Monitoring Re-Growth of Invasive Plants Using an Autonomous Surface Vessel

Copyright © 2021 Codd-Downey, Jenkin, Dey, Zacher, Blainey and Andrews..

Invasive aquatic plant species, and in particular Eurasian Water-Milfoil (EWM), pose a major threat to domestic flora and fauna and can in turn negatively impact local economies. Numerous strategies have been developed to harvest and remove these plant species from the environment. However it is still an open question as to which method is best suited to removing a particular invasive species and the impact of different lake conditions on the choice. One problem common to all harvesting methods is the need to assess the location and degree of infestation on an ongoing manner. This is a difficult and error prone problem given that the plants grow underwater and significant infestation at depth may not be visible at the surface. Here we detail efforts to monitor EWM infestation and evaluate harvesting methods using an autonomous surface vessel (ASV). This novel ASV is based around a mono-hull design with two outriggers. Powered by a differential pair of underwater thrusters, the ASV is outfitted with RTK GPS for position estimation and a set of submerged environmental sensors that are used to capture imagery and depth information including the presence of material suspended in the water column. The ASV is capable of both autonomous and tele-operation.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:7

Enthalten in:

Frontiers in robotics and AI - 7(2020) vom: 14., Seite 583416

Sprache:

Englisch

Beteiligte Personen:

Codd-Downey, Robert [VerfasserIn]
Jenkin, Michael [VerfasserIn]
Dey, Bir Bikram [VerfasserIn]
Zacher, James [VerfasserIn]
Blainey, Eva [VerfasserIn]
Andrews, Peter [VerfasserIn]

Links:

Volltext

Themen:

Autonomous surface vessel
Computer vision
Dataset
Journal Article
Object detection
Plant monitoring
Robotics
Underwater

Anmerkungen:

Date Revised 10.02.2021

published: Electronic-eCollection

Citation Status PubMed-not-MEDLINE

doi:

10.3389/frobt.2020.583416

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM321137663