Intelligent Autonomous Flying Robots Learn and Map Environment As They Fly 37
An anonymous reader writes with this story about a machine-learning project out of the UK's University of Sheffield: Using simple drones the researchers have created automatic-control software that enables the "flying robot" to learn about its surroundings using a camera and an array of sensors. The robot starts with no information about its environment and the objects within it. But by overlaying different frames from the camera and selecting key reference points within the scene, it builds up a 3D map of the world around it. Other sensors pick up barometric and ultrasonic data, which give the robot additional clues about its environment. All this information is fed into autopilot software to allow the robot to navigate safely, but also to learn about the objects nearby and navigate to specific items.
Different than the SLAMM stuff at Carnegie Lab (Score:1, Interesting)
Carnegie Mellon folks developed the SLAMM algorithm (and variants of if) some years back to do live mapping on their quadrotors. It has been used by almost everybody who is doing autonomous flying robots. It is hard not to say that anyone was not influenced by that work. Some of their work had laser scanners that would map the surroundings and identify walls -- building out a maze of sorts as it explored. Heck on seeedstudio.com you can pickup a (LIDAR) 360 2D laser scanner and algorithm to build your own.
Not taking anything away from the English (as what they have done is pretty impressive and a different approach), though am curious how it is different algorithm wise?
One of the research avenues I think at some point will be to have mesh networking on these robots to share info so that in a hostile/harsh environment as these things get damaged the mapping data is passed along for fire crews, soldiers, HAZMAT, etc.