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AgriPath: a robust multi-objective path planning framework for agricultural robots in dynamic field environments.

Chenghan Yang1, Dingkun Zheng1, Siming Chen2

  • 1Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, Kazakhstan.

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This study presents AgriPath, a novel framework for agricultural robot path planning. AgriPath enhances navigation efficiency and obstacle avoidance in complex fields using advanced AI algorithms.

Keywords:
multi-objective optimizationpath planningprecision agricultureroboticswhale optimization algorithm

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Precision Agriculture

Background:

  • Agricultural robots require effective path planning for complex field navigation.
  • Conventional algorithms struggle with dynamic obstacles, dense vegetation, and unstructured terrain.
  • Existing methods lack efficiency and adaptability in precision agriculture settings.

Purpose of the Study:

  • To introduce AgriPath, a robust multi-objective path planning framework for agricultural robots.
  • To enhance pathfinding, convergence efficiency, and obstacle avoidance in challenging agricultural environments.
  • To provide a more adaptive solution for autonomous navigation in precision agriculture.

Main Methods:

  • AgriPath integrates an improved convolutional neural network (CNN) with causal convolution and self-attention for trajectory prediction.
  • An improved A* algorithm utilizes dynamic heuristic functions (NDVI) and Kalman filtering for global path adaptability.
  • An improved whale optimization algorithm (IWOA) balances path length, smoothness, and planning time, complemented by Douglas-Peucker and B-spline smoothing.

Main Results:

  • AgriPath demonstrated superior performance over SBREA*, Ant Colony A*, Orchard A*, and Greedy A* in experiments.
  • The framework achieved better path length, smoothness, planning time, and dynamic obstacle avoidance success rates.
  • Results indicate a superior multi-objective optimization balance for agricultural robot navigation.

Conclusions:

  • AgriPath significantly enhances the efficiency and robustness of agricultural robot path planning.
  • The framework offers a more adaptive solution for autonomous navigation in precision agriculture.
  • This study provides new theoretical and practical directions for path planning in agricultural robotics.