Abstract
This research work suggests one kind of approach in developing a natural human–robot interface that will be used for control of four wheels differentially steered mobile robot. The designed system is capable of extracting, understanding and learning a sequence of full body gestures and poses, that were previously captured in standard RGB and IC DEPTH videos. The starting set of robot commands in first study case, includes the following 4 postures: START, WAIT A MINUTE, STOP and SLOW DOWN, while in the second study case 5 gestures were realized: START, TURN RIGHT, TURN LEFT, SPEED UP and SLOW, while command STOP is realized as pose. The special feature of proposed classifier system is fact that human user is always in visual domain of camera but without fixation for defined position or orientation. Two different kinds of classifiers were implemented: first, support vector machine (SVM) classifier and second, based on multiple interconnected FUZZY Logic systems. The research showed satisfactory results in small classification error, simple human operator training and user comfort.
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References
Burger, B., I. Ferran’e, F. Lerasle, and G. Infantes: Two-handed gesture recognition and fusion with speech to command a robot, Autonomous Robots, vol. 32, pp. 129–147. (2012).
Crammer, K. and Y.Singer: On the algorithmic implementation of multiclass kernelbased vector machines, Journal of Machine Learning Research 2, pp. 265–292.(2001).
Ghobadi, S.E., O.E.Loepprich, F.Ahmadov and J.Bernshausen: Real Time Hand Based Robot Control Using Multimodal Images, IAENG International Journal of Computer Science, Vol.35, No.4, (2008).
Guo, G., Li, S. Z. and K.L.Chan: Support vector machines for face recognition, Image and Vision Computing 19(9-10), pp. 631–638.(2001).
Hanai, M., R.Sato, S.Fukuma, S-I.Mori, T.Shimozawa and N.Hayashi: Physical Motion Analysis System in Driving using Image Processing, Proc. of ISPACS 2009, Kanazawa, Japan, pp. 123–126 (2009).
Hsu, R.CH, Lin, C-C., Lai, C-H. and C-T. Liu: Remotely controlling of mobile robots using gesture captured by the Kinect and recognized by machine learning method. Proc. SPIE 8662, Intelligent Robots and Computer Vision XXX: Algorithms and Techniques, (2013).
Iba S., Van de Weghe J.M., Paredis C.J.J. and P.K.K.Khosla:An Architecture for Gesture-Based Control of Mobile Robots, Proceedings of the IEEE/RSJ IROS, pp. 851–857.(1999).
iPiSoft, http://www.ipisoft.com/products.php
Kean S., Hall J. and P.Perry. Meet the Kinect, Technology in Action, (2011).
Lin, C.T. and C. S. G. Lee : Neuro Fuzzy Systems - A Neuro-Fuzzy Synergism to Intelligent Systems, Prentice Hall.(1996)
Microsoft Corporation: Kinect for Windows, http://www.microsoft.com/en-us/kinectforwindows/
Mitra, S. and T. Acharya,: Gesture recognition: A survey, Systems, IEEE Transactions on Man, and Cybernetics, Part C, vol. 37, no. 3, pp. 311–324.(2007).
OpenNI Consortium: OpenNI, The standard framework for 3D sensing, http://www.openni.org/
Pontil, M. and A.Verri: Support vector machines for 3D object recognition, IEEE Trans. Pattern Anal. Machine Intell. 20(6), pp. 637–646.(1998).
Pourmehr, S., Monajjemi, V., Wawerla, J., Vaughan, R., and G.Mori: A Robust Integrated System for Selecting and Commanding Multiple Mobile Robots. Proc. of 2013 IEEE ICRA, Karlsruhe, Germany, pp.2859–2864.(2013).
Raheja, L., R.Shyam, U.Kumar and P.B.Prasad:Real-Time Robotic Hand Control Using Hang Gestures, Machine Learning and Computing, (2010).
Vasilijević, G., Jagodin, N. and Z.Kovačić: Kinect-Based Robot Teleoperation by Velocities Control in the Joint/Cartesian Frames, Proc.of SYROCO 2012, pp.878–883, (2012).
Waldherr S., Romero R. snd S.Thrun: A Gesture Based Interface for Human-Robot Interaction, Journal Autonomous Robots, Volume 9, Issue 2, pp. 151–173, (2000).
Yang, H.-D., A.-Y. Park, and S.-W. Lee: Gesture spotting and recognition for human–robot interaction. IEEE Transactions on Robotics. (2007).
Acknowledgments
This chapter is supported by Serbian Ministry of Science under the grants TR-35003, III-44008, SNSF “CARE-robotics” project IZ74Z0_137361/1 and by the bilateral Serbia-Portugal “COLBAR” project 451-03-02338/2012-14/12.
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Katić, D., Radulović, P., Spasojević, S., Đurović, Ž., Rodić, A. (2014). Advanced Gesture and Pose Recognition Algorithms Using Computational Intelligence and Microsoft KINECT Sensor. In: Rodić, A., Pisla, D., Bleuler, H. (eds) New Trends in Medical and Service Robots. Mechanisms and Machine Science, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-05431-5_13
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DOI: https://doi.org/10.1007/978-3-319-05431-5_13
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