AUV

Scientists are trying to improve their understanding of the marine environment through the use of autonomous underwater vehicles (AUVs), programmable robotic vehicles that can independently study the ocean and its inhabitants.

But data collected by AUVs takes time to analyze and interpret, and scientists often lose the ability to use this critical information in real-time.

Mark Moline, director of the School of Marine Science and Policy in the University of Delaware co-authored a paper in Robotics on the advantage of linking multi-sensor systems aboard an AUV to enable the vehicle to process sound data in real-time so that it can independently make decisions about what action to take next.

The researchers pre-programmed the computers onboard the REMUS AUV to make certain decisions. While surveying the ocean 500 to 1000 m below the surface, the onboard computers were analysing the sonar data of marine organisms in the water based on size and density.

When acoustic sensors aboard the vehicle detected the right size and concentration of squid, it triggered a second mission: to report the robot’s position in the water and then run a preprogrammed grid to map the area in finer detail.

Squid by Leo Clifford
Squid by Leo Clifford

The higher-level scan revealed a very concentrated collection of squid in one area and a second less tightly woven mass of similarly sized squid as the scan moved north to south. According to Moline, these are details that might have been missed if the REMUS was only programmed to keep traveling on a straight line.

“It was a really simple test that demonstrated that it’s possible to use acoustics to find a species, to have an AUV target specific sizes of that species, and to follow the species, all without having to retrieve and reprogram the vehicle to hunt for something that will probably be long gone by the time you are ready,” he said.

The researchers would also like to know how squid and other prey are horizontally distributed in the water column, and how these distributions change based on oceanographic conditions or the presence or absence of predators, such as whales.

Combining available robotics technologies to explore the water in this way can help fill information gaps and may illuminate scales of prey distribution that scientists don’t know exist.

“Imagine what else could we learn if the vehicle was constantly triggering new missions based on real-time information?” Moline said.

With multiple decision loops, he continued, an AUV could follow an entire school of squid or other marine life, see where they went, and create a continuous roadmap of the prey’s travel through the ocean. It’s an exciting idea that has the potential to reveal new details about how prey move and behave — does the group break up into multiple schools, do they scatter or congregate even tighter over time — and if so, what affects these changes?

You can find the paper, which is open access, at
Moline M A and Benoit-Bird K (2016). Sensor Fusion and Autonomy as a Powerful Combination for Biological Assessment in the Marine Environment. Robotics 2016, 5(1), 4.

Photo credit: Leo Clifford

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