Coverage Path Planning Based on Region Segmentation and Path Orientation Optimization

Tao Yang, Xintong Du, Bo Zhang (), Xu Wang, Zhenpeng Zhang and Chundu Wu ()
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Tao Yang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Xintong Du: School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
Bo Zhang: School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
Xu Wang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Zhenpeng Zhang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Chundu Wu: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 14, 1-21

Abstract: To address the operational demands of irregular farmland with fixed obstacles, this study proposes a full-coverage path planning framework that integrates UAV-based 3D perception and angle-adaptive optimization. First, digital orthophoto maps (DOMs) and digital elevation models (DEMs) were reconstructed from low-altitude aerial imagery. The feasible working region was constructed by shrinking field boundaries inward and dilating obstacle boundaries outward. This ensured sufficient safety margins for machinery operation. Next, segmentation angles were scanned from 0° to 180° to minimize the number and irregularity of sub-regions; then a two-level simulation search was performed over 0° to 360° to optimize the working direction for each sub-region. For each sub-region, the optimal working direction was selected based on four criteria: the number of turns, travel distance, coverage redundancy, and planning time. Between sub-regions, a closed-loop interconnection path was generated using eight-directional A* search combined with polyline simplification, arc fitting, Chaikin subdivision, and B-spline smoothing. Simulation results showed that a 78° segmentation yielded four regular sub-regions, achieving 99.97% coverage while reducing the number of turns, travel distance, and planning time by up to 70.42%, 23.17%, and 85.6%. This framework accounts for field heterogeneity and turning radius constraints, effectively mitigating path redundancy in conventional fixed-angle methods. This framework enables general deployment in agricultural field operations and facilitates extensions toward collaborative and energy-optimized task planning.

Keywords: UAV-based 3D reconstruction; field segmentation; segmentation angle optimization; complete coverage path planning; operation direction 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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