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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

Author

Listed:
  • Huanyu Liu

    (Institute of Modern Agricultural Equipment, Xihua University, Chengdu 610039, China)

  • Jiahao Luo

    (Institute of Modern Agricultural Equipment, Xihua University, Chengdu 610039, China)

  • Lihan Zhang

    (Institute of Modern Agricultural Equipment, Xihua University, Chengdu 610039, China)

  • Hao Yu

    (Institute of Modern Agricultural Equipment, Xihua University, Chengdu 610039, China)

  • Xiangnan Liu

    (Institute of Modern Agricultural Equipment, Xihua University, Chengdu 610039, China)

  • Shuang Wang

    (Institute of Modern Agricultural Equipment, Xihua University, Chengdu 610039, China)

Abstract

This study addresses the challenges of long traversal paths, low efficiency, high fuel consumption, and costs in the collaborative harvesting of corn by harvesters and grain transport vehicles in hilly areas. A path-planning and collaborative scheduling method is proposed, combining Dijkstra’s algorithm with the Improved Harris Hawk Optimization (IHHO) algorithm. A field model based on Digital Elevation Model (DEM) data is created for full coverage path planning, reducing traversal path length. A field transfer road network is established, and Dijkstra’s algorithm is used to calculate distances between fields. A multi-objective collaborative scheduling model is then developed to minimize fuel consumption, scheduling costs, and time. The IHHO algorithm enhances search performance by introducing quantum initialization to improve the initial population, integrating the slime mold algorithm for better exploration, and applying an average differential mutation strategy and nonlinear energy factor updates to strengthen both global and local search. Non-dominated sorting and crowding distance techniques are incorporated to enhance solution diversity and quality. The results show that compared to traditional HHO and HHO algorithms, the IHHO algorithm reduces average scheduling costs by 4.2% and 14.5%, scheduling time by 4.5% and 8.1%, and fuel consumption by 3.5% and 3.2%, respectively. This approach effectively reduces transfer path costs, saves energy, and improves operational efficiency, providing valuable insights for path planning and collaborative scheduling in multi-field harvesting and transportation in hilly areas.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:3:p:233-:d:1573352
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    References listed on IDEAS

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    1. Devroye, Luc, 1982. "Bounds for the uniform deviation of empirical measures," Journal of Multivariate Analysis, Elsevier, vol. 12(1), pages 72-79, March.
    2. 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.
    3. Weicheng Pan & Jia Wang & Wenzhong Yang, 2024. "A Cooperative Scheduling Based on Deep Reinforcement Learning for Multi-Agricultural Machines in Emergencies," Agriculture, MDPI, vol. 14(5), pages 1-16, May.
    4. Mengwei Shen & Suzhen Wang & Shuang Wang & Yan Su, 2020. "Simulation Study on Coverage Path Planning of Autonomous Tasks in Hilly Farmland Based on Energy Consumption Model," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-15, August.
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