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A Full-Coverage Path Planning Method for an Orchard Mower Based on the Dung Beetle Optimization Algorithm

Author

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  • Lixing Liu

    (College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China)

  • Xu Wang

    (College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China)

  • Hongjie Liu

    (College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
    Hebei Province Smart Agriculture Equipment Technology Innovation Center, Baoding 071001, China)

  • Jianping Li

    (College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
    Hebei Province Smart Agriculture Equipment Technology Innovation Center, Baoding 071001, China)

  • Pengfei Wang

    (College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
    Hebei Province Smart Agriculture Equipment Technology Innovation Center, Baoding 071001, China)

  • Xin Yang

    (College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
    Hebei Province Smart Agriculture Equipment Technology Innovation Center, Baoding 071001, China)

Abstract

In order to optimize the operating path of orchard mowers and improve their efficiency, we propose an MI-DBO (multi-strategy improved dung beetle optimization algorithm) to solve the problem of full-coverage path planning for mowers in standardized quadrilateral orchard environments. First, we analyzed the operation scenario of lawn mowers in standardized orchards, transformed the full-coverage path planning problem into a TSP (traveling salesman problem), and mathematically modeled the U-turn and T-turn strategies based on the characteristics of lawn mowers in orchards. Furthermore, in order to overcome the issue of uneven distribution of individual positions in the DBO (dung beetle optimization) algorithm and the tendency to fall into local optimal solutions, we incorporated Bernoulli mapping and the convex lens reverse-learning strategy in the initialization stage of DBO to ensure a uniform distribution of the initial population. During the algorithm iteration stage, we incorporated the Levy flight strategy into the position update formulas of breeding beetles, foraging beetles, and stealing beetles in the DBO algorithm, allowing them to escape from local optimal solutions. Simulation experiments show that for 18 types of orchards with different parameters, MI-DBO can find the mowing machine’s operation paths. Compared with other common swarm intelligence algorithms, MI-DBO has the shortest average path length of 456.36 m and can ensure faster optimization efficiency. Field experiments indicate that the algorithm-optimized paths do not effectively reduce the mowing machine’s missed mowing rate, but the overall missed mowing rate is controlled below 0.8%, allowing for the completion of mowing operations effectively. Compared with other algorithms, MI-DBO has the least time and fuel consumption for operations. Compared to the row-by-row operation method, using paths generated by MI-DBO reduces the operation time by an average of 1193.67 s and the fuel consumption rate by an average of 9.99%. Compared to paths generated by DBO, the operation time is reduced by an average of 314.33 s and the fuel consumption rate by an average of 2.79%.

Suggested Citation

  • Lixing Liu & Xu Wang & Hongjie Liu & Jianping Li & Pengfei Wang & Xin Yang, 2024. "A Full-Coverage Path Planning Method for an Orchard Mower Based on the Dung Beetle Optimization Algorithm," Agriculture, MDPI, vol. 14(6), pages 1-17, May.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:6:p:865-:d:1405574
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    References listed on IDEAS

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    1. Neungmatcha, Woraya, 2016. "Multi-objective particle swarm optimization for mechanical harvester route planning of sugarcane field operationsAuthor-Name: Sethanan, Kanchana," European Journal of Operational Research, Elsevier, vol. 252(3), pages 969-984.
    2. Qian, Long & Lu, Hua & Gao, Qiang & Lu, Hualiang, 2022. "Household-owned farm machinery vs. outsourced machinery services: The impact of agricultural mechanization on the land leasing behavior of relatively large-scale farmers in China," Land Use Policy, Elsevier, vol. 115(C).
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    Cited by:

    1. Huanyu Liu & Jiahao Luo & Lihan Zhang & Hao Yu & Xiangnan Liu & Shuang Wang, 2025. "Research on Traversal Path Planning and Collaborative Scheduling for Corn Harvesting and Transportation in Hilly Areas Based on Dijkstra’s Algorithm and Improved Harris Hawk Optimization," Agriculture, MDPI, vol. 15(3), pages 1-33, January.

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