field-coverage 0.1.1

Pathfinding for autonomous robots

Damage-Aware Coverage Path Planning

A Python library for damage-aware coverage path planning (DACPP) in rectilinear environments, with a focus on agricultural fields. Produces coverage paths that jointly minimise tile revisits and turns — both of which contribute to soil compaction in agricultural applications.

This library is the reference implementation for the paper "Damage-Aware Coverage Path Planning as a Generalised Travelling Salesperson" (Cutteridge, Frenkel, and Mansouri).

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Overview

Classical coverage path planning minimises path length or completion time, treating revisits as an incidental side effect. In applications such as agricultural seeding, however, revisits are actively harmful: each additional pass crushes seedlings and compacts the soil. This library formalises coverage as an optimisation problem that explicitly penalises revisits and turns, and solves it as a Generalised Travelling Salesperson Problem (GTSP).

The solver has three stages:

1. Setup — the field is decomposed into rectilinear regions (via Fields2Cover), and up to eight boustrophedon sweep patterns are generated for each region.
2. Ant Colony Optimisation (ACO) — a parallel ACO solver finds a high-quality visiting order over the sweep patterns, formulated as a GTSP.
3. Simulated Annealing (SA) — a refinement stage with a novel absorption operator, which removes revisits by reassigning shared tiles from connector paths into adjacent regions.

Evaluated on 200 real UK field geometries from the Sentinel-2 dataset, the algorithm achieves an average revisit rate of 4.2% and 33 turns per 100 tiles.

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Repository Structure

Directory	Contents
field_coverage/	Core library (GTSP construction, ACO, SA, absorption operator)
docs/	Architecture diagrams and flowcharts
results/	Experimental data, CSV outputs, and figures from the paper
data/	GeoJSON field geometries

Note: This repository contains the backend library only. The AuTo-mato web interface used to produce Fig. 7 of the paper is not yet public.

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Setup

This Docker image has been tested on ARM CPUs only; results on other architectures may differ.

1. Build the Fields2Cover container

From the root of field_coverage/:

docker build -f decomp/dockerfile -t fields2cover-api:v3 --platform linux/arm64 .
docker run -d -p 8000:8000 --platform linux/arm64 fields2cover-api:v3

2. Install the Python package

Install uv, then:

uv pip install -e .

If field_coverage is not installed automatically:

uv pip install field_coverage

3. Start the backend API

uvicorn coverage_api:app --reload --port 8000

Tests

pytest