Build it yourself: a robust autonomous water monitoring system for non-roboticists
Monitoring water quality in seas, coastal, and inland waters is crucial for identifying pollutant sources, tracking trends, and ensuring compliance with water use policies. Traditional methods, such as manual sampling, high-frequency buoys, and remote sensing, face challenges in providing real-time data and capturing spatial variations. While Autonomous Surface Vehicles (ASVs) offer advanced solutions, their high cost (often exceeding 10k USD), primarily due to specific custom designs, limits accessibility for community-driven science. To address these challenges, we designed Catabot, a low-cost, open-source ASV platform designed for accessibility to (and use by) aquatic scientists. We also worked on its autonomy for obstacle avoidance to reduce the level of human supervision. Catabot provides a reliable, real-time water monitoring solution with key features: (1) stable and reliable vehicle motion and accurate sensor measurements; (2) modular design for easy transportation, assembly, and operation; (3) robust capabilities, including long-range communication, automated path following, integration of multiple sensors, and motion planning; and (4) user-friendly operation, including automated data processing for non-roboticists.Catabot ASV offers significant academic support and fosters a more inclusive and interdisciplinary community – e.g., monitoring water quality and long-term environmental monitoring; assisting as a hands-on tool for limnology-based classes; bathymetric mapping of lake and coastal waters; and supporting the work of Lake Protection Associations.
Presentation Preference: Oral
Primary Presenter: Mingi Jeong, Dartmouth College (mingi.jeong.gr@dartmouth.edu)
Authors:
Quin Shingai, Dartmouth College (quin.k.shingai.gr@dartmouth.edu)
Kizito Masaba, Dartmouth College (Kizito.Masaba.GR@dartmouth.edu)
Monika Roznere, Binghamton University (mroznere@binghamton.edu)
Emily Arsenault, State University of New York College of Environmental Science and Forestry (emarsena@esf.edu)
Denise Bruesewitz, Colby College (dabruese@colby.edu)
Holly Ewing, Bates College (hewing@bates.edu)
Kathryn Cottingham, Dartmouth College (kathryn.l.cottingham@dartmouth.edu)
Alberto Quattrini Li, Dartmouth College (alberto.quattrini.li@dartmouth.edu)
Build it yourself: a robust autonomous water monitoring system for non-roboticists
Category
Scientific Sessions > CS12 - Novel Methods
Description
Time: 10:00 AM
Date: 27/3/2025
Room: W206B