DARPA has completed Phase 2 flight tests for its Fast Lightweight Autonomy (FLA) programme, demonstrating advanced algorithms performing real-world tasks without human assistance.
The programme aims to enable small air and ground systems to autonomously perform tasks dangerous for humans, such as pre-mission reconnaissance in a hostile urban setting or searching damaged structures for survivors following an earthquake.
Building on Phase 1 flight tests in 2017, researchers advanced the software and adapted commercial sensors to achieve better performance with smaller, lighter quadcopters. During Phase 2, a team of engineers from the Massachusetts Institute of Technology and Draper Laboratory (MIT/Draper) reduced the number of onboard sensors to lighten the air vehicle for higher speed.
Aerial tests, conducted in a mock town at the Guardian Centers training facility in Perry, Georgia, showed significant progress in urban outdoor as well as indoor autonomous flight scenarios including flying at increased speeds between multi-story buildings and through tight alleyways while identifying objects of interest; flying through a narrow window into a building and down a hallway searching rooms and creating a 3-D map of the interior; and identifying and flying down a flight of stairs and exiting the building through an open doorway.
Using neural nets, the onboard computer recognised roads, buildings, cars, and other objects and identified them as such on the map, providing clickable images as well. The operator could download the map and images from the onboard processor after the mission is completed.
Additionally, the MIT/Draper team incorporated the ability to sync data collected by the air vehicle with a handheld app called the Android Tactical Assault Kit (ATAK), which is already deployed to military forces. Using an optional Wi-Fi link from the aircraft, the air vehicle can send real-time imagery of objects of interest. With exploration mode mode on, the air vehicle identified cars and provided their location with clickable high-resolution images in real-time via Wi-Fi, appearing as an overlay on the ATAK geospatial digital map on a handheld device.
Begun in 2015, the FLA programme focused on developing advanced autonomy algorithms—the smart software needed to yield better performance from a lightweight quadcopter weighing about 5lbs with limited battery power and computer processing capability onboard.
Algorithms developed in the FLA programme have been scheduled to transition to the Army Research Laboratory for further development for potential military applications.