How the U.S. Army Is Turning Robots Into Team Players

Engineers battle the limits of deep learning for battlefield bots

11 min read

RoMan, the Army Research Laboratory's robotic manipulator, considers the best way to grasp and move a tree branch at the Adelphi Laboratory Center, in Maryland.

Evan Ackerman
LightGreen

This article is part of our special report on AI, “The Great AI Reckoning.

"I should probably not be standing this close," I think to myself, as the robot slowly approaches a large tree branch on the floor in front of me. It's not the size of the branch that makes me nervous—it's that the robot is operating autonomously, and that while I know what it's supposed to do, I'm not entirely sure what it will do. If everything works the way the roboticists at the U.S. Army Research Laboratory (ARL) in Adelphi, Md., expect, the robot will identify the branch, grasp it, and drag it out of the way. These folks know what they're doing, but I've spent enough time around robots that I take a small step backwards anyway.

The robot, named RoMan, for Robotic Manipulator, is about the size of a large lawn mower, with a tracked base that helps it handle most kinds of terrain. At the front, it has a squat torso equipped with cameras and depth sensors, as well as a pair of arms that were harvested from a prototype disaster-response robot originally developed at NASA's Jet Propulsion Laboratory for a DARPA robotics competition. RoMan's job today is roadway clearing, a multistep task that ARL wants the robot to complete as autonomously as possible. Instead of instructing the robot to grasp specific objects in specific ways and move them to specific places, the operators tell RoMan to "go clear a path." It's then up to the robot to make all the decisions necessary to achieve that objective.

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Russia is the world's largest wheat exporter, with 20 percent of the world's wheat trade. Combine harvesters that can drive themselves using technology from Russian company Cognitive Pilot are helping to make the harvesting process faster and more efficient.

Cognitive Pilot
Blue

The field of automated precision agriculture is based on one concept—autonomous driving technologies that guide vehicles through GPS navigation. Fifteen years ago, when high-accuracy GPS became available for civilian use, farmers thought things would be simple: Put a GPS receiver station at the edge of the field, configure a route for a tractor or a combine harvester, and off you go, dear robot!

Practice has shown, however, that this kind of carefree field cultivation is inefficient and dangerous. It works only in ideal fields, which are almost never encountered in real life. If there's a log or a rock in the field, or a couple of village paramours dozing in the rye under the sun, the tractor will run right over them. And not all countries have reliable satellite coverage—in agricultural markets like Kazakhstan, coverage can be unstable. This is why, if you want safe and efficient farming, you need to equip your vehicle with sensors and an artificial intelligence that can see and understand its surroundings instead of blindly following GPS navigation instructions.

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