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, researchers 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. Researchers
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.
They are building on methods
previously developed to turn a flat video camera image into a 3D model of the
environment using such cues as converging straight lines. They also trained the
robot with 3D pictures of such obstacles as tree branches, poles, fences and
buildings; the robot's computer learns the characteristics all the images have
in common, such as color, shape, texture and context. The resulting set of
rules for deciding what is an obstacle is burned into a chip before the robot
flies. In flight the robot breaks the current 3D image of its environment into
small chunks based on obvious boundaries, decides which ones are obstacles and
computes a path through them as close as possible to the route it has been told
to follow, constantly making adjustments as the view changes. It was tested in
53 autonomous flights in obstacle-rich environments -- including Cornell's Arts
Quad -- succeeding in 51 cases, failing twice because of winds.
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