Abstract
Under the wave of rapid industrial development, automated production is gradually shifting to intelligent and customized production. The human-robot collaboration (HRC) system, as an effective way to improve the intelligence and flexibility of automated production, has received great attention from people. Recognizing human intentions quickly and accurately is the foundation for a safe and efficient HRC. In this work, we propose a novel approach of intention recognition, which transforms intention recognition into the recognition of feature images. The trajectory of human movement is projected and reconstructed into feature images, and transfer learning is implemented on Alexnet to complete the recognition of feature images to indirectly realize the recognition of the intention. We evaluate the proposed approach on a self-made dataset. The experimental results show our method can accurately recognize the intention in the early stage of human motion.
This work is partially supported by the National Natural Science Foundation of China (61773351), the Program for Science & Technology Innovation Talents in Universities of Henan Province (20HASTIT031).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, Y., Ge, S.S.: Human-robot collaboration based on motion intention estimation. IEEE/ASME Trans. Mechatron. 19(3), 1007–1014 (2014)
Weitschat, R., Ehrensperger, J., Maier, M., Aschemann, H.: Safe and efficient human-robot collaboration Part I: estimation of human arm motions. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1993–1999 (2018)
Pellegrinelli, S., Admoni, H., Javdani, S., Srinivasa, S.: Human-robot shared workspace collaboration via hindsight optimization, pp. 831–838 (2016)
Zanchettin, A.M., Ceriani, N.M., Rocco, P., Ding, H., Matthias, B.: Safety in human-robot collaborative manufacturing environments: metrics and control. IEEE Trans. Autom. Sci. Eng. 13(2), 882–893 (2016)
Hoffman, G.: Evaluating fluency in human-robot collaboration. IEEE Trans. Hum. Mach. Syst. 49(3), 209–218 (2019)
Liu, C., et al.: Goal inference improves objective and perceived performance in human-robot collaboration (2018)
Ding, W., Liu, K., Cheng, F., Zhang, J.: Learning hierarchical spatio-temporal pattern for human activity prediction. J. Vis. Commun. Image Represent. 35, 103–111 (2016)
Wu, C., Han, J., Li, X.: Time-asymmetric 3d convolutional neural networks for action recognition. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 21–25 (2019)
Martinez, J., Black, M.J., Romero, J.: On human motion prediction using recurrent neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4674–4683 (2017)
Liu, R., Liu, C.: Human motion prediction using adaptable recurrent neural networks and inverse kinematics. IEEE Control Syst. Lett. 5(5), 1651–1656 (2021)
Pérez-D’Arpino, C., Shah, J.A.: Fast target prediction of human reaching motion for cooperative human-robot manipulation tasks using time series classification. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 6175–6182 (2015)
Liu, H., Wang, L.: Human motion prediction for human-robot collaboration. J. Manuf. Syst. 44, 287–294 (2017)
Yan, L., Gao, X., Zhang, X., Chang, S.: Human-robot collaboration by intention recognition using deep LSTM neural network. In: 2019 IEEE 8th International Conference on Fluid Power and Mechatronics (FPM), pp. 1390–1396 (2019)
Liu, Z., Liu, Q., Xu, W., Liu, Z., Zhou, Z., Chen, J.: Deep learning-based human motion prediction considering context awareness for human-robot collaboration in manufacturing. Procedia CIRP 83, 272–278 (2019)
Wang, W., Li, R., Chen, Y., Diekel, Z.M., Jia, Y.: Facilitating human-robot collaborative tasks by teaching-learning-collaboration from human demonstrations. IEEE Trans. Autom. Sci. Eng. 16(2), 640–653 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Dong, M., Peng, J., Ding, S., Wang, Z. (2021). Transfer Learning - Based Intention Recognition of Human Upper Limb in Human - Robot Collaboration. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13014. Springer, Cham. https://doi.org/10.1007/978-3-030-89098-8_55
Download citation
DOI: https://doi.org/10.1007/978-3-030-89098-8_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89097-1
Online ISBN: 978-3-030-89098-8
eBook Packages: Computer ScienceComputer Science (R0)