Adaptive Robotics at Barkley Canyon and Hydrate Ridge

Not just intelligent but adaptive robots

The data collected will be used to update plans for underwater robot deployments on the fly, taking strategic approaches to gathering data in complex, dynamically changing environments like cold seeps. The information will be compiled onto a single geo referenced frame so humans, as well as machines, can recognize patterns that might be of interest and biological hot spots that we would want to know more about.

Hot spots in the deep

By developing tools for rapid visualization and machine-assisted interpretation, Dr. Thornton and his team will be able to focus observational efforts and generate a new framework that can cover larger ground with better resolution where it is most needed. This means biological hot spots will be easily identified providing a representative picture of the benthic ecosystem. The team will apply this concept by studying environmental aspects that shape the distribution of animal life in gas hydrate fields or “cold seeps.”

Gas hydrate fields and cold seeps sustain some of the richest known ecosystems on the seafloor. They only exist in cold, deep waters where the pressure is high. At the same time, hydrate fields cover vast regions, so the only way to study these systems and grasp an understanding of their influence on deep-sea ecology, is to make high-resolution observations over large areas. Understanding these systems is important for both benthic ecology, climate, and geo hazards research. Unfortunately, in the past we have not had the ability to intelligently investigate these systems in the time frames relevant to their dynamic changing environment.

Innovative Instrumentation

Barkley Canyon and Hydrate Ridge are the two sites that will be explored during this expedition. Since these are continuously changing environments, the AUV will be sent out first to complete a screening survey of the seafloor. Using three AUVs and ROV Subastian, the vehicles will undertake data-gathering deployments in real time looking at how the chemical, physical and biological properties in the water column influence (or is influenced by) the distribution of animals on the seafloor. An AUV will first produce 1 cm resolution 3D visual reconstructions of vast regions flying at a higher distance in the water column looking for patterns of interest.

Once biological and chemical hot spots are found, artificial intelligence (AI) will be used to tell another vehicle where to do higher resolution photo mosaicking, generating submillimeter resolution 3D visual reconstructions, allowing scientists to gather data in the most interesting regions and interpret them in a way that would not be possible otherwise. Hovering closer to the seafloor, the vehicles will cover a smaller, targeted area, making in-situ chemical and biological measurements using instruments such as laser spectrometers.

Having this level of detail where it matters most will help to illuminate relationships and advance the understanding of these environments. The deep-ocean is one of the most challenging ecosystems to deploy technology, and this work will be a small step towards improving our understanding of these environments, and a big step towards improving how effective we are a gathering information from these ecosystems.