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Published on: December 9, 2012
Variable scale operational path planning for land levelling based on the improved ant colony optimization algorithm
Wenming Chen1, Jiaxin Yang1, Shaocen Zhang1
1School of Agricultural Engineering, Jiangsu University, Zhenjiang, China.
Abstract:
To address the challenges associated with traditional traversal-based land leveling, which often suffered from a lack of coordination and planning, resulting in low operational efficiency and the management of large data volumes in contemporary path planning, this study proposed a variable-scale improved ant colony optimization algorithm specifically designed for land leveling path planning. The research introduced the IACO (Improved Ant Colony Optimization) algorithm for large-scale inter-region soil balance path planning, alongside the FIA*ACO (Fusion of the Improved A* Ant Colony Optimization) algorithm for small-scale, fine-grained full-field leveling path planning. Based on the analysis of actual field experiments, the inter-regional levelling operation resulted in a 47.5% reduction in the maximum elevation difference of the path, a 26.3% increase in the distribution of the 5 cm elevation difference, and a 53.7% improvement in flatness. In contrast, the refined levelling operation achieved a 62.9% reduction in the maximum elevation difference, a 52.0% increase in the distribution of the 5 cm elevation difference, and a 78.4% enhancement in flatness. These findings suggested that regional path planning could effectively enhance the area of fields meeting leveling standards, whereas fine-gridded path planning further optimized the leveling effect. This confirmed the efficacy of the scale-variable improved ant colony optimization algorithm in land leveling path planning.
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