Waterberry Farms: A Novel Benchmark For Informative Path Planning

TL;DR

Waterberry Farms introduces a new benchmark for evaluating informative path planning algorithms in complex, dynamic agricultural environments, aiding the development of more effective robotic sensing strategies.

Contribution

The paper presents Waterberry Farms, a novel benchmark that models complex, dynamic agricultural scenarios for testing path planning and estimation algorithms.

Findings

Benchmark captures dynamic disease spread and soil humidity variations.

Scoring system evaluates combined movement policy and estimator performance.

Benchmark demonstrates ability to compare and analyze different algorithms.

Abstract

Recent developments in robotic and sensor hardware make data collection with mobile robots (ground or aerial) feasible and affordable to a wide population of users. The newly emergent applications, such as precision agriculture, weather damage assessment, or personal home security often do not satisfy the simplifying assumptions made by previous research: the explored areas have complex shapes and obstacles, multiple phenomena need to be sensed and estimated simultaneously and the measured quantities might change during observations. The future progress of path planning and estimation algorithms requires a new generation of benchmarks that provide representative environments and scoring methods that capture the demands of these applications. This paper describes the Waterberry Farms benchmark (WBF) that models a precision agriculture application at a Florida farm growing multiple crop types. The benchmark captures the dynamic nature of the spread of plant diseases and variations of soil humidity while the scoring system measures the performance of a given combination of a movement policy and an information model estimator. By benchmarking several examples of representative path planning and estimator algorithms, we demonstrate WBF's ability to provide insight into their properties and quantify future progress.