ETH building fleet of acrobatic quadrotor robots

Engineers at ETH Zurich are designing flying machines capable of guiding themselves into complex, acrobatic flight formations

2 min read
ETH building fleet of acrobatic quadrotor robots
[youtube //www.youtube.com/v/xx3rsadZA6M&hl=en&fs=1&rel=0 expand=1]

Are they called quadrocopters or quadrotors?

Raffaello D'Andrea and his group at ETH Zurich are building a bunch of these amazing flying machines, which they plan to transform into an autonomous stunt flying squad. So far the vehicles can fly in circles and perform audacious flips, but the researches want more for their repertoire: they're designing control algorithms to make a dozen or more quadrocopters guide themselves into complex, acrobatic flight formations.

The trick involves more than just the flying robots. The machines are designed to fly within a special sensor-equipped environment where they'll "teach themselves -- and each other -- how to fly." The researchers call their airspace the Flying Machine Arena. The video above shows how users will be able to control the vehicles by moving a "magic wand" -- the controller has markers and the arena's sensor system captures the gestures and sends control signals to the vehicles. From their site:

Human beings learn from experience: when we try something and fail, we try doing it a different way the next time around. And we are incredibly efficient at this process.

We are so adept, in fact, that when it comes to learning complex activities such as racing a car or playing a violin, we can easily outperform automated systems. This is why we use autopilot programs for the routine aspects of flying a plane (such as cruising, take-off and landing), but why we still need human pilots to handle unexpected events and emergencies.

We are currently developing algorithms that will narrow the learning gap between humans and machines, and enable flight systems to ‘learn’ the way humans do: through practice.

Rather than being programmed with detailed instructions, these flight systems will learn from experience. Like baby birds leaving the nest, they will be clumsy at first. Over time, however, they will become capable of sophisticated, coordinated maneuvers.

Unlike humans, these systems won’t make the same mistake twice. And, when networked, they have the added advantage of being able to learn from each other’s successes and failures. The result is an impressively steep learning curve!

flying machine arena

Image: ETH Zurich

The Conversation (0)

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

“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.

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

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.

Keep Reading ↓ Show less