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ARES may not look like Johnny Five, the famous robot from the
1986 movie scene, but this robot's ability to integrate robotics,
artificial intelligence (AI) and data science is altering materials
research in a big way at Air Force Research Laboratory.
The
AFRL Materials and Manufacturing Directorate's Autonomous Research
System, or ARES, can design, conduct and evaluate experimental data
without human intervention, revolutionizing the materials research
process as it is today.
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September 9, 2016 - The Air Force Research Laboratory's Autonomous
Research System, or ARES, uses artificial intelligence to design,
execute and analyze experiments at a pace much faster than
traditional scientific research methods. This robotic research
machine is revolutionizing materials science research and
demonstrates the benefits of human-machine interaction for rapid
advancement and development of knowledge today. (U.S. Air Force
photo by Marisa Novobilski)
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“To our knowledge, ARES is the first of its kind to link
autonomous robotics, artificial intelligence, data science
and in situ experimental techniques for materials
development,” said Dr. Benji Maruyama, a senior materials
research engineer in the Functional Materials Division,
Materials and Manufacturing Directorate, AFRL. “Not only
does it allow us to be faster and smarter in how we do
experiments, we can get to a scientific understanding in a
shorter amount of time.”
Traditional materials
science research is a time-consuming, human-centered process
that takes a certain kind of individual with the knowledge,
patience and understanding to design, conduct, analyze and
interpret experimental data, and then decide what to do
next, said Maruyama. A typical research team may only
conduct one or two experiments per day using traditional
research routines.
ARES, on the other hand, can complete upwards of 100
experiments per day, expediting the materials discovery
process.
“We are in the dark ages in the way we do
experiments, yet we are inventing such high-tech materials.
There is a disconnect between the research process and the
high-end technology output,” said Maruyama. “ARES combines
the best of hardware experimentation, and modeling &
simulation with an AI planner that proposes what to do next.
We can get feedback faster.”
ARES' robotic expertise
was tested by Maruyama's team in the field of carbon
nanotube growth, an area of materials research that is
traditionally poorly controlled and not very well
understood. Carbon nanotubes are extremely valuable in
materials science, as they are strong, light weight and have
an amazing ability to conduct heat and electricity.
Nanotubes can be used in a number of different applications,
from airplane wings to lightweight, flexible conductor
wires, ballistic materials, computer chips and even for drug
delivery.
ARES conducted more than 600 experiments in
autonomous mode, with the computer “brain” determining
experimental conditions to achieve an objective maximum
growth rate for the nanotubes. Human scientists set the
objective growth rate, which ARES used to execute the
research. Each new experiment performed by the robot
resulted in new knowledge, which ARES incorporated into the
design of future experiments. As the number of experiments
increased, the results became more constant, converging on
predicted growth rates for the carbon nanotubes, indicating
that the AI system learned to grow carbon nanotubes and
applied the intelligence with scientific success.
Though ARES is capable of conducting scientific research
autonomously and can generate rapid results, the role of the
researcher remains extremely important, said Maruyama. “ARES
will not replace humans, but rather the success of ARES
depends strongly on the partnership between the human
researcher and the robotic system—a human-machine trust,” he
said.
ARES frees the researcher from tedious
bench-level experiment activities, such as instrument
preparation, monitoring and cleaning, and allows them to
undertake the creative, insightful, higher-level thinking
that can lead to new discoveries, said Maruyama. “The beauty
is that it makes us more efficient. We are able to be faster
and smarter in how we do experiments and can get to a new
state of understanding,” he said.
While ARES proved
itself in carbon nanotube growth, autonomous research robots
have the potential for use in a number of scientific
research areas. Kevin Decker, a software engineer from UES,
Inc., is working with the ARES team to program the AI
software to allow ARES to be a generic research tool,
enabling it to work on other materials research problems.
In the future, the direction of ARES will be to explore
chemical and physical phenomena autonomously.
“There
are multiple types of machine intelligence that work for
different areas and specific problems, said Decker. “We are
working to develop software that incorporates multiple
different types of AI that will allow us to determine the
most suitable strategy for an experimental problem.”
ARES is a “disruptive tool” that is changing the research
ecosystem, according to Maruyama.
“Research is core to what
we do in the Air Force. We are trying to cause a disruptive
improvement to the process of research wherein not only can we do
research 100 times faster, but 100 times smarter and more
economically. We ask ourselves, how can we reengineer the research
process to make research better and more cost effective?”
As
ARES shows, robots and machine intelligence may be the answer.
By Marisa Novobilski, Air Force Research Laboratory
Provided
through DVIDS
Copyright 2016
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