sFly Quadrotors Navigate Outdoors All By Themselves

No GPS, no Vicon, no nothing: these quadrotors can navigate anywhere

2 min read
sFly Quadrotors Navigate Outdoors All By Themselves

Quadrotors are famous for being able to pull all sorts of crazy stunts, but inevitably, somewhere in the background of the amazing video footage of said crazy stunts you'll notice the baleful red glow of a Vicon motion tracking system. Now, we don't want to call this cheating or anything, but we're certainly looking forward to the day when quadrotors can do this outside of a lab, and the sFly project is helping to make this happen.

What makes the sFly project, led by ETH Zurich's Autonomous Systems Lab, different is that the sFly quadrotors don't rely on motion capture systems. They also don't rely on GPS, remote control, radio beacons, laser rangefinders, frantically waving undergrads, or anything else. The only thing that sFly has to go on is an IMU and an onboard camera (and an integrated computer), but using just those systems (and a "very efficient onboard inertial-aided visual simultaneous localization and mapping algorithm"), sFly is capable of navigating all by itself. And if you have a fleet of sFly quadrotors, you can use them to make cooperative 3D maps of the environment:

Each quadrotor is completely autonomous, but they're also equipped with two extra cameras that stream stereo imagery back to a central computer over GSM or Wi-Fi that takes the data from several quadrotors and combines it into an overall 3D model of the environment as a whole. Then, the computer can guide each robot to an optimal surveillance site. The idea here is that you'd be able to rapidly deploy an sFly system with a swarm autonomous quadrotors in a disaster area or somewhere else without any infrastructure (or even a GPS signal) and still be able to take advantage of some clever autonomous aerial mapping.

[ sFly ]

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

Engineers battle the limits of deep learning for battlefield bots

11 min read
Robot with threads near a fallen branch

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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