Skip to:

Amphibious Unmanned Ground Vehicle Sensor System for Rapid Detection of PFAS in Water


The goal of this project is to integrate environmental instrumentation onto an existing robotic system (an amphibious unmanned ground vehicle (AM-UGV)) to collect and analyze samples for the presence of perfluorooctane sulfonate (PFOS) in water sources to support the sensing of hazards and feed into geospatial services, algorithms, or displays. Polyfluoroalkyl substances (PFAS), including PFOS and perfluorooctanoic acid (PFOA), are man-made chemicals commonly used in consumer goods (e.g., apparel, paper, plastic, carpet) and firefighting chemicals used by both the military and first responders. Exposure to PFAS has led to liver disease, thyroid disease, and some cancers. In 2018, the Department of Defense found over 400 military installations with some level of PFAS contamination, 24 of these installations having drinking water contaminations higher than the Environmental Protection Agency’s lifetime health advisory of 70 ng/L. While there are exposure systems that analyze PFAS, such as chromatography and mass spectrometry, the major limitation is the bulky nature of the equipment needed for analyzing. These methods are expensive, time consuming, and non-trivial in context of preparation. This, along with the logistics of transporting samples from the field to a laboratory, results in low turnaround times from sample extraction to dissemination of information.

The objective of this project is to detect the presence of perfluorooctane sulfonate (PFOS) in water rapidly via an autonomous vehicle. To help facilitate shorter processing times and added safety from exposure, sensors will be integrated onto an amphibious unmanned ground vehicle (AM-UGV) to measure and transmit the concentration of PFOS in water. This device will consist of a waterproof platform, a sample extractor, an electrochemical sensor functionalized to determine PFOS concentration, and a communication system to relay data within a quarter of a mile line of sight distance.

Project Personnel

Dr. Ryan Green
Assistant Professor
Electrical & Computer Engineering
Mississippi State University


This work is supported by The U.S. Army Engineer Research and Development Center (ERDC) federal award identification no. W912HZ2020063.

Period of Performance

September 30, 2020 – August 31, 2023