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Research on Agricultural Intelligent Robot Based on Path Planning

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 613))

Abstract

Based on research and application of agricultural robot’s path planning and autonomous navigation, this paper proposed path planning proposal relied on genetic algorithm, which programmed and calculated some elements, such as target identification, image segmentation and two dimensional grid map of rough sets technology. Through the test, it was observed that harvesting robots can efficiently segment and extract ripe fruits. Besides, it can complete multi-goal tasks. It was proved by practice that rough sets genetic algorithm can obviously improve the speed of path planning. Benefit from it, the efficiency of harvesting task can be promoted as well.

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References

  1. Cherubini, U., Mulinacci, S., Gobbi, F.: Dynamic Copula Methods in Finance, vol. 24, pp. 465–673. Wiley, New York (2011)

    Book  Google Scholar 

  2. Fantazzini, D.: Dynamic copula modelling for value at risk. Front. Finance Econ. 5, 148–158 (2008)

    Google Scholar 

  3. Nelsen, R.B.: An Introduction to Copulas, vol. 37, pp. 47–73. Springer, Berlin (2006)

    MATH  Google Scholar 

  4. Patton, A.J.: Modeling asymmetric exchange rate dependence. Int. Econ. Rev. 47, 80–85 (2006)

    Article  Google Scholar 

  5. Qin, Y.Q., Sun, D.B., Li, N., et al.: Path planning for mobile robot based on particle swarm optimization. Robot 26(3), 222–225 (2004)

    Google Scholar 

  6. Dai, B., Xiao, X.M., Cai, Z.X.: Current status and future development of mobile robot path planning technology. Control Eng. China 12(3), 198–202 (2005)

    Google Scholar 

  7. Sun, B., Chen, W., Xi, Y.: Particle swarm optimization based global path planning for mobile robots. Control Decis. 20(9), 1052 (2005)

    Google Scholar 

  8. Zhuang, H., Du, S., Wu, T.: Research on path planning and related algorithms for robots. Bull. Sci. Technol. 20(3), 210–215 (2004)

    Google Scholar 

  9. Bak, T., Jakobsen, H.: Agricultural robotic platform with four wheel steering for weed detection. Biosyst. Eng. 87(2), 125–136 (2004)

    Article  Google Scholar 

  10. Masehian, E., Sedighizadeh, D.: Classic and heuristic approaches in robot motion planning—a chronological review. World Acad. Sci. Eng. Technol. 29(1), 101–106 (2007)

    Google Scholar 

  11. Oksanen, T., Visala, A.: Coverage path planning algorithms for agricultural field machines. J. Field Robot. 26(8), 651–668 (2009)

    Article  MATH  Google Scholar 

  12. Zhu, Q.B., Zhang, Y.L.: An ant colony algorithm based on grid method for mobile robot path planning. Robot 27(2), 132–136 (2005)

    MathSciNet  Google Scholar 

  13. Li, M., Imou, K., Wakabayashi, K., et al.: Review of research on agricultural vehicle autonomous guidance. Int. J. Agric. Biol. Eng. 2(3), 1–16 (2009)

    Google Scholar 

  14. He, G., Chen, J.: Research on algorithm of intelligent path finding in game development. Comput. Eng. Des. 13, 2334–2337 (2006)

    Google Scholar 

  15. Yu, Z., Yan, J., Zhao, J., et al.: Mobile robot path planning based on improved artificial potential field method. Harbin Gongye Daxue Xuebao (J. Harbin Inst. Technol.) 43(1), 50–55 (2011)

    Google Scholar 

  16. Parker, L.E.: Path planning and motion coordination in multiple mobile robot teams. Encycl. Complex. Syst. Sci. 36, 5783–5800 (2009)

    Article  Google Scholar 

  17. Yang, S.X., Luo, C.: A neural network approach to complete coverage path planning. IEEE Trans. Syst. Man Cybern. B (Cybern.) 34(1), 718–724 (2004)

    Article  Google Scholar 

  18. Yu, H.B., Li, X.A.: Fast path planning based on grid model of robot. Microelectron. Comput. 22(6), 37–84 (2005)

    Google Scholar 

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Correspondence to Xiaoqiang Tang .

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Tang, X., Ji, Y. (2018). Research on Agricultural Intelligent Robot Based on Path Planning. In: Mizera-Pietraszko, J., Pichappan, P. (eds) Lecture Notes in Real-Time Intelligent Systems. RTIS 2016. Advances in Intelligent Systems and Computing, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-60744-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-60744-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60743-6

  • Online ISBN: 978-3-319-60744-3

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