DARPA announces fast lightweight autonomy UAV program

By Emily Aasand | December 29, 2014

The Defense Advanced Research Projects Agency, a department of the U.S. Military, issued an agency announcement solicitation for the Fast Lightweight Autonomy program which will focus on creating a new class of algorithms to enable small, unmanned vehicles to quickly navigate rooms, stairways and corridors without the use of a remote pilot.

In the solicitation, DARPA said the FLA program will focus on autonomy algorithms and software—specifically on sensing, perception, planning and control—rather than on the flight hardware platform.

If successful, the algorithms developed under this program could impact a wide range of unmanned systems by reducing the amount of processing power, communications, and human intervention needed for low-level tasks such as navigation through a cluttered environment, said DARPA.

“Birds of prey and flying insects exhibit the kinds of capabilities we want for small UAVs,” said Mark Micire, DARPA program manager. “Goshawks, for example, can fly very fast through a dense forest without smacking into a tree. Many insects, too, can dart and hover with incredible speed and precision. The goal of the FLA program is to explore non-traditional perception and autonomy methods that would give small UAVs the capacity to perform in a similar way, increasing an ability to easily navigate tight spaces at high speed and quickly recognize if it had already been in a room before.”

The program aims to develop unmanned aircraft vehicles small enough to fit through an open window and is able to fly at speeds up to 20 meters per second (45 miles per hour).

“Urban and disaster relief operations would be obvious key beneficiaries, but applications for this technology could extend to a wide variety of missions using small and large unmanned systems linked together with manned platforms as a system of systems,” said Stefanie Tompkins, director of DARPA’s defense sciences office. “By enabling unmanned systems to learn ‘muscle memory’ and perception for basic tasks like avoiding obstacles, it would relieve overload and stress on human operators so they can focus on supervising the systems and executing the larger mission.”