Biologists and cyberneticians at NTNU have built a smart robot,  quick and inexpensive to operate, that can, among other things, take samples of microplastics and measure the density of salmon lice.

The unmanned surface vessel (USV), named “Pamela”, is the result of an interdisciplinary collaboration between doctoral candidate Andrea Faltynkova from the Department of Biology and Artur Zolich, a postdoctoral fellow at the Department of Technical Cybernetics.

Se Pamela in action.

Studying microplastics becomes easier with “Pamela”

Faltynkova studies microplastics in the ocean. Microplastics are pieces of plastic smaller than 5 millimeters. Microplastics is known to have negative effects on marine and freshwater life, but we know less about how it affects human health.

Studying microplastics is tricky because it comes in so many varieties and is smaller than five millimeters.

– Microplastics are so heterogeneous. It is a large, diverse group of particles. In addition, it is also unevenly distributed in the water bodies, says Faltynkova.

– Microplastics are not like other types of dissolved pollutants that we can detect even if we only look at small amounts of water or soil. If you take a liter of seawater and there is no plastic in it, can you conclude that there is no plastic in the sea? she asks.

A common procedure today is to go out by boat and collect samples a few times, from which the researchers try to draw conclusions based on how much plastic has been collected.

– But we really have no way of knowing how good the estimates are, says Faltynkova.

Uses hyperspectral imaging

Faltynkova’s main research project is to adapt and develop a technique called hyperspectral imaging to identify and count microplastics. This technology was developed in the mid-1980s to study the Earth from aircraft or space. Today, it is used for everything from studying shipwrecks to identifying different types of tissue in humans. The recycling industry uses this technology to separate plastics. This makes it a perfect tool for studying microplastics.

With a hyperspectral camera, Faltynkova takes a picture of the samples. NTNU’s supercomputer Idun then processes large amounts of data to determine what types of plastic are captured in a sample.

Combines fast analysis with a fast collection

Collecting microplastics usually means dragging a net behind a boat at a very low speed. This is both expensive and inefficient. But Faltynkova can study many samples because Pamela collects them so quickly, easily, and efficiently. Pamela doesn’t cost much and can work independently.

– I am trying to connect rapid analysis using hyperspectral imaging with a method that makes it possible to collect samples quickly, says Faltynkova.

 – Together, this will give us a greater opportunity to map and monitor plastic pollution effectively.

Can follow the planned route

Pamela is held up by two orange buoys, just like those from the popular TV series Baywatch.

– Pamela can follow a pre-programmed route without scientists having to accompany or control the vessel while it does its job, says Zolich, who invented the robot.

Pamela is designed to meet the needs of those who will use it, and the robot continues to be modified – based on the response of the first users. The advanced machine uses inexpensive, readily available components where possible, and uses prototype components for parts that must be customized specifically for the robot. This means that it is both easy to use and easy to improve. Among the other advantages of the vessel is that it can be controlled from a distance and operate independently of a boat.

– The vessel is built up of modules, so that it can be adapted and specialized in several ways depending on what the researchers want to collect, Zolich explains.

The collaboration between Faltynkova and Zolich was initiated by NTNU biologist Geir Johnsen, and has been supported by Tor Arne Johansen at the Department of Technical Cybernetics. Johnsen and Johansen are both key researchers at the Center for Autonomous Marine Operations and Systems (AMOS) at NTNU.

This article was first published by Gemini.no