MoistureMapper: An Autonomous Mobile Robot for High-Resolution Soil Moisture Mapping at Scale

TL;DR

MoistureMapper is an autonomous robot that uses adaptive sampling and TDR sensors to efficiently create high-resolution soil moisture maps, reducing deployment costs and improving mapping accuracy at scale.

Contribution

This work introduces MoistureMapper, a novel autonomous robot with adaptive sampling strategies for large-scale, high-resolution soil moisture mapping, demonstrating significant efficiency improvements.

Findings

Adaptive sampling reduces travel distance by up to 30%

MoistureMapper achieves 5% lower variance in moisture maps

Field deployment validates simulation results

Abstract

Soil moisture is a quantity of interest in many application areas including agriculture and climate modeling. Existing methods are not suitable for scale applications due to large deployment costs in high-resolution sensing applications such as for variable irrigation. In this work, we design, build and field deploy an autonomous mobile robot, MoistureMapper, for soil moisture sensing. The robot is equipped with Time Domain Reflectometry (TDR) sensors and a direct push drill mechanism for deploying the sensor to measure volumetric water content in the soil. Additionally, we implement and evaluate multiple adaptive sampling strategies based on a Gaussian Process based modeling to build a spatial mapping of moisture distribution in the soil. We present results from large scale computational simulations and proof-of-concept deployment on the field. The adaptive sampling approach outperforms a greedy benchmark approach and results in up to 30\% reduction in travel distance and 5\% reduction in variance in the reconstructed moisture maps. Link to video showing field experiments: https://youtu.be/S4bJ4tRzObg