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IROS 2013: Robot Cars Get Hyper-Maneuverable With Actuated Tails

A robot with a tail modeled on a cheetah can make turns at speeds twice as fast

2 min read
IROS 2013: Robot Cars Get Hyper-Maneuverable With Actuated Tails

That 2012 paper from UC Berkeley on the advantages of giving mobile robots tails continues to inspire roboticists, nearly two years later. At IROS 2013, we checked out a new implementation of an actuated tail that makes for one seriously maneuverable robot car.

The robot is called Dima (a name derived from a Sotho word that means "flash of lightning"), and it was deliberately designed to be able to achieve high speeds with a high center of mass, a combination that works best (or only) when driving in straight lines. Turning at any speed that you might charitably call exciting leads to an immediate toppling over, but the addition of an actuated tail that can swing in the roll axis of the robot can effectively keep it stable:

This is a bit different from some of the other tail-assisted turns that we've seen robots doing (most notably this robot from UC Berkeley), because in this case, the tail is being used to counteract the torque that the robot generates while turning, rather than being used to generate torque to turn the robot. Also, the tail is being turned in a roll axis instead of a yaw axis, an idea that the researchers got after watching how a cheetah's tail moves when it makes high-speed turns.

After a bunch of experiments with both tail-less and tailed versions of Dima, results showed that the addition of the actuated tail allowed the robot to make stable turns at over twice the speed that it would be able to otherwise (7.5 m/s as opposed to 3.1 m/s). 

So, the obvious question now is whether there are any rules in Formula One specifically prohibiting the use of active tails, and if not, when we'll start seeing them on race cars.

"Rapid Turning at High-Speed: Inspirations from the Cheetah's Tail," by Amir Patel and Martin Braae from the University of Cape Town, South Africa, was presented last week at IROS 2013 in Tokyo, Japan.

[ UCT Robotics ]

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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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