Micro Air Vehicles (MAVs) may be small, but they're costly, so researchers have devised ways for them to fly in urban and indoor environments where they could otherwise get lost or crash.
Existing highly-precise, non-Robust Robotics Group at MIT's Computer Science and Artificial Intelligence Laboratory addressed this problem by developing algorithms that allow a miniature robo-quadrocopter to estimate their relative position, identify a clear path and then fly through dense air space.navigation units are too large, heavy, and expensive to install on an MAV. But the
"The size, weight, and budget limitations of micro air vehicles (MAVs) typically preclude high-precision inertial navigation units that can mitigate the loss of GPS," according to the MIT release. "We are developing estimation and planning algorithms that allow MAVs to use environmental sensors such as range finders to estimate their position, build maps of the environment, and fly safely and robustly."
The laser range-finder estimates the MAV's position, yaw angle, and altitude information from surrounding landscape out to about a 12 foot range.
In recent tests, the MAV navigated cluttered offices and unknown hallways and found its way through other unmapped environments by using its onboard laser scanners and cameras to build its own map, according to MIT.
MIT's secret sauce is based on the Belief Roadmap (BRM) algorithm, which performs searches in the MAV's "information space" to determine the "minimum expected cost path for the vehicle," according to a learned paper on the subject. Anything that mentions the Unscented Kalman Filter is worth a click.(PDF)