Aerospace & Defense Technology - December 2022 - 18

Military Robotics
NEMO combines information from the Awarion™ autonomous lookout system with deep-learning YOLO
neural networks and probabilistic data fusion methods to accurately detect and classify marine mammals
in visual and acoustic sensor feeds. (Image: Charles River Analytics)
gence " doesn't guarantee mission success-how
AI performs depends on its
knowledge of the environment and
everything in it, including itself.
To accurately identify whether
something is worth investigating in
an image of the seafloor, an AI must
also know the seafloor type, the state
of the water column, and what is usual
for that environment. It also needs to
be aware of its own performance; if
something is wrong with a sensor, it
must discount that sensor's observations,
the same way we would if we
were wearing glasses with a smudge
on the lens. At a higher level, it needs
to know if it has enough experience in
a certain environment, or if it has previously
performed well or poorly in
that environment.
All this information must feed into a
decision-making process that supports
curious behaviors. Decision-making
software, that is, the combination of AI
algorithms and knowledge models that
can make a machine exhibit curiosity,
must be able to run on small, lightweight,
low-power processors onboard
the vehicle.
Anomaly Detection: Target Detection,
Classification, and World Modeling.
Advances in target detection and
classification on their own can't determine
whether an object is unusual.
That judgment requires a model of the
environment, and an understanding of
what is normal within it.
Currently, baseline parameters are
The ALPACA machine learning agent not only learns how to navigate in outdoor environments, it also
learns about its own competence at the task. (Image: Charles River Analytics)
fields: perception, AI algorithms,
world modeling, navigation, anomaly
detection, human-understandable
communication, and more. Let's
examine a few.
Perception: Signal Processing and
Data Interpretation. Visual sensors
(EO/IR) become less effective underwater,
so their information must be fused
with acoustic sensors (sonar, doppler,
vibrometers). Advances in onboard
image processing are needed to remove
artifacts like " marine snow " -bright
spots in underwater images from the
18
reflection of particles suspended in the
water-so the images are clear enough
to support decision-making. All sensors
must become smaller and require less
energy to support longer missions to
fully explore an area or track species'
patterns of life.
Onboard Intelligence: Autonomy,
Planning, Situational and Self-Awareness.
We need a big shift in AI and
machine learning to incorporate true
situational awareness; that is, awareness
of the surrounding external and internal
environment. Artificial " intellimobilityengineeringtech.com
mostly
provided by humans, but by
replacing these hardcoded rules with
complex models, a machine can know,
for example, how water and sediment
tend to mix in a certain underwater
environment, and how the information
being returned by the sonar sensor
should be interpreted differently as
a result.
Development of a world model is in
progress for the topography of the seafloor.
Companies like Terradepth are
using a fleet of autonomous submersibles
to generate a set of detailed bathymetric
maps so we can Google Earth
above and below the water's surface.
Human-Machine Collaboration
and Communication: Current methods
of docking and transferring data
Aerospace & Defense Technology, December 2022

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Aerospace & Defense Technology - December 2022

Table of Contents for the Digital Edition of Aerospace & Defense Technology - December 2022