Designed by Saxena, assistant professor at Cornell, the flying robot, the size of a card table is able to guide itself through forests, tunnels or damaged buildings.
The toughest part is keeping the object from slamming into walls and tree branches.
Human controllers can't always react swiftly enough and the radio signals may not reach everywhere the robot goes, according to a Cornell statement.
So, Saxena, who did his B.Tech. in 2004 from the Indian Institute of Technology (IIT) Kanpur, in a bid to overcome these limitations is building on methods he previously had developed to turn a flat video camera image into a 3-D model.
He used 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.
The flying robot was tested in 53 autonomous flights in obstacle-rich environments including Cornell's Arts Quad succeeding in 51 cases, failing twice because of winds.
The test vehicle is a quadrotor, a commercially available flying machine about the size of a card table with four helicopter rotors.
Graduate students Ian Lenz and Mevlana Gemici trained the robot with 3-D 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 colour, shape and texture.
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 3-D 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.
Saxena plans to improve the robot's ability to respond to environment variations such as winds and enable it to detect and avoid moving objects, like real birds.
The results were presented at the International Conference on Intelligent Robots and Systems in Portugal during early October.