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Research on Tractor Optimal Obstacle Avoidance Path Planning for Improving Navigation Accuracy and Avoiding Land Waste

Author

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  • Hongtao Chen

    (School of Mechanical Engineering, Tianjin University, Tianjin 300350, China
    State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471039, China
    Luoyang Tractor Research Institute Co., Ltd., Luoyang 471039, China)

  • Hui Xie

    (School of Mechanical Engineering, Tianjin University, Tianjin 300350, China)

  • Liming Sun

    (State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471039, China
    Luoyang Tractor Research Institute Co., Ltd., Luoyang 471039, China)

  • Tansu Shang

    (State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471039, China
    Luoyang Tractor Research Institute Co., Ltd., Luoyang 471039, China)

Abstract

Obstacle avoidance operations of tractors can cause parts of land to be unavailable for planting crops, which represents a reduction in land utilization. However, land utilization is significant to the increase in agricultural productivity. Traditional obstacle avoidance path planning methods mostly focus on automatic tractor navigation with small errors, ignoring the decrease in land utilization due to obstacle avoidance operations. To address the problem, this paper proposed an obstacle avoidance path planning method based on the Genetic Algorithm (GA) and Bezier curve. In this paper, a third-order Bezier curve was used to plot the obstacle avoidance path, and the range of control points for the third-order Bezier curve was determined according to the global path and the location of the obstacle. To target the navigation error and land utilization problems, GA was used to search for the optimal point from the selection range of the control point under multiple constraints for automatic tractor navigation such as the obstacle collision avoidance, the minimum turning radius, and the maximum turning angle. Finally, the optimal obstacle avoidance path was determined based on the selected control points to minimize the navigation error and maximize land utilization. The algorithm proposed in this paper was compared with existing methods and the results showed that it has generally favorable performance on obstacle avoidance path planning.

Suggested Citation

  • Hongtao Chen & Hui Xie & Liming Sun & Tansu Shang, 2023. "Research on Tractor Optimal Obstacle Avoidance Path Planning for Improving Navigation Accuracy and Avoiding Land Waste," Agriculture, MDPI, vol. 13(5), pages 1-17, April.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:5:p:934-:d:1131402
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    References listed on IDEAS

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    1. Adriano Zanin Zambom & Brian Seguin & Feifei Zhao, 2019. "Robot path planning in a dynamic environment with stochastic measurements," Journal of Global Optimization, Springer, vol. 73(2), pages 389-410, February.
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