Advertisement

3D Research

, 10:15 | Cite as

Simulation and Analysis of Three-Dimensional Space Path Prediction for Six-Degree-of-Freedom (SDOF) Manipulator

  • Yu XiangEmail author
3DR Express
  • 21 Downloads

Abstract

Traditional methods are ineffective in predicting the three-dimensional path of a six-degree-of-freedom (SDOF) manipulator. In view of the above situation, this paper proposes a three-dimensional space path prediction simulation method for a SDOF manipulator. The structure of the SDOF manipulator is analyzed, and the kinematics model of the manipulator is constructed. The kinematics model of the manipulator is solved by the forward and reverse kinematics solutions. According to the inverse kinematics solutions, the method of automatic optimization of multiple solutions is obtained, which can effectively improve the performance of the manipulator. The collision is avoided in the three-dimensional motion of a SDOF manipulator. On the basis of the kinematics of the manipulator, the Cartesian space is used to predict the path trajectory. The average operation time of the path planning cycle and the deviation of the relative smooth trajectory are compared through the three-dimensional path prediction distance and the straight line distance of the SDOF manipulator. The experimental results show that the average operation time of the period of the path prediction between the two is close, and the deviation of the three-dimensional space path prediction distance of the SDOF manipulator is better than that of the straight line distance. It has certain application performance.

Keywords

Six-degree-of-freedom (SDOF) Manipulator Three-dimensional space Path prediction 

Notes

References

  1. 1.
    Wang, H., Wang, T. F., & Liu, S. F. (2016). A ati-based network attack path prediction method. Computer Engineering, 42(9), 132–137.Google Scholar
  2. 2.
    Hu, H., Liu, Y. L., Zhang, H. Q., Yang, Y. J., & Ye, R. G. (2018). Network intrusion path prediction method based on absorbed Markov chain. Computer Research and Development, 55(4), 831–845.Google Scholar
  3. 3.
    Yang, D. P., Pan, H. Y., Zhao, Y., & Li, T. Y. (2017). Three-dimensional linear elastic bending crack path prediction of welded joints under fatigue loading. Ship Mechanics, 21(3), 318–328.Google Scholar
  4. 4.
    Chen, W., & Jiang, X. Y. (2016). About wheeled robot path planning control simulation. Computer Simulation, 33(5), 367–371.Google Scholar
  5. 5.
    Zhou, Y. J., Liu, S. J., Yu, J. C., & Wang, X. H. (2018). Water glider path planning based on local flow field construction. Robot, 40(1), 1–7.Google Scholar
  6. 6.
    Xu, X. S., Yang, S. J., & Chen, R. Y. (2016). Optimal path planning method for mobile robots in complex environments. Journal of Electronic Measurement and Instrument, 30(2), 274–282.Google Scholar
  7. 7.
    Chen, Y. J., Wang, Y. N., Tan, J. H., & Mao, J. X. (2017). Service robot path planning for incremental sampling of local environment. Chinese Journal of Scientific Instrument, 38(5), 1093–1100.Google Scholar
  8. 8.
    Xu, C., & Zheng, Y. L. (2016). Structural and kinematics simulation research on manipulator with six degrees of freedom. Journal of Liaoning University (Natural Science Edition), 43(4), 331–334.MathSciNetGoogle Scholar
  9. 9.
    Li, B. J., & Shi, G. L. (2016). Simulation research on the position control strategy for pneumatic robot above rough ground. Mechatronics, 22(4), 3–7.Google Scholar
  10. 10.
    Liu, X. Y., Xu, C., Han, W. J., Wang, Z. C., Li, X. F., Fang, L. Y., et al. (2017). Control system design of 6 degree freedom teaching robotic arm based on AVR MCU. Development and Innovation of Machinery and Electrical Products, 30(1), 90–91.Google Scholar
  11. 11.
    You, X. M., Liu, S., & Lu, J. Q. (2017). An ant colony algorithm for dynamic search strategy and its application in robot path planning. Control and Decision, 32(3), 552–556.Google Scholar
  12. 12.
    Hu, D. D., & Yin, H. (2017). Path recognition of corn harvesting robot based on machine vision. Agricultural Mechanization Research, 39(12), 190–194.Google Scholar
  13. 13.
    Yu, N. G., & Mo, F. F. (2016). Mobile robot path planning method based on deep autoencoder and q learning. Journal of Beijing University of Technology, 42(5), 668–673.MathSciNetzbMATHGoogle Scholar
  14. 14.
    Liu, W., You, X. M., & Liu, S. (2016). Improved ant colony algorithm for path planning of mobile robots in complex environments. Computer Engineering and Applications, 52(13), 60–63.Google Scholar
  15. 15.
    Yang, Y., Tong, D. B., & Chen, Q. Y. (2018). Hexapod robot path planning algorithm for unknown maps. Computer Application, 38(6), 1809–1813.Google Scholar
  16. 16.
    Wang, H. J., Xu, J., Zhao, H., & Yue, Y. J. (2017). Robot path planning based on smooth ant colony algorithm. Journal of Yanshan University, 41(3), 278–282.Google Scholar
  17. 17.
    Wang, H. Q., Hu, Y. Y., Liao, W. D., Yan, T. B., & Wang, D. Y. (2016). Robot path planning based on improved artificial bee colony algorithm. Control Engineering, 23(9), 1407–1411.Google Scholar
  18. 18.
    Tian, H. T., Li, T., & Qin, Y. (2017). Path search for four-way mobile robot based on a~* improved algorithm. Control and Decision, 32(6), 1007–1012.Google Scholar
  19. 19.
    Gu, W. L., Hu, Y. F., Gong, X., Cai, S., & Chen, H. (2017). Path tracking control and experiment of mobile robots with given speed requirements. Journal of Agricultural Machinery, 48(10), 25–31.Google Scholar
  20. 20.
    Chen, H., Guo, Y. K., Cheng, C., Ou, L. L., & Yu, L. (2016). Warehousing robot path planning based on linear sequential logic theory. High-Tech Communication, 26(1), 16–23.Google Scholar

Copyright information

© 3D Display Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Equipment ManufacturingChengdu Vocational and Technical College of IndustryChengduChina

Personalised recommendations