Ground-based Robots for Autonomous Soil Collection and Analysis

Abstract:

Vast areas of contaminated soil are ubiquitous globally. These can come from large scale industrial processes such as oil and gas production, mining operations, and other industrial activities, as well as from flooding events from natural disasters, e.g. after hurricane Katrina in Houston, TX. Contamination characterization through manual soil sample collection in extensive areas is difficult, costly, and time consuming, typically resulting in low sampling density and relatively poor delineation of environmental impacts. This can lead to poor risk evaluation, incorrect cost estimates, selection of inappropriate remediation technologies, or unnecessary remediation such as over excavation. These problems are particularly relevant for remote sites with challenging terrain, including sites with hills, dense vegetation, marshes, or areas prone to flooding.

Automating the contaminant characterization process with robotic systems could overcome these problems. However, these robots are currently limited by their soil collection mechanisms and on-board analysis methods. Soil sampling robots require compact soil collection mechanisms that can operate in a variety of terrain and soil conditions and collect soil in a consistent fashion to allow for accurate contaminant measurements. On-board analysis methods must be robust and reliable. We present a new soil collection mechanism that is compact enough to deploy on ground-based robotic systems without affecting mobility. It consists of a rototiller-like device to break through tough surface crusts to access soil beneath for consistent soil samples. It also consists of an auger-like device with a spring-loaded sample storage sheath to reliably collect and store samples from various soil types. We also identify methods for autonomous measurement of chloride (salinity) and petroleum hydrocarbon, two contaminants of interest to the oil and gas industry.

This soil collection and analysis mechanisms could allow robots to make onsite geo-tagged measurements of soil contaminants. Intelligent algorithms could then be used to process prior measurement data and select future sampling locations to maximize expected information gain. This development will allow for high fidelity delineation of environmental impacts, as well as reduced risk exposure to workers.