Motion-Planning Chip Speeds Robots

A programmable chip turns a robot’s long pauses into quick action

6 min read
Photo: Duke Robotics
Jaco robot arm doing motion planning with custom FPGA processor
Photo: Duke Robotics

If you’ve seen a robot manipulation demo, you’ve almost certainly noticed that the robot tends to spend a lot of time looking like it’s not doing anything. It’s tempting to say that the robot is “thinking” when this happens, and that might even be mostly correct: Odds are that you’re waiting for some motion-planning algorithm to figure out how to get the robot’s arm and gripper to do what it’s supposed to do without running into anything. This motion-planning process is one of the most important skills a robot can have, and it’s also one of the most time consuming.

Researchers at Duke University, in Durham, N.C., have found a way to speed up motion planning by three orders of magnitude while using one-twentieth the power. Their solution is a custom processor that can perform the most time-consuming part of the job—checking for all potential collisions across the robot’s entire range of motion—with unprecedented efficiency.

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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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