A Hybrid Path Planning Algorithm for Orchard Robots Based on an Improved D* Lite Algorithm

Quanjie Jiang, Yue Shen, Hui Liu (), Zohaib Khan, Hao Sun and Yuxuan Huang
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Quanjie Jiang: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Yue Shen: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Hui Liu: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Zohaib Khan: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Hao Sun: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Yuxuan Huang: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 15, 1-25

Abstract: Due to the complex spatial structure, dense tree distribution, and narrow passages in orchard environments, traditional path planning algorithms often struggle with large path deviations, frequent turning, and reduced navigational safety. In order to overcome these challenges, this paper proposes a hybrid path planning algorithm based on improved D* Lite for narrow forest orchard environments. The proposed approach enhances path feasibility and improves the robustness of the navigation system. The algorithm begins by constructing a 2D grid map reflecting the orchard layout and inflates the tree regions to create safety buffers for reliable path planning. For global path planning, an enhanced D* Lite algorithm is used with a cost function that jointly considers centerline proximity, turning angle smoothness, and directional consistency. This guides the path to remain close to the orchard row centerline, improving structural adaptability and path rationality. Narrow passages along the initial path are detected, and local replanning is performed using a Hybrid A* algorithm that accounts for the kinematic constraints of a differential tracked robot. This generates curvature-continuous and directionally stable segments that replace the original narrow-path portions. Finally, a gradient descent method is applied to smooth the overall path, improving trajectory continuity and execution stability. Field experiments in representative orchard environments demonstrate that the proposed hybrid algorithm significantly outperforms traditional D* Lite and KD* Lite-B methods in terms of path accuracy and navigational safety. The average deviation from the centerline is only 0.06 m, representing reductions of 75.55% and 38.27% compared to traditional D* Lite and KD* Lite-B, respectively, thereby enabling high-precision centerline tracking. Moreover, the number of hazardous nodes, defined as path points near obstacles, was reduced to five, marking decreases of 92.86% and 68.75%, respectively, and substantially enhancing navigation safety. These results confirm the method’s strong applicability in complex, constrained orchard environments and its potential as a foundation for efficient, safe, and fully autonomous agricultural robot operation.

Keywords: orchard robot; path planning; D* lite; Hybrid A*; trajectory optimization (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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