# Fields2Cover: An open-source coverage path planning library for unmanned agricultural vehicles

arXiv: https://arxiv.org/abs/2210.07838 · HTML: https://mlsanity.com/paper/2210.07838/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2022.

## Built on by (most cited first, 5 of 5)

- [NEUSIS: A Compositional Neuro-Symbolic Framework for Autonomous Perception, Reasoning, and Planning in Complex UAV Search Missions](https://mlsanity.com/paper/2409.10196/) (2024)
- [Reinforcement Learning-Based Energy-Aware Coverage Path Planning for Precision Agriculture](https://mlsanity.com/paper/2601.16405/) (2026)
- [Adaptive path planning for efficient object search by UAVs in agricultural fields](https://mlsanity.com/paper/2504.02473/) (2025)
- [Baseline-Free Policy Optimization for Neural Combinatorial Optimization](https://mlsanity.com/paper/2606.10321/) (2026)
- [Critic-Free Deep Reinforcement Learning for Maritime Coverage Path Planning on Irregular Hexagonal Grids](https://mlsanity.com/paper/2603.28385/) (2026)

## Abstract (arXiv)

This paper describes Fields2Cover, a novel open source library for coverage path planning (CPP) for agricultural vehicles. While there are several CPP solutions nowadays, there have been limited efforts to unify them into an open source library and provide benchmarking tools to compare their performance. Fields2Cover provides a framework for planning coverage paths, developing novel techniques, and benchmarking state-of-the-art algorithms. The library features a modular and extensible architecture that supports various vehicles and can be used for a variety of applications, including farms. Its core modules are: a headland generator, a swath generator, a route planner and a path planner. An interface to the Robot Operating System (ROS) is also supplied as an add-on. In this paper, the functionalities of the library for planning a coverage path in agriculture are demonstrated using 8 state-of-the-art methods and 7 objective functions in simulation and field experiments.