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Safflower Picking Trajectory Planning Strategy Based on an Ant Colony Genetic Fusion Algorithm

Author

Listed:
  • Hui Guo

    (College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
    Xinjiang Key Laboratory of Intelligent Agricultural Equipment, Urumqi 830052, China)

  • Zhaoxin Qiu

    (College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
    Xinjiang Key Laboratory of Intelligent Agricultural Equipment, Urumqi 830052, China)

  • Guomin Gao

    (College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
    Xinjiang Key Laboratory of Intelligent Agricultural Equipment, Urumqi 830052, China)

  • Tianlun Wu

    (College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
    Xinjiang Key Laboratory of Intelligent Agricultural Equipment, Urumqi 830052, China)

  • Haiyang Chen

    (College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
    Xinjiang Key Laboratory of Intelligent Agricultural Equipment, Urumqi 830052, China)

  • Xiang Wang

    (College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
    Xinjiang Key Laboratory of Intelligent Agricultural Equipment, Urumqi 830052, China)

Abstract

In order to solve the problem of the low pickup efficiency of the robotic arm when harvesting safflower filaments, we established a pickup trajectory cycle and an improved velocity profile model for the harvest of safflower filaments according to the growth characteristics of safflower. Bezier curves were utilized to optimize the picking trajectory, mitigating the abrupt changes produced by the delta mechanism during operation. Furthermore, to overcome the slow convergence speed and the tendency of the ant colony algorithm to fall into local optima, a safflower harvesting trajectory planning method based on an ant colony genetic algorithm is proposed. This method includes enhancements through an adaptive adjustment mechanism, pheromone limitation, and the integration of optimized parameters from genetic algorithms. An optimization model with working time as the objective function was established in the MATLAB environment, and simulation experiments were conducted to optimize the trajectory using the designed ant colony genetic algorithm. The simulation results show that, compared to the basic ant colony algorithm, the path length with the ant colony genetic algorithm is reduced by 1.33% to 7.85%, and its convergence stability significantly surpasses that of the basic ant colony algorithm. Field tests demonstrate that, while maintaining an S-curve velocity, the ant colony genetic algorithm reduces the harvesting time by 28.25% to 35.18% compared to random harvesting and by 6.34% to 6.81% compared to the basic ant colony algorithm, significantly enhancing the picking efficiency of the safflower-harvesting robotic arm.

Suggested Citation

  • Hui Guo & Zhaoxin Qiu & Guomin Gao & Tianlun Wu & Haiyang Chen & Xiang Wang, 2024. "Safflower Picking Trajectory Planning Strategy Based on an Ant Colony Genetic Fusion Algorithm," Agriculture, MDPI, vol. 14(4), pages 1-17, April.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:4:p:622-:d:1377092
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    References listed on IDEAS

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    1. Rongshen Lai & Zhiyong Wu & Xiangui Liu & Nianyin Zeng, 2023. "Fusion Algorithm of the Improved A* Algorithm and Segmented Bézier Curves for the Path Planning of Mobile Robots," Sustainability, MDPI, vol. 15(3), pages 1-17, January.
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