Coverage Path Planning Based on Region Segmentation and Path Orientation Optimization

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 reconstru...

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Main Authors: Tao Yang, Xintong Du, Bo Zhang, Xu Wang, Zhenpeng Zhang, Chundu Wu
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Agriculture
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Online Access:https://www.mdpi.com/2077-0472/15/14/1479
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author Tao Yang
Xintong Du
Bo Zhang
Xu Wang
Zhenpeng Zhang
Chundu Wu
author_facet Tao Yang
Xintong Du
Bo Zhang
Xu Wang
Zhenpeng Zhang
Chundu Wu
author_sort Tao Yang
collection DOAJ
description 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.
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spelling doaj-art-ec021f09a02b4500bd7e9265249e53912025-08-20T02:45:52ZengMDPI AGAgriculture2077-04722025-07-011514147910.3390/agriculture15141479Coverage Path Planning Based on Region Segmentation and Path Orientation OptimizationTao Yang0Xintong Du1Bo Zhang2Xu Wang3Zhenpeng Zhang4Chundu Wu5School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaTo 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.https://www.mdpi.com/2077-0472/15/14/1479UAV-based 3D reconstructionfield segmentationsegmentation angle optimizationcomplete coverage path planningoperation direction optimization
spellingShingle Tao Yang
Xintong Du
Bo Zhang
Xu Wang
Zhenpeng Zhang
Chundu Wu
Coverage Path Planning Based on Region Segmentation and Path Orientation Optimization
Agriculture
UAV-based 3D reconstruction
field segmentation
segmentation angle optimization
complete coverage path planning
operation direction optimization
title Coverage Path Planning Based on Region Segmentation and Path Orientation Optimization
title_full Coverage Path Planning Based on Region Segmentation and Path Orientation Optimization
title_fullStr Coverage Path Planning Based on Region Segmentation and Path Orientation Optimization
title_full_unstemmed Coverage Path Planning Based on Region Segmentation and Path Orientation Optimization
title_short Coverage Path Planning Based on Region Segmentation and Path Orientation Optimization
title_sort coverage path planning based on region segmentation and path orientation optimization
topic UAV-based 3D reconstruction
field segmentation
segmentation angle optimization
complete coverage path planning
operation direction optimization
url https://www.mdpi.com/2077-0472/15/14/1479
work_keys_str_mv AT taoyang coveragepathplanningbasedonregionsegmentationandpathorientationoptimization
AT xintongdu coveragepathplanningbasedonregionsegmentationandpathorientationoptimization
AT bozhang coveragepathplanningbasedonregionsegmentationandpathorientationoptimization
AT xuwang coveragepathplanningbasedonregionsegmentationandpathorientationoptimization
AT zhenpengzhang coveragepathplanningbasedonregionsegmentationandpathorientationoptimization
AT chunduwu coveragepathplanningbasedonregionsegmentationandpathorientationoptimization