. Taleghani, N. De Freitas, J.J. Little, D.G. Lowe, Computer Vision (Springer, 2004), pp. 28–39

    Google Scholar 

  57. S. Oron, A. Bar-Hillel, D. Levi, S. Avidan, Locally orderless tracking, in IEEE Conference on Computer Vision and Pattern Recognition 1940–1947 (2012)

    Google Scholar 

  58. J. Kwon, K.M. Lee, Tracking by sampling trackers, in IEEE International Conference on Computer Vision (2011), pp. 1195–1202

    Google Scholar 

  59. J. Kwon, K.M. Lee, F.C. Park, Visual tracking via geometric particle filtering on the affine group with optimal importance functions, in IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 991–998

    Google Scholar 

  60. R. Gao, L. Wang, R. Teti, D. Dornfeld, S. Kumara, M. Mori, M. Helu, Cloud-enabled prognosis for manufacturing. CIRP Ann. Technol. 64(2), 749–772 (2015)

    Article  Google Scholar 

  61. T. Li, S. Sun, T.P. Sattar, J.M. Corchado, Fight sample degeneracy and impoverishment in particle filters: a review of intelligent approaches. Expert Syst. Appl. 41, 3944–3954 (2014)

    Article  Google Scholar 

  62. T. Li, T.P. Sattar, S. Sun, Deterministic resampling: unbiased sampling to avoid sample impoverishment in particle filters. Sig. Process. 92, 1637–1645 (2012)

    Article  Google Scholar 

  63. Rincón J.M. Del, D. Makris, C.O. Uruňuela, J.-C. Nebel, Tracking human position and lower body parts using Kalman and particle filters constrained by human biomechanics. Syst. Man. Cybern. Part B Cybern. IEEE Trans. 41, 26–37 (2011)

    Article  Google Scholar 

  64. D.A. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77, 125–141 (2008)

    Article  Google Scholar 

  65. Z. Kalal, J. Matas, K. Mikolajczyk, Pn learning: bootstrapping binary classifiers by structural constraints, in IEEE Conference on Computer Vision and Pattern Recognition (2010), pp. 49–56

    Google Scholar 

  66. B. Babenko, M.-H. Yang, S. Belongie, Visual tracking with online multiple instance learning, in IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 983–990

    Google Scholar 

  67. A.W.M. Smeulders et al., Visual tracking: an experimental survey. Pattern Anal. Mach. Intell. IEEE Trans. 36, 1442–1468 (2014)

    Article  Google Scholar 

  68. L.E. Peterson, K-nearest neighbor. Scholarpedia 4, 1883 (2009)

    Article  Google Scholar 

  69. A.D. Wilson, A.F. Bobick, Parametric hidden markov models for gesture recognition. Pattern Anal. Mach. Intell. IEEE Trans. 21, 884–900 (1999)

    Article  Google Scholar 

  70. S. Lu, J. Picone, S. Kong, Fingerspelling Alphabet Recognition Using A Two-level Hidden Markov Modeli in Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (2013), p. 1

    Google Scholar 

  71. J. McCormick, K. Vincs, S. Nahavandi, D. Creighton, S. Hutchison, Teaching a digital performing agent: artificial neural network and hidden Markov model for recognising and performing dance movement, in Proceedings of the 2014 International Workshop on Movement and Computing (2014), p. 70

    Google Scholar 

  72. S.-Z. Yu, Hidden semi-Markov models. Artif. Intell. 174, 215–243 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  73. M.A. Hearst, S.T. Dumais, E. Osman, J. Platt, B. Scholkopf, Support vector machines. IEEE Intell. Syst. their Appl. 13, 18–28 (1998)

    Article  Google Scholar 

  74. M.E. Tipping, Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1, 211–244 (2001)

    MathSciNet  MATH  Google Scholar 

  75. B. Schiilkopf, The kernel trick for distances, in Proceedings of the 2000 Conference on Advances in Neural Information Processing Systems, vol. 13 (2001), p. 301

    Google Scholar 

  76. A. Cenedese, G.A. Susto, G. Belgioioso, G.I. Cirillo, F. Fraccaroli, Home automation oriented gesture classification from inertial measurements. Autom. Sci. Eng. IEEE Trans. 12, 1200–1210 (2015)

    Article  Google Scholar 

  77. K. Feng, F. Yuan, Static hand gesture recognition based on HOG characters and support vector machines, in 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (2013), pp. 936–938

    Google Scholar 

  78. D. Ghimire, J. Lee, Geometric feature-based facial expression recognition in image sequences using multi-class adaboost and support vector machines. Sensors 13, 7714–7734 (2013)

    Article  Google Scholar 

  79. O. Patsadu, C. Nukoolkit, B. Watanapa, Human gesture recognition using Kinect camera, in International Joint Conference on Computer Science and Software Engineering (2012), pp. 28–32

    Google Scholar 

  80. R.E. Schapire, Nonlinear estimation and classification (Springer, 2003), pp. 149–171

    Google Scholar 

  81. Y. Freund, R.E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  82. S. Celebi, A.S. Aydin, T.T. Temiz, T. Arici, Gesture recognition using skeleton data with weighted dynamic time warping. VISAPP 1, 620–625 (2013)

    Google Scholar 

  83. E.J. Keogh, M.J. Pazzani, Derivative dynamic time warping. SDM 1, 5–7 (2001)

    Google Scholar 

  84. S.S. Haykin, Neural Networks and Learning Machines, vol. 3 (Pearson Education Upper Saddle River, 2009)

    Google Scholar 

  85. T.H.H. Maung, Real-time hand tracking and gesture recognition system using neural networks. World Acad. Sci. Eng. Technol. 50, 466–470 (2009)

    Google Scholar 

  86. H. Hasan, S. Abdul-Kareem, Static hand gesture recognition using neural networks. Artif. Intell. Rev. 41, 147–181 (2014)

    Article  Google Scholar 

  87. T. D’Orazio, G. Attolico, G. Cicirelli, C. Guaragnella, A neural network approach for human gesture recognition with a kinect sensor. ICPRAM 741–746 (2014)

    Google Scholar 

  88. A.H. El-Baz, A.S. Tolba, An efficient algorithm for 3D hand gesture recognition using combined neural classifiers. Neural Comput. Appl. 22, 1477–1484 (2013)

    Article  Google Scholar 

  89. K. Subramanian, S. Suresh, Human action recognition using meta-cognitive neuro-fuzzy inference system. Int. J. Neural Syst. 22, 1250028 (2012)

    Article  Google Scholar 

  90. Z.-H. Zhou, J. Wu, W. Tang, Ensembling neural networks: many could be better than all. Artif. Intell. 137, 239–263 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  91. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–444 (2015)

    Article  Google Scholar 

  92. J. Schmidhuber, Deep learning in neural networks: an overview. Neural Netw 61, 85–117 (2015)

    Article  Google Scholar 

  93. J. Tompson, M. Stein, Y. Lecun, K. Perlin, Real-time continuous pose recovery of human hands using convolutional networks. ACM Trans. Graph 33, 169 (2014)

    Article  Google Scholar 

  94. K. Simonyan, A. Zisserman, Two-stream convolutional networks for action recognition in videos. Adv. Neural Inf. Process. Syst. 568–576 (2014)

    Google Scholar 

  95. J. Nagi et al., Max-pooling convolutional neural networks for vision-based hand gesture recognition, in IEEE International Conference on Signal and Image Processing Applications (2011), pp. 342–347

    Google Scholar 

  96. A. Jain, J. Tompson, Y. LeCun, C. Bregler, Computer Vision (2014), pp. 302–315

    Google Scholar 

  97. K. Li, Y. Fu, Prediction of human activity by discovering temporal sequence patterns. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1644–1657 (2014)

    Article  Google Scholar 

  98. M.S. Ryoo, Human activity prediction: early recognition of ongoing activities from streaming videos, in IEEE International Conference on Computer Vision (2011), pp. 1036–1043

    Google Scholar 

  99. W. Ding, K. Liu, F. Cheng, J. Zhang, Learning hierarchical spatio-temporal pattern for human activity prediction. J. Vis. Commun. Image Represent. 35, 103–111 (2016)

    Article  Google Scholar 

  100. L.R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition. IEEE Proc. 77, 257–286 (1989)

    Article  Google Scholar 

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  1. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

    Lihui Wang & Xi Vincent Wang

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  1. Lihui Wang
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Correspondence to Lihui Wang .

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Wang, L., Wang, X.V. (2018). Context-Aware Human-Robot Collaborative Assembly. In: Cloud-Based Cyber-Physical Systems in Manufacturing . Springer, Cham. https://doi.org/10.1007/978-3-319-67693-7_11

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  • DOI: https://doi.org/10.1007/978-3-319-67693-7_11

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