Artificial Intelligence and Machine Learning for Unmanned Vehicles

Military & Aerospace Electronics

Published in Military & Aerospace Electronics
Written by Jamie Whitney

What was once the realm science fiction writers is growing as unmanned vehicles are given more capability for autonomous decision-making thanks to improvements in artificial intelligence (AI) and machine learning.

First, unmanned aerial vehicles (UAVs) took to the skies. In fact, the British military developed the first radio-controlled unmanned aircraft during World War I — a scant 14 years after the Wright Brothers’ first flight in 1903. UAVs really came into their own during the Vietnam War and have become even more prevalent and essential since then.

As UAVs proved themselves more invaluable over the years, the Department of Defense (DOD) asked industry experts to bring the unmanned revolution down to Earth in the form of unmanned ground vehicles (UGVs) and even to the seas with unmanned underwater vehicles (UUVs).

Though each domain had unique problems to solve, the difficulty communicating from the surface to UUVs meant a greater need for machine autonomy — the ability to make decisions without direct human input — made possible with artificial intelligence (AI) and machine learning (machine learning).

Even though radio frequency (RF) and satellite communications (SATCOM) methods allow for relatively easy contact with UGVs and UAVs, the prospect of “smart” unmanned systems on the ground and in the sky also is helping drive the autonomous revolution.

Processing power

The ability for unmanned systems underwater, on the ground, and in the skies with full autonomy requires a lot of processing power.

Mike Southworth, the senior product manager at Curtiss-Wright Defense Solutions division in Salt Lake City notes that tying together all the different technologies needed to make a vehicle autonomous is a large source for the need for big computing power.

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