AgriPath: a robust multi-objective path planning framework for agricultural robots in dynamic field environments
Yang C. Zheng D. Chen S. Mansurova M. Belgibaev B. Zhao B.
2025Frontiers Media SA
Frontiers in Plant Science
2025#16
Robot path planning is a cornerstone of precision agriculture, enabling safe and efficient operations for agricultural robots. However, complex field environments—characterized by static and dynamic obstacles, dense vegetation, and unstructured terrain—pose significant challenges to effective path planning. Conventional methods, such as A*, Dijkstra, and rapidly exploring random tree (RRT), exhibit limitations in efficiency and adaptability to dynamic conditions. To address these challenges, this study introduces AgriPath, a robust multi-objective path planning framework that integrates an improved convolutional neural network (CNN), an improved A* algorithm, and an improved whale optimization algorithm (IWOA) to optimize pathfinding, convergence efficiency, and obstacle avoidance in complex agricultural settings. Key innovations include an improved CNN leveraging causal convolution and multi-head self-attention mechanisms to improve temporal modeling for short-term trajectory prediction, augmented by Gaussian perturbations to enhance initial solution diversity; an improved A* algorithm incorporating dynamic heuristic functions based on Normalized Difference Vegetation Index (NDVI), combined with Kalman filtering, to bolster global path adaptability; IWOA employing non-linear convergence factors and differential evolution mechanisms to dynamically balance path length, smoothness, and planning time; and an improved Douglas–Peucker algorithm paired with cubic B-spline smoothing and navigation command modules to ensure path simplification and real-time execution. Experiments conducted in the Modern Agricultural Demonstration Zone at Chengdu, Sichuan Province, China, across simple, moderate, and complex scenarios, demonstrate that AgriPath outperforms advanced algorithms—SBREA*, Ant Colony A*, Orchard A*, and Greedy A*—in path length, smoothness, planning time, and dynamic obstacle avoidance success rate, indicative of superior multi-objective optimization balance. This study significantly enhances the efficiency and robustness of agricultural robot path planning, offering a more adaptive solution for autonomous navigation in precision agriculture while providing new theoretical and practical directions for the field of path planning. Copyright
multi-objective optimization , path planning , precision agriculture , robotics , whale optimization algorithm
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Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, Kazakhstan
School of Data Science, Fudan University, Shanghai, China
Faculty of Information Technology
School of Data Science
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026