Abstract
According to the ISO/TS 15066, human safety in quasi-static impact situations in human-robot collaboration is assessed first by identifying all high-risk impact situations and then by measuring maximal and steady-state values the impact force and pressure at these possibly critical situations. This means that if something is changed in a collaborative application, the ISO/TS 15066 requires that the risk analysis and the force measurements must be redone, which severely limits the flexibility of a robotic system. In this paper, a physics guided boosted decision tree is proposed as a tool to assess human safety. The basic hypothesis is that a physics guided boosted decision tree can be trained to estimates the peak impact force for a given impact velocity, robot configuration, an impact point on the robot and a human body part. Based on experimental measurements done with the Universal Robots UR10e and on a simple mathematical model of an impact between a point on a robot and a point on a human body part, a feature vector is generated as an input to the boosted decision tree. After the training using Matlab’s Least-squares boosting algorithm, the boosted decision tree can predict the measured peak impact force with a relative error of less than 9% thus supporting the basic hypothesis. However, the predictions of the trained boosted decision tree are valid only for the case of a quasi-static impact in a vertical direction between a robot’s end-effector and a back of human’s non-dominant hand.
This work was supported by the DR.KORS - Dynamic reconfigurability of collaborative robot systems project, funded by FFG - Österreichische Forschungsförderungsgesellschaft, project number 864892.
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References
Technical Specification: ISO/TS 15066:2016, Robots and robotic devices: collaborative robots. Technical report, The International Organization for Standardization (2016)
Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O.: Progress and prospects of the human-robot collaboration. Auton. Robots 42(5), 957–975 (2018)
Echávarri, J., Ceccarelli, M., Carbone, G., Alén, C., Muñoz, J.L., Díaz, A., Munoz-Guijosa, J.M.: Towards a safety index for assessing head injury potential in service robotics. Adv. Robot. 27(11), 831–844 (2013)
Forssell, U., Lindskog, P.: Combining semi-physical and neural network modeling: an example of its usefulness. IFAC Proc. Vol. 30(11), 767–770 (1997)
Garofalo, G., Mansfeld, N., Jankowski, J., Ott, C.: Sliding mode momentum observers for estimation of external torques and joint acceleration. In: IEEE International Conference on Robotics and Automation (ICRA) (2019)
Géron, A.: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media, Sebastopol (2019)
Haddadin, S., Albu-Schäffer, A., Hirzinger, G.: Safety evaluation of physical human-robot interaction via crash-testing. In: Robotics: Science and Systems, vol. 3, pp. 217–224. Citeseer (2007)
Haddadin, S., De Luca, A., Albu-Schäffer, A.: Robot collisions: a survey on detection, isolation, and identification. IEEE Trans. Rob. 33(6), 1292–1312 (2017)
Haddadin, S., Haddadin, S., Khoury, A., Rokahr, T., Parusel, S., Burgkart, R., Bicchi, A., Albu-Schäffer, A.: On making robots understand safety: embedding injury knowledge into control. Int. J. Robot. Res. 31(13), 1578–1602 (2012)
Hoo, K.A., Sinzinger, E.D., Piovoso, M.J.: Improvements in the predictive capability of neural networks. J. Process Control 12(1), 193–202 (2002)
Jia, X., Willard, J., Karpatne, A., Read, J., Zwart, J., Steinbach, M., Kumar, V.: Physics guided RNNs for modeling dynamical systems: a case study in simulating lake temperature profiles. In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 558–566. SIAM (2019)
Karpatne, A., Atluri, G., Faghmous, J.H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., Kumar, V.: Theory-guided data science: a new paradigm for scientific discovery from data. IEEE Trans. Knowl. Data Eng. 29(10), 2318–2331 (2017)
Karpatne, A., Watkins, W., Read, J., Kumar, V.: Physics-guided neural networks (PGNN): an application in lake temperature modeling. arXiv preprint arXiv:1710.11431 (2017)
Kovincic, N., Gattringer, H., Müller, A., Weyrer, M., Schlotzhauer, A., Kaiser, L., Brandstötter, M.: A model-based strategy for safety assessment of a robot arm interacting with humans. PAMM 19(1), e201900247 (2019)
Mansfeld, N., Hamad, M., Becker, M., Marin, A.G., Haddadin, S.: Safety map: a unified representation for biomechanics impact data and robot instantaneous dynamic properties. IEEE Robot. Autom. Lett. 3(3), 1880–1887 (2018)
Ren, H., Stewart, R., Song, J., Kuleshov, V., Ermon, S.: Learning with weak supervision from physics and data-driven constraints. AI Mag. 39(1), 27–38 (2018)
Schlotzhauer, A., Kaiser, L., Wachter, J., Brandstotter, M., Hofbaur, M.: On the trustability of the safety measures of collaborative robots: 2D Collision-force-map of a sensitive manipulator for safe HRC. In: IEEE International Conference on Automation Science and Engineering (CASE) (2019)
Stewart, R., Ermon, S.: Label-free supervision of neural networks with physics and domain knowledge. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
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Kovincic, N., Gattringer, H., Müller, A., Brandstötter, M. (2020). A Boosted Decision Tree Approach for a Safe Human-Robot Collaboration in Quasi-static Impact Situations. In: Zeghloul, S., Laribi, M., Sandoval Arevalo, J. (eds) Advances in Service and Industrial Robotics. RAAD 2020. Mechanisms and Machine Science, vol 84. Springer, Cham. https://doi.org/10.1007/978-3-030-48989-2_26
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