Abstract
In this article, we develop a novel strategy for automatic error classification and recovery in robotic assembly tasks. The strategy does not require error diagnosis. It allows for effective reduction of an undetermined number of error states to 4, without the need for further operator updates of error space. The strategy integrates existing methods for computer vision, active vision and active manipulation. Our solution is implemented in a generic software framework, which is independent from software and hardware for implementing error detection and allows for application in other assembly types and components. The value of our strategy was experimentally validated on a simple case, where we inserted a battery into a cell phone. The experiment was performed on 1500 assembly attempts and included 500 detected errors. The whole experiment ran for 42 hours, with no need for operator assistance or supervision. The resulting classification rate is 99.6% and the resulting recovery rate is 98.8%. The 6 unrecovered errors were successfully resolved in a successive assembly attempt.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
SPARC, euRobotics AISBL: Multi-annual roadmap - for robotics in europe (2016)
Robotics VO: A roadmap for u.s. robotics from internet to robotics (2016)
Vaaler, E.G., Seering, W.P.: A machine learning algorithm for automated assembly. In: Robotics and Automation, 1991. Proceedings., 1991 IEEE International Conference on. IEEE, pp 2231–2237 (1991). https://doi.org/10.1109/ROBOT.1991.131962
Newman, W.S., Zhao, Y., Pao, Y.-H.: Interpretation of force and moment signals for compliant peg-in-hole assembly. In: ICRA, pp 571–576 (2001). https://doi.org/10.1109/ROBOT.2001.932611
Jörg, S., Langwald, J., Stelter, J., Hirzinger, G., Natale, C.: Flexible robot-assembly using a multi sensory approach. In: Robotics and Automation (ICRA), 2000 IEEE International Conference on. IEEE, pp 3687–3694 (2000). https://doi.org/10.1109/ROBOT.2000.845306
Marvel, J.A., Newman, W.S., Gravel, D.P., Zhang, G., Wang, J., Fuhlbrigge, T.: Automated learning for parameter optimization of robotic assembly tasks utilizing genetic algorithms. In: Robotics and Biomimetics, 2008. ROBIO 2008. IEEE International Conference on. IEEE, pp 179–184 (2009). https://doi.org/10.1109/ROBIO.2009.4913000
Krabbe, E., Kristiansen, E., Hansen, L., Bourne, D.: Autonomous optimization of fine motions for robotic assembly. In: Robotics and Automation (ICRA), 2014 IEEE International Conference on. IEEE, pp 4168–4175 (2014). https://doi.org/10.1109/ICRA.2014.6907465
Rodriguez, A., Bourne, D., Mason, M., Rossano, F.G., Wang, J.: Failure detection in assembly: Force signature analysis. In: Automation Science and Engineering, 2010 IEEE Conference on. IEEE, pp 210–215 (2010). https://doi.org/10.1109/COASE.2010.5584452
Camarinha-Matos, L.M., Lopes, L.S., Barata, J.: Integration and learning in supervision of flexible assembly systems. IEEE Trans. Robot. Autom. 12(2), 202–219 (1996). https://doi.org/10.1109/70.488941
Loborg, P.: Error recovery in automation - an overview. In: AAAI Technical Report SS-94-04, pp 94–100 (2001)
Chen, F., Cannella, F., Huang, J., Sasaki, H., Fukuda, T.: A study on error recovery search strategies of electronic connector mating for robotic fault-tolerant assembly. J. Intell. Robot. Syst. pp. 257–271 (2015). https://doi.org/10.1007/s10846-015-0248-5
Hamner, B., Koterba, S., Shi, J., Simmons, R., Singh, S.: An autonomous mobile manipulator for assembly tasks. Auton. Robot. 28(1), 131 (2010). https://doi.org/10.1007/s10514-009-9142-y
Hayami, Y., Shi, P., Ramirez-Alpizar, I.G., Harada, K.: Multi-dimensional error identification during robotic snap assembly. In: Advances in Mechanism and Machine Science, pp 2189–2198 (2019). https://doi.org/10.1007/978-3-030-20131-9_217
Aronson, R.M., Bhatia, A., Jia, Z., Guillane-Bert, M., Bourne, D., Dubrawski, A., Mason, M.T.: Data-driven classification of screwdriving operations. In: Springer Proceedings in Advanced Robotics, pp 244–253 (2016). https://doi.org/10.1007/978-3-319-50115-4_22
Wu, Z., Hsieh, S.-J.: A realtime fuzzy petri net diagnoser for detecting progressive faults in plc based discrete manufacturing system. Int. J. Adv. Manuf. Technol. 61(1-4), 405–421 (2012). https://doi.org/10.1007/s00170-011-3689-4
Liu, Y., Jin, S., Lin, Z., Zheng, C., Yu, K.: Optimal sensor placement for fixture fault diagnosis using bayesian network. Assem. Autom. 31(2), 176–181 (2011). https://doi.org/10.1108/01445151111117764
Majdzik, P., Akielaszek-Witczak, A., Seybold, L., Stetter, R., Mrugalska, B.: A fault-tolerant approach to the control of a battery assembly system. Control. Eng. Pract. 55, 139–148 (2016). https://doi.org/10.1016/j.conengprac.2016.07.001
Hasegawa, M., Takata, M., Temmyo, T., Matsuka, H.: Modelling of exception handling in manufacturing cell control and its application to plc programming. In: Robotics and Automation, 1990. Proceedings., 1990 IEEE International Conference on. IEEE, pp 514–519 (1990). https://doi.org/10.1109/ROBOT.1990.126031
Laursen, J.S., Ellekilde, L.-P., Schultz, U.P.: Modelling reversible execution of robotic assembly. In: Robotica, pp 625–654 (2018). https://doi.org/10.1017/S0263574717000613
El-Wardany, T.I., Gao, D., Elbestawi, M.A.: Tool condition monitoring in drilling using vibration signature analysis. Int. J. Mach. Tools Manuf. 36(6), 687–711 (1996). https://doi.org/10.1016/0890-6955(95)00058-5
Hsueh, Y.-W., Yang, C.-Y.: Prediction of tool breakage in face milling using support vector machine. Int. J. Adv. Manuf. Technol. 37(9-10), 872–880 (2008). https://doi.org/10.1007/s00170-007-1034-8
Batal, I., Hauskrecht, M.: A supervised time series feature extraction technique using dct and dwt,. In: Machine Learning and Appications, Fourth International Conference on. IEEE, pp 735–739 (2009). https://doi.org/10.1109/ICMLA.2009.13
Chang, C.-C., Lin, C.-J.: Libsvm: a library from support vector machines, software available at http://www.csie.ntu.edu.tw/cjlin/libsvm (2001)
Manning, C.D., Raghavan, P., Schötze, H.: An introduction to information retrieval. Cambridge University Press, Cambridge (2008). ISBN: 0521865719
Staelin, C.: Parameter selection for support vector machines. HPL-2002-354 (R.1) (2003)
Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification (2016). https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
Shlens, J.: A tutorial on principal component analysis. In: Systems Neurobiology Laboratory, Salk Institute for Biological Studies. https://www.cc.gatech.edu/~lsong/teaching/CX4240spring16/pca_schlens.pdf (2005)
Edwards, C., Raskutti, B.: The effects of attribute scaling on the performance of support vector machines. In: AI 2004: Advances in Artificial Intelligence. Springer, pp 500–512 (2004). https://doi.org/10.1007/978-3-540-30549-1_44
Acknowledgements
We would like to thank Nishant Kelkar for his contributions to methodology and software. The authors would also like to thank Alberto Rodriguez and Robert Paolini for their help.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Kristiansen, E., Nielsen, E.K., Hansen, L. et al. A Novel Strategy for Automatic Error Classification and Error Recovery for Robotic Assembly in Flexible Production. J Intell Robot Syst 100, 863–877 (2020). https://doi.org/10.1007/s10846-020-01248-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10846-020-01248-3