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Ground-Effect Robot Could Be Key To Future High-Speed Trains

Trains that levitate on cushions of air could be the future of fast and efficient travel, if this robot can figure out how to keep them stable

2 min read
Ground-Effect Robot Could Be Key To Future High-Speed Trains

japanese air cushion high speed train

Japanese prototype of a train that levitates on cushions of air.

High speed trains are huge in Asia, but barring a catastrophe, most of them are designed to stay firmly on the ground, running on rails. There are plenty of good reasons not to run on rails, though, one of which is that you can go much faster without all that friction. This is the idea behind maglev trains, but there’s still a lot of wind drag that crops up between the bottom of a maglev train and its track that makes them less efficient (which combined with other problems make maglevs very costly).

japanese air cushion high speed train

A ground-effect vehicle takes advantage of this fast-moving air and uses some stubby little wings to fly just above the ground, like a maglev without the mag. This is a tricky thing to do, since you have to control the vehicle more like an airplane than a train, meaning that you have to deal with pitch, roll, and yaw and not just the throttle. A Japanese research group led by Yusuke Sugahara at Tohoku University has built robotic prototype of a free flying ground-effect vehicle [photo above] that they’re using to test an autonomous three axis stabilization system:

The researchers are looking to use this robot to generate a dynamic model of how vehicles like these operate, which they hope to apply to a manned experimental prototype train [first photo at the top] that can travel at 200 kilometers per hour in a U-shaped concrete channel that keeps it from careening out of control.

Later, the plan is that the same technology can scale and power a large commuter rail system called the Aero Train [concept below]. If this is the future of commuting, we’ll be literally flying to work some day.

japanese air cushion high speed train

Sugahara and his colleagues describe the project in a paper, “Levitation Control of Experimental Wing-in-Ground Effect Vehicle along Z Axis and about Roll and Pitch Axes,” presented today at the IEEE International Conference on Robotics and Automation (ICRA), in Shanghai.

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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

“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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