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Bolstering Army Tanks Capability with Artificial Intelligence

  /  Artificial Intelligence   /  Bolstering Army Tanks Capability with Artificial Intelligence

Bolstering Army Tanks Capability with Artificial Intelligence

Will AI-powered Armour Vehicles Replace Human Militants?

Although analysts have alerted that disruptive technologies ought not to be considered the magic ingredient in military preparedness, it remains undeniable that their acquisition could potentially provide the continents with a relative competitive advantage in the field. Over the past decade, several leaps in technologies and their projected trajectories have been provided militaries worldwide with a wide range of options and areas to improve their capabilities. The most popular and developing area with extensive capacities of disruption is artificial intelligence (AI).

AI can be deployed in warfare using a combination of surveying, machine learning, searching, and planning to analyze big data and automate decision-making. Through its unique and evolving algorithms and edge-cutting technologies, AI can offer a military platform that can streamline rapid responses to cyberattacks, conventional onslaughts, and warfare.

For instance, the U.S Army wants future armored vehicles like tanks to instantly decide about terrain navigation, target identification, incoming enemy fire, force positions, and warfare strategy. The military wants this to occur in the blink of an eye. And every nuance needs to be controlled or micro-managed by humans.

Military tanks are now taking on a newer, more advanced character as AI-enabled sensors, computers, and targeting systems increasingly process and organize information more quickly, enabling ever-advancing measures of autonomy. Operating a tank relies entirely on manual inputs from highly-trained operations.

Abrams Master Gunner Sgt. 1st Class Dustin Harris explains, “Today, tank crews use a manual process to detect and engage targets.”

“Tank commanders and gunners are manually slewing, trying to identify targets using the sensors. Once they come across a target, they have selected the ammunition manually that they will use to service that target, lase the target to get an accurate range to it, and a few other factors,” he adds.

This process has to be repeated for each target, which can take time a long time, but everything will be done manually.

Army senior leaders identify that how the tank operates is broadly analogous to how those things were done 30-45 years ago. With extensive technical expertise, many of them recognized that opportunities to improve the way these crews operate.  Therefore, they challenged the combat capabilities Development command, the Armaments Centre, and the C5ISR center to look at the problem.

On October 28, the Army invited reporters to Aberdeen Proving Ground to see the Advanced Targetting and Lethality Aided System (ATLAS) solution. It uses advanced sensors, machine learning (ML) algorithms, and a new touchscreen display to automate the process of finding and fixing targets, enabling crews to respond to threats faster than ever before.

“The assistance that we’re offering to the soldiers will stimulate those engagement times and allow them to execute multiple targets simultaneously that they currently take to execute a single target,” said C5ISR project lead for ATLAS Dawne Deaver.

The ATLAS prototype looks like something out of a Star Wars film at first glance, albeit with treads and not easily harpooned legs. The system is deployed on a mishmash of systems, a sleek black General Dynamics Griffin I chassis with the Army’s Advanced Lethality and Accuracy System for Medium Calibar (ALAS-MC) auto-loading 50mm turret stacked on top.


ATLAS and its Function

ATLAS is not the tank. It is a mounted sensor collecting data, the machine learning algorithm processing that data, and the display/controller that the crew uses to operate the tank.

ATLAS starts with the optical sensor mounted on top of the tank. When activated, the sensor consistently scans the battlefield, feeding that data into an ML algorithm that automatically identifies threats.

Pictures of those threats are then sent to a new touchscreen display, the graphical user interfaces for the tank’s intelligent fire control system. These images are lined up vertically on the left side of the screen, with the primary part of the display showing what the gun is aimed at. Around the edges are many different controls for selecting ammunition, camera settings, fire type, and more.

The tank automatically swivels its gun, training its sights on the selected object’s dead center by touching one of the targets on the left with a finger. Doing that, the fire control system automatically recommends the appropriate ammo and setting like a burst or a single shot to respond with. The user can adjust these as required.

With everything ready, including the target in its sights, weapon selected, the operator has a choice. The operator can approve the AI’s recommendations, adjust the setting before responding, pull the trigger, or disengage. This entire process from target detection to pull off the trigger takes just seconds. Once one target is destroyed, the operator can touch the screen to select ATLAS’s next target.

Although ATLAS is efficient enough to replace human tank crews, it is not meant to. It is made to make militants’ jobs more manageable, and in the process, much faster. Even if ATLAS is widely adopted, crews will still need to be trained for manual operations (if the system breaks down). They will need to rely on their training to verify the algorithm‘s recommendations.