Abstract
The multi-joints motion planning method by online auto-learning mode based on neural network which can realize new trajectories’ planning and control of multi-joints manipulator is extensively used in the field of trajectory tracking, planning and control of intelligent robot, especially for rigid manipulator to achieve the discovery of new trajectory, real-time self-learning and control itself. Such method can achieve the real-time self-learning and control of nonlinear complex trajectories, by taking advantage of the global optimal approximation performance of the neural network. The functional relationship can be established between the current trajectory information and trajectory information at the previous N moments by neural network, and because of this, information related to trajectory can be predicted, thereby realizing on-line self-learning of multi-joints. This method is used for the real-time control of intelligent mechanical arm which can reduce the difficulty of numerical solution greatly, improve the efficiency of calculation and boost the ability of real-time self-learning.
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References
Li, Z., Li, J., Kang, Y.: Adaptive robust coordinated control of multiple mobile manipulators interacting with rigid environments. Automatica -Oxford- 46(12), 2028–2034 (2010)
Yu, S., Yu, X., Shirinzadeh, B., Man, Z.: Continuous finite-time control for robotic manipulators with terminal sliding mode. Automatica 41(11), 1957–1964 (2005)
Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Rob. Res. 30(7), 846–894 (2011)
Canny, J., Reif, J.: New lower bound techniques for robot motion planning problems. In: 28th Annual Symposium on Foundations of Computer Science (sfcs 1987) (2008)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional neural networks. In: European Conference on Computer Vision (2013)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)
Kim, Y.: Convolutional neural networks for sentence classification. Eprint Arxiv (2014)
Hermans, E.J.: Stress-related noradrenergic activity prompts large-scale neural network reconfiguration. Science 334(6059), 1151–1153 (2011)
Gan, T.Y.: A time series model for estimating the weekly winter maximum temperature of northwest territories. J. Appl. Meteorol. 34(4), 847–860 (2010)
Chen, Y., Lei, S., Lei, W.: Traffic flow prediction with big data: A deep learning based time series model. In: IEEE INFOCOM 2017 -IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (2017)
Doukhan, P., Latour, A., Oraichi, D.: A simple integer-valued bilinear time series model. Adv. Appl. Probab. 38(2), 559–578 (2006)
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Guo, Y., Yang, F., Zhang, J., Li, P., Lv, B. (2022). Research on Multi-joints Motion Planning Method by Online Auto-Learning Mode Based on Neural Network. In: Jia, Y., Zhang, W., Fu, Y., Yu, Z., Zheng, S. (eds) Proceedings of 2021 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 804. Springer, Singapore. https://doi.org/10.1007/978-981-16-6324-6_46
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DOI: https://doi.org/10.1007/978-981-16-6324-6_46
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