Abstract
The rising interest in collaborative robotics leads to research solutions in order to increase robot interaction with the environment. The development of methods that permit robots to recognize and track human motion is relevant for safety and collaboration matters. A large quantity of data can be measured in real time by Microsoft Kinect®, a well-known low-cost depth sensor, able to recognize human presence and to provide postural information by extrapolating a skeleton. However, the Kinect sensor tracks motion with relatively low accuracy and jerky behavior. For this reason, the effective use in industrial applications in which the measurement of arm velocity is required can be unsuitable. The present work proposes a filtering method that allows the measurement of more accurate velocity values of human arm, based on row data provided by the Kinect sensor. The estimation of arm motion is achieved by a Kalman filter based on a kinematic model and by the imposition of fixed lengths for the skeleton links detected by the sensor. The development of the method is supported by experimental tests. The achieved results suggest the practical applicability of the developed algorithms.
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References
Tsarouchi, P., Makris, S., Chryssolouris, G.: Human–robot interaction review and challenges on task planning and programming. Int. J. Comput. Integr. Manuf. 29(8), 916–931 (2016)
Vasic, M., Billard, A.: Safety issues in human-robot interactions. In: 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, pp. 197–204 (2013)
Schmidt, B., Wang, L.: Depth camera based collision avoidance via active robot control. J. Manuf. Syst. 33(4), 711–718 (2014)
Scimmi, L.S., Melchiorre, M., Mauro, S., Pastorelli, S.P.: Implementing a vision-based collision avoidance algorithm on a UR3 Robot. In: 2019 23rd International Conference on Mechatronics Technology (ICMT), SALERNO (2019)
Liu, H., Wang, L.: Gesture recognition for human-robot collaboration: a review. Int. J. Ind. Ergon. 68, 355–367 (2018)
Han, J., Shao, L., Xu, D., Shotton, J.: Enhanced computer vision with Microsoft Kinect sensor: a review. IEEE Trans. Cybern. 43, 1318–1334 (2013)
Ding, H., Heyn, J., Matthias, B., Staab, H.: Structured collaborative behavior of industrial robots in mixed human-robot environments. In: 2013 IEEE International Conference on Automation Science and Engineering (CASE), Madison, WI, pp. 1101–1106 (2013)
Microsoft Kinect SDK. https://developer.microsoft.com/windows/kinect/. Accessed 20 Feb 2020
Wang, Q., Kurillo, G., Ofli, F., Bajcsy, R.: Evaluation of pose tracking accuracy in the first and second generations of Microsoft Kinect. In: 2015 International Conference on Healthcare Informatics, Dallas, TX, pp. 380–389 (2015)
Melchiorre, M., Scimmi, L.S., Pastorelli, S.P., Mauro, S.: Collison avoidance using point cloud data fusion from multiple depth sensors: a practical approach. In: 2019 23rd International Conference on Mechatronics Technology (ICMT), Salerno, Italy (2019)
Chen, S.Y.: Kalman filter for robot vision: a survey. Trans. Ind. Electron. 59, 4409–4420 (2012)
Edwards, M., Green, R.: Low-latency filtering of kinect skeleton data for video game control. In: Proceedings of the 29th International Conference on Image and Vision Computing New Zealand, IVCNZ 2014, pp. 190–195 (2014)
Melchiorre, M., Scimmi, L.S., Mauro, S., Pastorelli, S.: Influence of human limb motion speed in a collaborative hand-over task. In: Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2018, vol. 2 (2018)
Scimmi, L.S., Melchiorre, M., Mauro, S., Pastorelli, S.: Multiple collision avoidance between human limbs and robot links algorithm in collaborative tasks. In: Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2018, vol. 2 (2018)
Mauro, S., Pastorelli, S., Scimmi, L.S.: Collision avoidance algorithm for collaborative robotics. Int. J. Autom. Technol. 11(3), 481–489 (2017). https://doi.org/10.20965/ijat.2017.p0481. ISSN 1881-7629
Brown, R.G., Hwang, P.Y.C.: Introduction to Random Signals and Applied Kalman Filtering, 4th edn. Wiley, New York (2012)
Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation: Algorithms and Software for Information Extraction. Wiley, New York (2001)
Isen, F.W.: DSP for MATLAB and LabVIEW I: Fundamentals of Discrete Signal Processing. Morgan & Claypool Publishers, San Rafael (2008)
OptiTrack Trio. https://optitrack.com/products/v120-trio/. Accessed 20 Feb 2020
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Palmieri, P., Melchiorre, M., Scimmi, L.S., Pastorelli, S., Mauro, S. (2021). Human Arm Motion Tracking by Kinect Sensor Using Kalman Filter for Collaborative Robotics. In: Niola, V., Gasparetto, A. (eds) Advances in Italian Mechanism Science. IFToMM ITALY 2020. Mechanisms and Machine Science, vol 91. Springer, Cham. https://doi.org/10.1007/978-3-030-55807-9_37
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DOI: https://doi.org/10.1007/978-3-030-55807-9_37
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