Cornell researchers have created an autonomous flying robot that is as smart as a bird when it comes to maneuvering around obstacles.
Able to guide itself through forests, tunnels or damaged buildings, the machine could have tremendous value in search-and-rescue operations. Small flying machines are already common, and GPS technology provides guidance. Now, Ashutosh Saxena, assistant professor of computer science, and his team are tackling the hard part: how to keep the vehicle from slamming into walls and tree branches. Human controllers can’t always react swiftly enough, and radio signals may not reach everywhere the robot goes.
The test vehicle is a quadrotor, a commercially available flying machine about the size of a card table with four helicopter rotors. Saxena and his team have already programmed quadrotors to navigate hallways and stairwells. But in the wild, current methods aren’t accurate enough at large distances to plan a route around obstacles. Saxena is building on methods he previously developed to turn a flat video camera image into a 3-D model of the environment using such cues as converging straight lines, the apparent size of familiar objects and what objects are in front of or behind each other — the same cues humans unconsciously use to supplement their stereoscopic vision.
via Cornell University – Bill Steele
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