Robotic tactile recognition and adaptive grasping control based on CNN-LSTM

被引：0
|
作者：
Hui W. [1 ]
Li H. [1 ]
Chen M. [2 ]
Song A. [1 ]
机构：
[1] School of Instrument Science and Engineering, Southeast University, Nanjing
[2] Aerospace System Engineering Shanghai, Shanghai
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2019年 / 40卷 / 01期
关键词：
Convolutional neural network(CNN); Grasping control; Long-short term memory(LSTM) neural network; Robot dexterous hand; Tactile sequence;
D O I：
10.19650/j.cnki.cjsi.J1804211
中图分类号：
学科分类号：
摘要：
Object recognition based on tactile is of great significance to the robotic fine operation and man-machine interaction. Combining deep learning theory, a robot tactile sequence recognition method is proposed based on the fusion model of convolutional neural network (CNN) and long short term memory (LSTM) neural network. Fourteen classification test and four classification test are conducted with the tactile dataset constructed with 14 kinds of experiment samples, and the correct recognition rates of 94.2% and 95.0% are achieved, respectively. On this basis, a stable grasping system combining object online recognition is built, which effectively improves the grasping effect of the robot dexterous hand. The experiment results show that compared with the basic convolution neural network model and simple long short term memory neural network model, the proposed fusion model has better recognition capability for the tactile sequence and can be applied to the object on-line recognition and stable grasping control practically. © 2019, Science Press. All right reserved.
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页码：211 / 218
页数：7
相关论文
共 21 条
  • [1] Qin H.H., Song A.G., Mo Y.T., Et al., Design of human-machine system with two-handed force feedback, Chinese Journal of Scientific Instrument, 39, 7, pp. 66-73, (2018)
  • [2] Gu H.W., Research on object touch recognition and adaptive grasping by robot dexterous hand, (2017)
  • [3] Schneider A., Sturm J., Stachniss C., Et al., Object identification with tactile sensors using bag-of-features, International Conference on Intelligent Robots and Systems, pp. 243-248, (2009)
  • [4] Xiao W., Sun F.C., Liu H.P., Tactile analysis and modeling of dexterous robotic hand, Robot, 35, 4, pp. 394-401, (2013)
  • [5] Madry M., Bo L., Kragic D., Et al., ST-HMP: Unsupervised spatio-temporal feature learning for tactile data, International Conference on Robotics and Automation, pp. 2262-2269, (2014)
  • [6] Aggarwal A., Kampmann P., Lemburg J., Et al., Haptic object recognition in underwater and deep-sea environments, Journal of Field Robotics, 32, 1, pp. 167-185, (2015)
  • [7] Wettrls N., Loeb G.E., Haptic feature extraction from a biomimetic tactile sensor: Force, contact location and curvature, International Conference on Robotics and Biomimetics, pp. 2471-2478, (2012)
  • [8] Chu V., Mcmahon I., Riano L., Et al., Robotic learning of haptic adjectives through physical interaction, Robotics & Autonomous Systems, 63, P3, pp. 279-292, (2015)
  • [9] Lecun Y., Bottou L., Bengio Y., Et al., Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86, 11, pp. 2278-2324, (1998)
  • [10] Hochreiter S., Schmidhuber J., Long short-term memory, Neural Computation, 9, 8, pp. 1735-1780, (1997)
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