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Blimps Seem Like the Friendliest Kind of Indoor Flying Robots

Georgia Tech's Miniature Autonomous Blimp can recognize and follow humans, and it's gentle enough to fly in your home

2 min read
Miniature Robotic Blimp.
Image: Georgia Tech

Every time we go to a conference, we see flying robots that aregetting smaller and more talented, capable of dynamically avoidingall sorts of obstacles, indoors and out. But that’s a lot of work. What’s less work is floating calmly through the air, without any concern for hurting people or running into things, or running out of battery: Such is the life of the gentle and slightly chubby Miniature Autonomous Blimp from Georgia Tech (GT-MAB), which can now detect faces and autonomously follow people around.

Using a blimp rather than something with spinning rotors solves lots of common problems with UAVs, though it also creates a few new ones. A blimp is inherently very safe, since impacts, even with people, are more comical than dangerous. Without wasting energy keeping itself aloft, battery life is measured in hours rather than minutes, and hovering in particular is very energy efficient, since it’s the default state of the blimp. The tradeoffs are that dynamic movement isn’t really possible, outdoor operation is a bad idea, and even if you’ve decided to call your blimp “miniature,” it’s not ever going to be particularly inconspicuous.

A blimp is inherently very safe, since impacts, even with people, are more comical than dangerous. Without wasting energy keeping itself aloft, battery life is measured in hours rather than minutes, and hovering in particular is very energy efficient, since it’s the default state of the blimp.

But for some specific environments and use cases, like indoor interaction with people, miniature blimps seem like a very friendly idea, if they can be taught to do useful things. In particular, blimps could be good at detecting and following people, since they’re exceptionally stable, vibration free, and non-threatening even in close proximity.

GT-MAB was developed by a Georgia Tech team led by Professor Fumin Zhang. The researchers presented the face-tracking capability at the International Conference on Robotics and Automation (ICRA) in Singapore last week. 

Since the blimp can lift just 50 grams in total, one of the biggest challenges was minimizing the on-board hardware, which consists of five small motors (with propellers), a microcontroller, battery, wireless camera, and Xbee wireless module. Instead of using stereo vision or a 3D sensor to track people, the blimp’s camera beams the video to a ground PC, which runs the face-tracking algorithms and uses a Xbee module to transmit control signals back to the blimp. The tricky bit is then calculating the blimp-to-face distance to enable a courteous following behavior. Without any kind of depth information, the blimp uses prior knowledge of human face lengths to estimate distance from a monocular image, which only works because the blimp’s camera (unlike the camera on a quadrotor) is always face-parallel, and never pitching or rolling.

Once the blimp locks on to a person it wants to follow, it uses its five little propellers to help it translate in all three dimensions as well as yaw, and it works fairly well—at least as long as the “human is not moving too fast,” the researchers say. Seems reasonable to me. 

“Monocular Vision-based Human Following on Miniature Robotic Blimp,” by Ningshi Yao, Emily Anaya, Qiuyang Tao, Sungjin Cho, Hongrui Zheng, and Fumin Zhang from Georgia Institute of Technology, was presented at ICRA 2017 in Singapore.

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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

“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.

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