AI News, Programming drones to fly in the face of uncertainty

Programming drones to fly in the face of uncertainty

Many existing approaches rely on intricate maps that aim to tell drones exactly where they are relative to obstacles, which isn’t particularly practical in real-world settings with unpredictable objects.

“Overly confident maps won’t help you if you want drones that can operate at higher speeds in human environments,” says graduate student Pete Florence, lead author on a new related paper.

“An approach that is better aware of uncertainty gets us a much higher level of reliability in terms of being able to fly in close quarters and avoid obstacles.” Specifically, NanoMap uses a depth-sensing system to stitch together a series of measurements about the drone’s immediate surroundings.

At high speeds, computer-vision algorithms can’t make much of their surroundings, forcing drones to rely on inexact data from the inertial measurement unit (IMU) sensor, which measures things like the drone’s acceleration and rate of rotation.

“The key difference to previous work is that the researchers created a map consisting of a set of images with their position uncertainty rather than just a set of images and their positions and orientation,” says Sebastian Scherer, a systems scientist at Carnegie Mellon University’s Robotics Institute.

“Keeping track of the uncertainty has the advantage of allowing the use of previous images even if the robot doesn’t know exactly where it is and allows in improved planning.” Florence describes NanoMap as the first system that enables drone flight with 3-D data that is aware of “pose uncertainty,” meaning that the drone takes into consideration that it doesn't perfectly know its position and orientation as it moves through the world.

(The researchers tested NanoMap last year in a program tied to the Defense Advanced Research Projects Agency, or DARPA.) The team says that the system could be used in fields ranging from search and rescue and defense to package delivery and entertainment.

MIT's NanoMap Tech Allows for Consistent, High-Speed, Autonomous Drone Navigation

A group of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory has developed a system called “NanoMap.” This technology uses a drone’s uncertainty to its benefit allowing for unmanned aerial vehicles to navigate through complex environments at a consistent 20 mph pace.

“For the drone to plan motions, it essentially goes back in time to think individually of all the different places that it was in.” The implementation of NanoMap apparently reduced the drone’s crash rate from 25 percent to 2 percent, which is an extraordinary decrease and one that drone delivery companies eager to operate in narrow, inner-city environments are likely eager to employ.

MIT teaches drones to fly with uncertainty

MIT researchers are working on a new steering system for drones that uses uncertainty to ensure that they don’t hit obstacles as they fly autonomously.

The system is a bit complex but it’s called NanoMap and it quite simply finds ways to get from point A to point B without crashing and while handling random objects in its path.

With drift well over 1 m/s, the drone was only safe 10 percent of the time, but that was three times more robust than testing without uncertainty modeling.

The press release sums it up overall: If NanoMap wasn’t modeling uncertainty and the drone drifted just 5 percent away from where it was expected to be, the drone would crash more than once every four flights.

MIT’s NanoMap helps drones to navigate safely at high speed

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a sophisticated computer vision system for flying robots.

“Overly confident maps won’t help you if you want drones that can operate at higher speeds around humans,” says graduate student Pete Florence, lead author on a related paper.

“An approach that is better aware of uncertainty gets us a much higher level of reliability in terms of being able to fly in close quarters and avoid obstacles.” Read more: Pyeongchang Winter Olympics to be defended by drone-catching drones Using NanoMap, a drone can build a picture of its surroundings by stitching together a series of measurements via depth-sensing.

“For the drone to plan its motions, it essentially goes back into time to think individually of all the different places that it was in.” NanoMap operates under an assumption that humans are familiar with: if you know roughly where something is and how large it is, you don’t need much more detail if your only aim is to avoid crashing into it.

“The key difference to previous work is that the researchers have created a map consisting of a set of images with their position uncertain, rather than just a set of images with their positions and orientation,” says Sebastian Scherer, a systems scientist at Carnegie Mellon University’s Robotics Institute.

This allows for improved planning.” As drones spread into more and more vertical applications, such as farming, manufacturing, critical infrastructure maintenance, building, environmental monitoring, security, law enforcement, broadcasting, autonomous cargo, deliveries, and even public transport, their safety around human beings, and in complex environments, becomes ever more important to demonstrate.

MIT’s NanoMap Uses Uncertainty to Improve Drone Navigation Systems

The NanoMap system, which allows drones to top speeds of 20 miles per hour while consistently avoiding obstacles presented by dense environments such as forests or skyscraper-lined cities, has been shown to reduce crash rates as low as 2 percent.

While systems commonly referred to as simultaneous localization and mapping (SLAM) have been used in other technology, the speed with which an in-motion operating system must simultaneously collect and utilize data in order to plan movements have made it tricky to incorporate in drones.

Florence, professor Russ Tedrake and research software engineers John Carter and Jake Ware designed the system by allowing the drone’s sensors to rely upon predetermined scenarios and terrains to figure out how to react in a given situation, even if it hasn’t encountered a truly similar obstacle in the past.

MIT's NanoMap enables Drone Navigation in Uncertain Environments | QPT

Companies like Amazon have big ideas for drones that can deliver packages right to your door. But even putting aside the policy issues, programming drones to ...

Drone Navigation in Uncertain Environments

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