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Object-Shape Recognition Based on Haptic Image

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Intelligent Robotics and Applications (ICIRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10462))

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Abstract

This paper introduces a concept of haptic image which contains sufficient haptic data acquired from haptic interaction. Deep mining and proper processing of haptic image may extend the applications to many fields. An approach of haptic shape recognition based on haptic image is presented. Firstly, a glove-like device mounted with pressure sensors and fiber sensors is utilized to acquire haptic image during the exploration of object shape. Secondly, pre-processing of haptic image is conducted including smoothing and standardization. Thirdly, haptic flow is extracted from haptic image as shape feature. Haptic flow proposed in this paper is the displacement of contact points between adjacent time intervals, which is inspired by optical flow. At last, a self-organizing map (SOM) is employed for the classification and recognition of the explored shapes. In the experiment, a recognition test of 4 different shapes, including cube, block, cylinder and sphere, is conducted and the mean recognition rate is approximately 90%.

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References

  1. Schill, J., Laaksonen, J., Przybylski, M., Kyrki, V., Asfour, T., Dillmann, R.: Learning continuous grasp stability for a humanoid robot hand based on tactile sensing. In: 4th IEEE RAS & EMBS International Conference, pp. 1901–1906. IEEE (2012)

    Google Scholar 

  2. Shahabi, C., Kolahdouzan, M.R., Barish, G., Zimmermann, R., Yao, D., Fu, K., Zhang, L.: Alternative techniques for the efficient acquisition of haptic data. In: ACM SIGMETRICS Performance Evaluation Review, vol. 29, no. 1, pp. 334–335. ACM, Berlin (2001)

    Google Scholar 

  3. Gao, Y., Hendricks, L.A., Kuchenbecker, K.J., Darrell, T.: Deep learning for tactile understanding from visual and haptic data. In: 2016 IEEE International Conference on Robotics and Automation, pp. 536–543. IEEE (2016)

    Google Scholar 

  4. Nakano, T., Uozumi, S., Johansson, R., Ohnishi, K.: A quantization method for haptic data lossy compression. In: 2015 IEEE International Conference on Mechatronics, pp. 126–131. IEEE (2015)

    Google Scholar 

  5. Kaneko, T., Ito, S., Sakaino, S., Tsuji, T.: Haptic data compression for rehabilitation databases. In: 13th International Workshop on Advanced Motion Control, pp. 657–662. IEEE (2015)

    Google Scholar 

  6. Lee, J.-Y., Payandeh, S.: Haptic data compression. In: Haptic Teleoperation Systems, pp. 61–85. Springer, Cham (2015). doi:10.1007/978-3-319-19557-5_5

  7. Nadjarbashi, O.F., Abdi, H., Nahavandi, S.: Applying inverse just-noticeable-differences of velocity to position data for haptic data reduction. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 440–445. IEEE (2015)

    Google Scholar 

  8. Burka, A., Hu, S., Helgeson, S., Krishnan, S., Gao, Y., Hendricks, L.A., Kuchenbecker, K.: Proton: a visuo-haptic data acquisition system for robotic learning of surface properties. In: 2016 IEEE International Conference on Multisensor Fusion and Intergration for Intelligent Systems, pp. 58–65. IEEE (2016)

    Google Scholar 

  9. Gemici, M.C., Saxena, A.: Learning haptic representation for manipulating deformable food objects. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 638–646. IEEE (2014)

    Google Scholar 

  10. Park, N., Zhu, W., Jung, Y., McLaughlin, M., Jin, S.: Utility of haptic data in recognition of user state. In: Proceedings of HCI International, vol. 11. Las Vegas, Nevada (2005)

    Google Scholar 

  11. Oz, C., Leu, M.: Recognition of finger spelling of American sign language with artificial neural network using position/orientation sensors and data glove. In: Advances in Neural Networks ISNN 2005, pp. 812–812. Springer, New York (2005)

    Google Scholar 

  12. Ji, W., Zhao, D., Cheng, F., Xu, B., Zhang, Y., Wang, J.: Automatic recognition vision system guided for apple harvesting robot. Comput. Electr. Eng. 38(5), 1186–1195 (2015)

    Article  Google Scholar 

  13. Yatani, K., Banovic, N., Truong, K.: SpaceSense: representing geographical information to visually impaired people using spatial tactile feedback. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 415–424. ACM, New York (2012)

    Google Scholar 

  14. Hirano, Y., Kitahama, K.I., Yoshizawa, S.: Image-based object recognition and dexterous hand/arm motion planning using RRTs for grasping in cluttered scene. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2041–2046. IEEE (2005)

    Google Scholar 

  15. Choi, J., Seo, B.K., Lee, D., Park, H., Park, J.I.: RGB-D camera-based hand shape recognition for human-robot interaction. In: 2013 44th International Symposium on Robotics, pp. 1–2. IEEE (2013)

    Google Scholar 

  16. Johnsson, M., Balkenius, C.: Experiments with proprioception in a self-organizing system for haptic perception. In: Towards Autonomous Robotic Systems, pp. 239–245 (2007)

    Google Scholar 

  17. Dipietro, L., Sabatini, A.M., Dario, P.: A survey of glove-based systems and their applications. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 38(4), 461–482 (2008)

    Google Scholar 

  18. Marion, G.C.: Wireless communication glove apparatus for motion tracking, gesture recognition, data transmission, and reception in extreme environments. In: Proceedings of the 2009 ACM symposium on Applied Computing, pp. 172–173. ACM, New York (2009)

    Google Scholar 

  19. Okada, K., Kojima, M., Tokutsu, S., Maki, T., Mori, Y., Inaba, M.: Multi-cue 3D object recognition in knowledge-based vision-guided humanoid robot system. In: 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3217–3222. IEEE (2007)

    Google Scholar 

  20. Yabuta, Y., Mizumoto, H., Arii, S.: Binocular robot vision system with shape recognition. In: ICCAS2007 International Conference on Control, Automation and Systems, pp. 2299–2302. IEEE (2007)

    Google Scholar 

  21. Bhattacharjee, T., Rehg, J.M., Kemp, C.C.: Haptic classification and recognition of objects using a tactile sensing forearm. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4090–4097. IEEE (2012)

    Google Scholar 

  22. Gorges, N., Navarro, S.E., Goger, D., Worn, H.: Haptic object recognition using passive joints and haptic key features. In: 2010 IEEE International Conference on Robotics and Automation, pp. 2349–2355. IEEE (2010)

    Google Scholar 

  23. Luzhnica, G., Simon, J., Lex, E., Pammer, V.: A sliding window approach to natural hand gesture recognition using a custom data glove. In: 2016 IEEE Symposium on 3D User Interfaces, pp. 81–90. IEEE (2016)

    Google Scholar 

  24. Craig, J.J.: Introduction to robotics: mechanics and control, vol. 3, pp. 48–70. Pearson Prentice Hall, Upper Saddle River (2005)

    Google Scholar 

  25. Gibson, J.J.: The Perception of the Visual World. Houghton Mifflin, Oxford (1950)

    Google Scholar 

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Acknowledgement

This research was supported by Natural Science Foundation of China under grants 61473088.

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Authors and Affiliations

  1. School of Instrument Science and Engineering, Southeast University, Sipailou 2#, Nanjing, 210096, Jiangsu, China

    Yi Gong, Juan Wu, Miao Wu & Xiao Han

Authors
  1. Yi Gong
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  2. Juan Wu
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  3. Miao Wu
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  4. Xiao Han
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Corresponding author

Correspondence to Juan Wu .

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Editors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    YongAn Huang

  2. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Hao Wu

  3. Institute of Industrial Research, University of Portsmouth, Portsmouth, United Kingdom

    Honghai Liu

  4. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

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Gong, Y., Wu, J., Wu, M., Han, X. (2017). Object-Shape Recognition Based on Haptic Image. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10462. Springer, Cham. https://doi.org/10.1007/978-3-319-65289-4_39

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  • DOI: https://doi.org/10.1007/978-3-319-65289-4_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65288-7

  • Online ISBN: 978-3-319-65289-4

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