Abstract
A solution for intuitive robot command and fast robot programming is presented to assemble pins in car doors. Static and dynamic gestures are used to instruct an industrial robot in the execution of the assembly task. An artificial neural network (ANN) was used in the recognition of twelve static gestures and a hidden Markov model (HMM) architecture was used in the recognition of ten dynamic gestures. Results of these two architectures are compared with results displayed by a third architecture based on support vector machine (SVM). Results show recognition rates of 96 % and 94 % for static and dynamic gestures when the ANN and HMM architectures are used, respectively. The SVM architecture presents better results achieving recognition rates of 97 % and 96 % for static and dynamic gestures, respectively.
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References
Ren, Z., Yuan, J., Meng, J., Zhang, Z.: Robust Part-Based Hand Gesture Recognition Using Kinect Sensor. IEEE Trans. Multimed. 15, 1110–1120 (2013)
Huang, P.-C., Jeng, S.-K.: Human body pose recognition from a single-view depth camera. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2144–2149. IEEE (2012)
Seal, A., Bhattacharjee, D., Nasipuri, M., Basu, D.K.: Thermal human face recognition based on GappyPCA. In: 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013), pp. 597–600. IEEE (2013)
Kirishima, T., Sato, K., Chihara, K.: Real-time gesture recognition by learning and selective control of visual interest points. IEEE Trans. Pattern Anal. Mach. Intell. 27, 351–364 (2005)
Lambrecht, J., Kruger, J.: Spatial programming for industrial robots based on gestures and Augmented Reality. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 466–472. IEEE (2012)
Oz, C., Leu, M.C.: Linguistic properties based on American Sign Language isolated word recognition with artificial neural networks using a sensory glove and motion tracker. Neurocomputing 70, 2891–2901 (2007)
Neto, P., Pires, J.N., Moreira, A.P.: High-level programming and control for industrial robotics: using a hand-held accelerometer-based input device for gesture and posture recognition. Ind. Robot. An. Int. J. 37, 137–147 (2010)
Neto, P., Pires, J.N., Moreira, A.P.: Accelerometer-based control of an industrial robotic arm. In: RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 1192–1197. IEEE (2009)
Mitra, S., Acharya, T.: Gesture Recognition: A Survey. IEEE Trans. Syst. Man Cybern. Part C Applications Rev. 37, 311–324 (2007)
Yang, J., Bang, W., Choi, E., Cho, S., Oh, J., Cho, J., Kim, S., Ki, E., Kim, D.: A 3D hand-drawn gesture input device using fuzzy ARTMAP-based recognizer. J. Syst. Cybern. Informatics 4, 1–7 (2006)
Yamashita, Y., Tani, J.: Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment. PLoS Comput. Biol. 4 (2008)
Peng, B., Qian, G.: Online gesture spotting from visual hull data. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1175–1188 (2011)
Badi, H.S., Hussein, S.: Hand posture and gesture recognition technology. Neural Comput. Appl. 25, 871–878 (2014)
Wang, X., Xia, M., Cai, H., Gao, Y., Cattani, C.: Hidden-Markov-Models-Based Dynamic Hand Gesture Recognition. Math. Probl. Eng. (2012)
Bertsch, F.A., Hafner, V. V.: Real-time dynamic visual gesture recognition in human-robot interaction. In: 9th IEEE-RAS International Conference on Humanoid Robots, pp. 447–453. IEEE (2009)
Kurakin, A., Zhang, Z., Liu, Z.: A real time system for dynamic hand gesture recognition with a depth sensor. In: 20th European Signal Processing Conference (EUSIPCO 2012), pp. 1975–1979 (2012)
Zhang, Y., Zhang, L., Hossain, M.A.: Adaptive 3D facial action intensity estimation and emotion recognition. Expert Syst. Appl. 42, 1446–1464 (2015)
El-Baz, A.H., Tolba, A.S.: An efficient algorithm for 3D hand gesture recognition using combined neural classifiers. Neural Comput. Appl. 22, 1477–1484 (2012)
Badi, H., Hussein, S.H., Kareem, S.A.: Feature extraction and ML techniques for static gesture recognition. Neural Comput. Appl. 25, 733–741 (2014)
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Mendes, N., Neto, P., Safeea, M., Moreira, A.P. (2016). Online Robot Teleoperation Using Human Hand Gestures: A Case Study for Assembly Operation. In: Reis, L., Moreira, A., Lima, P., Montano, L., Muñoz-Martinez, V. (eds) Robot 2015: Second Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-319-27149-1_8
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DOI: https://doi.org/10.1007/978-3-319-27149-1_8
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