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Model Construction of Multi-target Grasping Robot Based on Digital Twin

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Book cover Intelligent Robotics and Applications (ICIRA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13456))

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Abstract

In the unstructured environment, how to grasp the target object accurately and flexibly as human is always a hot spot in the field of robotics research. This paper proposes a multi-object grasping method based on digital twin to solve the problem of multi-object grasping in unstructured environment. The twin model of robot grasping process is constructed to realize multi-object collision-free grasping. Firstly, this paper analyzes the composition of digital twin model based on the five-dimensional digital twin structure model, and constructs the twin model of unknown target grasping process combining with multi-target grasping process of robot. Then, a digital model of the key elements in the multi-target grasping process of the robot is established to realize the interaction between the physical entity and the virtual model, and an evaluation model of the virtual model grasping strategy is established to screen the optimal grasping strategy. Finally, based on the twin model of multi-target grasping process, a multi-target grasping process based on digital twin is constructed to realize the multi-target stable grasping without collision.

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References

  1. Han, Y., Chu, Z., Zhao, K.: Arget positioning method in binocular vision manipulator control based on improved canny operator. Multimedia Tools Appl. 13(79), 9599–9614 (2020)

    Article  Google Scholar 

  2. Zhukov, A.: Improvement and extension of the capabilities of a manipulator based on the probe of an atomic-force microscope operating in the hybrid mode. Instrum. Exp. Tech. 62(3), 416–420 (2019)

    Article  Google Scholar 

  3. Wang, L., Yan, J., Cao, T.: Manipulator control law design based on Backstepping and ADRC Methods. Lect. Notes Electr. Eng. 705, 261–269 (2021)

    Article  Google Scholar 

  4. Tuegel, E., Ingraffea, A.R., Eason, T.G.: Reengineering aircraft structural life prediction using a digital twin. Int. J. Aerosp. Eng. 2011, 1687–5966 (2011)

    Article  Google Scholar 

  5. Tao, F., Cheng, J., Qi, Q.: Digital twin-driven product design, manufacturing and service with big data. The Int. J. Adv. Manuf. Technol. 94(9–12), 3563–3576 (2018)

    Article  Google Scholar 

  6. Schleich, B., Anwer, N., Mathieu, L.: Shaping the digital twin for design and production engineering. CIRP Ann. 66(1), 141–144 (2017)

    Article  Google Scholar 

  7. Tao, F., Sui, F., Liu, A.: Digital twin-driven product design framework. Int. J. Prod. Res. 2018, 1–19 (2018)

    Google Scholar 

  8. Grieves, M.: Irtually intelligent product systems: digital and physical twins. Journal 2(5), 99–110 (2016)

    Google Scholar 

  9. Zhang, H., Liu, Q.: A digital twin- based approach for designing and decoupling of hollow glass production line. IEEE Access 5, 26901–26911 (2017)

    Article  Google Scholar 

  10. Singh, S., Raval, S., Banerjee, B.: Roof bolt identification in underground coal mines from 3D point cloud data using local point descriptors and artificial neural network. Int. J. Remote Sens. 42(1), 367–377 (2021)

    Article  Google Scholar 

  11. Gupta, M., Muller, J., Sukhatme, G.: Using manipulation primitives for object sorting in cluttered environments. IEEE Trans. Autom. Sci. Eng. 12(2), 608–614 (2015)

    Article  Google Scholar 

  12. Lenz, I., Lee, H., Saxena, A.: Deep learning for detecting robotic grasps. Int. J. Robot. Res. 34(4/5), 705–724 (2015)

    Article  Google Scholar 

  13. Redmon, J., Angelova, A.: Real-time grasp detection using convolutional neural networks. In: 2015 IEEE International Conference on Robotics and Automation, pp. 1316–1322. IEEE, Piscataway, USA (2015)

    Google Scholar 

  14. Qiu, Z., Zhang, S.: Fuzzy fast terminal sliding mode vibration control of a two-connected flexible plate using laser sensors. J. Sound Vib. 380, 51–77 (2016)

    Article  Google Scholar 

  15. Yang, C., Peng, G., Li, Y.: Neural networks enhanced adaptive admittance control of optimized robot-environment interaction. IEEE Trans. Cybern. 49(7), 2568–2579 (2019)

    Article  Google Scholar 

  16. Andreas, P., Marcus, G., Kate, S.: Grasp pose detection in point clouds. The Int. J. Robot. Res. 36(13–14), 1455–1473 (2017)

    Google Scholar 

  17. Tran, Q., Young, J.: Design of adaptive kinematic controller using radial basis function neural network for trajectory tracking control of differential-drive mobile robot. Int. J. Fuzzy Logic Intell. Syst. 19(4), 349–359 (2019)

    Article  Google Scholar 

  18. He, W., Dong, Y.: Adaptive fuzzy neural network control for a constrained robot using impedance learning. IEEE Trans. Neural Netw. Learn. Syst. 29(4), 1174–1186 (2018)

    Article  Google Scholar 

  19. Dumlu, A.: Design of a fractional-order adaptive integral sliding mode controller for the trajectory tracking control of robot manipulators. J. Syst. Control Eng. 232(9), 1212–1229 (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, China

    Juntong Yun

  2. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China

    Juntong Yun & Ying Liu

  3. Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, 430081, China

    Juntong Yun & Xin Liu

  4. Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, 430081, China

    Ying Liu

  5. Longzhong Laboratory, Xiangyang, 441000, Hubei, China

    Ying Liu & Xin Liu

Authors
  1. Juntong Yun
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  2. Ying Liu
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  3. Xin Liu
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Corresponding author

Correspondence to Ying Liu .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Honghai Liu

  2. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  3. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  4. Harbin Institute of Technology, Harbin, China

    Li Jiang

  5. Shanghai Jiao Tong University, Shanghai, China

    Guoying Gu

  6. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Xinyu Wu

  7. Harbin Institute of Technology, Shenzhen, China

    Weihong Ren

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Yun, J., Liu, Y., Liu, X. (2022). Model Construction of Multi-target Grasping Robot Based on Digital Twin. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13456. Springer, Cham. https://doi.org/10.1007/978-3-031-13822-5_9

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  • DOI: https://doi.org/10.1007/978-3-031-13822-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13821-8

  • Online ISBN: 978-3-031-13822-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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