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Human–Robot Interaction Interface

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Advanced Technologies in Modern Robotic Applications

Abstract

Human–robot interaction is an advanced technology and plays an increasingly important role in robot applications. This chapter first gives a brief introduction to various human–robot interfaces and several technologies of human–robot interaction using visual sensors and electroencephalography (EEG) signals. Next, a hand gesture-based robot control system is developed using Leap Motion, with noise suppression, coordinate transformation, and inverse kinematics. Then, another hand gesture control, which is one of natural user interfaces, is then developed based on a parallel system. ANFIS and SVM algorithms are employed to realize the classification. We also investigate controlling the commercialized Spykee mobile robot using EEG signals transmitted by the Emotiv EPOC neuroheadset. The Emotiv headset is connected to the OpenViBE to control a virtual manipulator moving in 3D Cartesian space, using a P300 speller.

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References

  1. Green, S.A., Billinghurst, M., Chen, X., Chase, G.: Human-robot collaboration: a literature review and augmented reality approach in design (2008)

    Google Scholar 

  2. Bauer, A., Wollherr, D., Buss, M.: Human-robot collaboration: a survey. Int. J. Humanoid Robot. 5(01), 47–66 (2008)

    Article  Google Scholar 

  3. Lu, J.-M., Hsu, Y.-L.: Contemporary Issues in Systems Science and Engineering. Telepresence robots for medical and homecare applications, p. 725. Wiley, New Jersey (2015)

    Google Scholar 

  4. Arai, T., Kato, R., Fujita, M.: Assessment of operator stress induced by robot collaboration in assembly. CIRP Ann. Manuf. Technol. 59(1), 5–8 (2010)

    Article  Google Scholar 

  5. Bechar, A., Edan, Y.: Human-robot collaboration for improved target recognition of agricultural robots. Ind. Robot: Int. J. 30(5), 432–436 (2003)

    Article  Google Scholar 

  6. Matthias, B., Kock, S., Jerregard, H., Kallman, M., Lundberg, I., Mellander, R.: Safety of collaborative industrial robots: certification possibilities for a collaborative assembly robot concept. In: 2011 IEEE International Symposium on Assembly and Manufacturing (ISAM), pp. 1–6 IEEE (2011)

    Google Scholar 

  7. Morabito, V.: Big Data and Analytics. Big data driven business models, pp. 65–80. Springer, Berlin (2015)

    Google Scholar 

  8. Top 10 emerging technologies of 2015. https://agenda.weforum.org. (2015)

  9. Scassellati, B., Tsui, K.M.: Co-robots: Humans and robots operating as partners (2015)

    Google Scholar 

  10. Reardon, C., Tan, H., Kannan, B., Derose, L.: Towards safe robot-human collaboration systems using human pose detection. In: 2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA) (2015)

    Google Scholar 

  11. Kronander, K., Billard, A.: Learning compliant manipulation through kinesthetic and tactile human-robot interaction. IEEE Trans. Haptics 7(3), 367–380 (2014)

    Article  Google Scholar 

  12. Peternel, L., Petric, T., Oztop, E., Babic, J.: Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach. Autonomous Robots 36(1–2), 123–136 (2014)

    Article  Google Scholar 

  13. Rozo, L., Calinon, S., Caldwell, D.G., Jimnez, P., Torras, C.: Learning collaborative impedance-based robot behaviors. In: Association for the Advancement of Artificial Intelligence (2013)

    Google Scholar 

  14. Ganesh, G., Takagi, A., Osu, R., Yoshioka, T., Kawato, M., Burdet, E.: Two is better than one: physical interactions improve motor performance in humans. Sci. Rep. 4(7484), 3824–3824 (2014)

    Google Scholar 

  15. Burdet, E., Osu, R., Franklin, D.W., Milner, T.E., Kawato, M.: The central nervous system stabilizes unstable dynamics by learning optimal impedance. Nature 414(6862), 446–449 (2001). doi:10.1038/35106566

    Article  Google Scholar 

  16. Burdet, E., Ganesh, G., Yang, C., Albu-Schaffer, A.: Interaction force, impedance and trajectory adaptation: by humans, for robots. Springer Tracts Adv. Robot. 79, 331–345 (2010)

    Article  Google Scholar 

  17. Yang, C., Ganesh, G., Haddadin, S., Parusel, S., Albu-Schäeffer, A., Burdet, E.: Human-like adaptation of force and impedance in stable and unstable interactions. In: IEEE Transactions on Robotics, vol.27(5) (2011)

    Google Scholar 

  18. Ajoudani, A., Tsagarakis, N.G., Bicchi, A.: Tele-impedance: preliminary results on measuring and replicating human arm impedance in tele operated robots. In: 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 216 – 222 (2011)

    Google Scholar 

  19. Gradolewski, D., Tojza, P.M., Jaworski, J., Ambroziak, D., Redlarski, G., Krawczuk, M.: Arm EMG Wavelet-Based Denoising System. Springer International Publishing, Berlin (2015)

    Google Scholar 

  20. Ajoudani, A.: Tele-impedance: teleoperation with impedance regulation using a bodycmachine interface. Int. J. Robot. Res. 31(13), 1642–1656 (2012)

    Article  Google Scholar 

  21. Dragan, A.D., Bauman, S., Forlizzi, J., Srinivasa, S.S.: Effects of robot motion on human-robot collaboration. In: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 51–58. ACM (2015)

    Google Scholar 

  22. Saktaweekulkit, K., Maneewarn, T.: Motion classification using imu for human-robot interaction. In: 2010 International Conference on Control Automation and Systems (ICCAS), pp. 2295–2299. IEEE (2010)

    Google Scholar 

  23. Shi, G.Y., Zou, Y.X., Li, W.J., Jin, Y.F., Guan, P.: Towards multi-classification of human motions using micro imu and svm training process. In: Advanced Materials Research, vol. 60, pp. 189–193. Trans Tech Publication (2009)

    Google Scholar 

  24. Yoshimoto, H., Arita, D., Taniguchi et al., R.-I.: Real-time human motion sensing based on vision-based inverse kinematics for interactive applications. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 318–321. IEEE (2004)

    Google Scholar 

  25. Starner, T., Weaver, J., Pentland, A.: Real-time american sign language recognition using desk and wearable computer based video. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1371–1375 (1998)

    Article  Google Scholar 

  26. Shanableh, T., Assaleh, K., Al-Rousan, M.: Spatio-temporal feature-extraction techniques for isolated gesture recognition in arabic sign language. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 37(3), 641–650 (2007)

    Article  Google Scholar 

  27. Liang, R.-H., Ouhyoung, M.: A sign language recognition system using hidden markov model and context sensitive search. Proc. ACM Symp. Virtual Real. Softw. Technol. 96, 59–66 (1996)

    Google Scholar 

  28. Starner, T., Pentland, A.: Real-time american sign language recognition from video using hidden markov models. In: Motion-Based Recognition, pp. 227–243. Springer (1997)

    Google Scholar 

  29. Kim, J., Mastnik, S., André, E.: Emg-based hand gesture recognition for realtime biosignal interfacing. In: Proceedings of the 13th international conference on Intelligent user interfaces, pp. 30–39. ACM (2008)

    Google Scholar 

  30. Moradi, H., Lee, S.: Joint limit analysis and elbow movement minimization for redundant manipulators using closed form method. In: Advances in Intelligent Computing, pp. 423–432. Springer (2005)

    Google Scholar 

  31. Fang, C., Ding, X.: A set of basic movement primitives for anthropomorphic arms. In: 2013 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 639–644. IEEE (2013)

    Google Scholar 

  32. Pan, J.J., Xu, K.: Leap motion based 3D. gesture. CHINA. SCIENCEPAPER 10(2), 207–212 (2015)

    MathSciNet  Google Scholar 

  33. Jiang, Y.C.: Menacing motion-sensing technology, different leap motion. PC. Fan 11, 32–33 (2013)

    Google Scholar 

  34. Chen, S., Ma, H., Yang, C., Fu, M.: Hand gesture based robot control system using leap motion. In: Intelligent Robotics and Applications, pp. 581–591. Springer (2015)

    Google Scholar 

  35. V-rep introduction. http://www.v-rep.eu/

  36. UR10 introduction. http://news.cmol.com/2013/0530/33267.html

  37. Barretthand introduction. http://wiki.ros.org/Robots/BarrettHand

  38. Qian, K., Jie, N., Hong, Y.: Developing a gesture based remote human-robot interaction system using Kinect. Int. J. Smart Home 7(4), 203–208 (2013)

    Google Scholar 

  39. Casiez, G., Roussel, N., Vogel, D.: 1 filter: a simple speed-based low-pass filter for noisy input in interactive systems. In: Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems, pp. 2527–2530. (Austin, TX, USA, 2012)

    Google Scholar 

  40. Craig, J.J.: Introduction to Rbotics: Mechanics and Control, 3rd edn. China Machine Press, Beijing (2006)

    Google Scholar 

  41. Li, C., Ma, H., Yang, C., Fu, M.: Teleoperation of a virtual icub robot under framework of parallel system via hand gesture recognition. In: 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1469–1474. IEEE (2014)

    Google Scholar 

  42. Wang, F.-Y.: Parallel system methods for management and control of complex systems. Control Decis. 19, 485–489 (2004)

    MATH  Google Scholar 

  43. S. W. S. (2012), ““wiki for the icub simulator specifications from its installation to its use”.” http://www.eris.liralab.it/wiki/

  44. Yet another robot platform. http://yarp0.sourceforge.net/

  45. Wachs, J.P., Kölsch, M., Stern, H., Edan, Y.: Vision-based hand-gesture applications. Commun. ACM 54(2), 60–71 (2011)

    Article  Google Scholar 

  46. Hsu, R.-L., Abdel-Mottaleb, M., Jain, A.K.: Face detection in color images. Pattern Anal. Mach. Intell. 24(5), 696–706 (2002)

    Article  Google Scholar 

  47. Hu, M.-K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)

    Article  MATH  Google Scholar 

  48. Granlund, G.H.: Fourier preprocessing for hand print character recognition. IEEE Trans. Comput. 100(2), 195–201 (1972)

    Article  MathSciNet  MATH  Google Scholar 

  49. Nedjah, N.e.: Adaptation of fuzzy inference system using neural learning, fuzzy system engineering: theory and practice. In: Studies in Fuzziness and Soft Computing, pp. 53–83 (2001)

    Google Scholar 

  50. Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)

    Google Scholar 

  51. Corke, P.I.: Robotics toolbox (2008)

    Google Scholar 

  52. Zhu, G.C., Wang, T.M., Chou, W.S., Cai, M.: Research on augmented reality based teleoperation system. Acta Simul. Syst. Sin. 5, 021 (2004)

    Google Scholar 

  53. TF, C.: History and evolution of electroencephalographic instruments and techniques. J. Clin. Neurophysiol. 10(4), 476–504 (1993)

    Article  Google Scholar 

  54. Grude, S., Freeland, M., Yang, C., Ma, H.: Controlling mobile spykee robot using emotiv neuro headset. In: Control Conference (CCC), 2013 32nd Chinese, pp. 5927–5932. IEEE (2013)

    Google Scholar 

  55. Hoffmann, A.: Eeg signal processing and emotivs neuro headset, Hessen: sn (2010)

    Google Scholar 

  56. Sanei, S., Chambers, J.A.: EEG Signal Processing. Wiley, New Jerssey (2013)

    Google Scholar 

  57. Jasper, H.H.: The ten twenty electrode system of the international federation. Electroencephal. Clin. Neurophysiol. 10, 371–375 (1958)

    Google Scholar 

  58. Lotte, F.: Les interfaces cerveau-ordinateur: Conception et utilisation en réalité virtuelle. Revue Technique et Science Informatiques 31(3), 289–310 (2012)

    Article  Google Scholar 

  59. Carmena, J.M., Lebedev, M.A., Crist, R.E., O’Doherty, J.E., Santucci, D.M., Dimitrov, D.F., Patil, P.G., Henriquez, C.S., Nicolelis, M.A.: Learning to control a brain-machine interface for reaching and grasping by primates. Plos Biol. 1(2), 193–208 (2003)

    Article  Google Scholar 

  60. J. D. R. Milln, R. Frdric, and a. W. G. Josep, Mourio, “Non-invasive brain-actuated control of a mobile robot by human eeg,” IEEE Trans. on Biomedical Engineering, Special Issue on Brain-Machine Interfaces, vol. 51, no. 6, pp. 1026–1033, 2004

    Google Scholar 

  61. Wang, Y., Gao, X., Hong, B., Jia, C., Gao, S.: Brain-computer interfaces based on visual evoked potentials. IEEE Engineering in Medicine & Biology Magazine the Quarterly Magazine of the Engineering in Medicine & Biology Society 27(5), 64–71 (2008)

    Article  Google Scholar 

  62. Diez, P.F., Mut, V.A., Perona, E.M.A., Leber, E.L.: Asynchronous bci control using high-frequency ssvep. Journal of Neuroengineering & Rehabilitation 8(2), 642–650 (2011)

    Google Scholar 

  63. Bashashati, A., Fatourechi, M., Ward, R.K., Birch, G.E.: A survey of signal processing algorithms in brainccomputer interfaces based on electrical brain signals. Journal of Neural Engineering 4(2), R32–R57 (2007)

    Article  Google Scholar 

  64. J. Ding, G. Sperling, and R. Srinivasan, “Attentional modulation of ssvep power depends on the network tagged by the flicker frequency.,” Cerebral Cortex, vol. 16, no. 7, pp. 1016–1029(14), 2006

    Google Scholar 

  65. H. Ekanayake, “P300 and emotiv epoc: Does emotiv epoc capture real eeg?,” P300 and Emotiv EPOC: Does Emotiv EPOC capture real EEG? - ResearchGate, 2011

    Google Scholar 

  66. Vidal, J.-J.: Toward direct brain-computer communication. Annual review of Biophysics and Bioengineering 2(1), 157–180 (1973)

    Article  Google Scholar 

  67. T. J. Sullivan, S. R. Deiss, T. P. Jung, and G. Cauwenberghs, “A brain-machine interface using dry-contact, low-noise eeg sensors,” in In Circuits and Systems, 2008. ISCAS 2008. IEEE International Symposium on, pp. 1986–1989, 2008

    Google Scholar 

  68. A. Malki, C. Yang, N. Wang, and Z. Li, “Mind guided motion control of robot manipulator using eeg signals,” in Information Science and Technology (ICIST), 2015 5th International Conference on, 2015

    Google Scholar 

  69. Coppeliarobotics, “Vrep description.” http://www.coppeliarobotics.com, 2014

  70. Piccione, F., Priftis, K., Tonin, P., Vidale, D., Furlan, R., Cavinato, M., Merico, A., Piron, L.: Task and stimulation paradigm effects in a p300 brain computer interface exploitable in a virtual environment: A pilot study. Psychnology Journal 6(1), 99–108 (2008)

    Google Scholar 

  71. Chen, W.D., Zhang, J.H., Zhang, J.C., Li, Y., Qi, Y., Su, Y., Wu, B., Zhang, S.M., Dai, J.H., Zheng, X.X.: A p300 based online brain-computer interface system for virtual hand control. Journal of Zhejiang University Science C 11(08), 587–597 (2010)

    Article  Google Scholar 

  72. Renard, Y., Lotte, F., Gibert, G., Congedo, M., Maby, E., Delannoy, V., Bertrand, O., Cuyer, A.: Openvibe: An open-source software platform to design, test, and use brain-computer interfaces in real and virtual environments. Presence Teleoperators & Virtual Environments 19(1), 35–53 (2010)

    Article  Google Scholar 

  73. Inria, “P300: Old p300 speller.” http://openvibe.inria.fr/openvibe-p300-speller, 2014

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Authors and Affiliations

  1. Key Lab of Autonomous Systems and Networked Control, Ministry of Education, South China University of Technology, Guangzhou, China

    Chenguang Yang

  2. Centre for Robotics and Neural Systems, Plymouth University, Devon, UK

    Chenguang Yang

  3. School of Automation, Beijing Institute of Technology, Beijing, China

    Hongbin Ma & Mengyin Fu

  4. State Key Lab of Intelligent Control and Decision of Complex System, Beijing Institute of Technology, Beijing, China

    Hongbin Ma & Mengyin Fu

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  1. Chenguang Yang
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  2. Hongbin Ma
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  3. Mengyin Fu
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Correspondence to Chenguang Yang or Hongbin Ma .

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Yang, C., Ma, H., Fu, M. (2016). Human–Robot Interaction Interface. In: Advanced Technologies in Modern Robotic Applications. Springer, Singapore. https://doi.org/10.1007/978-981-10-0830-6_8

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  • DOI: https://doi.org/10.1007/978-981-10-0830-6_8

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