Optimal Coverage Path Planning for Agricultural Ground Vehicles
Veröffentlichungsdatum
2026-08-31
Betreuer
Gutachter
Korte, Hubert
Zusammenfassung
Complete coverage path planning (CCPP) is fundamental in the field of precision farming and especially controlled traffic farming, where planned field paths are reused across seasons and by multiple machines. Consequently, even small quality improvements can accumulate into substantial long-term benefits in fuel consumption, processing time and crop yield. This motivates a systematic application of mathematical optimization to refine these paths beyond the capabilities of traditional or heuristic approaches. In this thesis, we present a modular, optimization-based framework for CCPP tailored to agricultural ground vehicles operating within open-field polygonal regions.
The approach is machine-agnostic and relies solely on geometric and kinematic constraints, ensuring that all paths remain within the field boundaries while satisfying prescribed limits on curvature and sharpness. We follow a stage-based methodology that begins by decomposing the field into a headland area along the field boundaries and an interior region, the latter further partitioned into simple cells. This decomposition provides the foundation for a structured guidance track system that covers the entire field. A subsequent route planning stage determines the traversal order of the cells and the sequence of tracks within each one. Finally, smooth path planning techniques generate curvature and sharpness constrained headland paths and interior coverage paths that follow the guidance tracks in accordance with the planned route.
This work bridges agricultural engineering and mathematical optimization, demonstrating how targeted optimization techniques can systematically improve coverage path quality. Each stage of the framework is formulated rigorously and addressed with dedicated methods: the decomposition and global interior track angle are optimized to minimize the number of tracks, binary linear programs are used for combinatorial route planning to obtain a short, full-coverage route, and solving dedicated optimal control problems yields shortest feasible turning maneuvers and provides path smoothing.
A benchmark dataset of 193 real-world agricultural fields is published with this work and used to evaluate both, the individual stages and the full planning pipeline. The results show consistently high success rates across multiple machine configurations and demonstrate reduced region boundary crossing, improved coverage quality, and greater robustness compared to an open-source CCPP framework.
Overall, this work presents a practical, optimization-based CCPP framework for agricultural machines, offering reliable offline path planning, reproducible evaluation, and a modular structure that can be further extended to real-world constraints and agronomic priorities.
The approach is machine-agnostic and relies solely on geometric and kinematic constraints, ensuring that all paths remain within the field boundaries while satisfying prescribed limits on curvature and sharpness. We follow a stage-based methodology that begins by decomposing the field into a headland area along the field boundaries and an interior region, the latter further partitioned into simple cells. This decomposition provides the foundation for a structured guidance track system that covers the entire field. A subsequent route planning stage determines the traversal order of the cells and the sequence of tracks within each one. Finally, smooth path planning techniques generate curvature and sharpness constrained headland paths and interior coverage paths that follow the guidance tracks in accordance with the planned route.
This work bridges agricultural engineering and mathematical optimization, demonstrating how targeted optimization techniques can systematically improve coverage path quality. Each stage of the framework is formulated rigorously and addressed with dedicated methods: the decomposition and global interior track angle are optimized to minimize the number of tracks, binary linear programs are used for combinatorial route planning to obtain a short, full-coverage route, and solving dedicated optimal control problems yields shortest feasible turning maneuvers and provides path smoothing.
A benchmark dataset of 193 real-world agricultural fields is published with this work and used to evaluate both, the individual stages and the full planning pipeline. The results show consistently high success rates across multiple machine configurations and demonstrate reduced region boundary crossing, improved coverage quality, and greater robustness compared to an open-source CCPP framework.
Overall, this work presents a practical, optimization-based CCPP framework for agricultural machines, offering reliable offline path planning, reproducible evaluation, and a modular structure that can be further extended to real-world constraints and agronomic priorities.
Schlagwörter
Coverage Path Planning
;
Agricultural Robotics
;
Precision Farming
;
Controlled Traffic Farming
;
Region Feasibility
;
Curvature and Sharpness Constraints
;
Headland Planning
;
Cellular Decomposition
;
Guidance Track System
;
Route Planning
;
Path Smoothing
;
Nonlinear Optimization
;
Optimal Control
;
Binary Linear Optimization
Institution
Dokumenttyp
Dissertation
Sprache
Englisch
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thesis_hoeffmann.pdf
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