3D Soil Compaction Mapping through Kriging-based Exploration with a Mobile Robot

TL;DR

This paper introduces an automated, robot-driven approach for real-time soil mapping using Kriging-based exploration, improving efficiency and map quality over traditional manual methods.

Contribution

It develops a novel method for adaptive soil data collection with a mobile robot, leveraging Kriging variance to optimize exploration strategies in real-time.

Findings

Robot-based soil mapping is more efficient than manual data collection.

Adaptive exploration improves soil map accuracy.

Kriging variance effectively guides robotic exploration.

Abstract

This paper presents an automated method for creating spatial maps of soil condition with an outdoor mobile robot. Effective soil mapping on farms can enhance yields, reduce inputs and help protect the environment. Traditionally, data are collected manually at an arbitrary set of locations, then soil maps are constructed offline using Kriging, a form of Gaussian process regression. This process is laborious and costly, limiting the quality and resolution of the resulting information. Instead, we propose to use an outdoor mobile robot for automatic collection of soil condition data, building soil maps online and also adapting the robot's exploration strategy on-the-fly based on the current quality of the map. We show how using Kriging variance as a reward function for robotic exploration allows for both more efficient data collection and better soil models. This work presents the theoretical foundations for our proposal and an experimental comparison of exploration strategies using soil compaction data from a field generated with a mobile robot.