DARPA's fully-loaded quadcopter autonomously navigates an indoor maze at 45 mph

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The quadcopter reached speeds of 20 m/s (45 mph)

The quadcopter reached speeds of 20 m/s (45 mph) (Credit: DARPA) View gallery (5 images)

A fully-laden quadcopter recently flew through an indoor obstacle course at 45 mph (72 km/h) as part of DARPA's Fast Lightweight Autonomy (FLA) program. The test flight was conducted entirely under autonomous control with the goal of developing small unmanned aerial vehicles (UAV) with the ability to navigate through tight spaces without the need for outside control or GPS.

Drones are becoming an increasingly common part of everyday military and civilian life. For the armed forces, they are a way of carrying out reconnaissance or engaging the enemy while keeping soldiers out of harm's way. For civilians, they allow engineers or disaster workers to safely access hazardous environments. The trouble is, drones aren't very good at negotiating cramped, unpredictable spaces – and certainly not at speed.

"Very lightweight UAVs exist today that are agile and can fly faster than 20 meters per second (45 mph), but they can't carry the sensors and computation to fly autonomously in cluttered environments," says Mark Micire, DARPA program manager. "And large UAVs exist that can fly high and fast with heavy computing payloads and sensors on board. What makes the FLA program so challenging is finding the sweetspot of a small size, weight and power air vehicle with limited onboard computing power to perform a complex mission completely autonomously."

DARPA's FLA program is aimed at creating a demonstrator UAV system that is small enough to be put through a building window by hand, yet can autonomously negotiate the interior at up to 45 mph without help from an operator or GPS waypoints. The agency says the key to this is a new a new class of algorithms that use less processing power and reliance on a human operator, yet can handle rooms, stairs, corridors, and obstacles.

The idea behind the program is that it will not only allow the UAVs to operate in cluttered environments with poor navigation signals, but will also free up the operator to concentrate on higher level tasks while the drone worries about the driving. In this way, there would be less operator fatigue and one person could monitor several drones at one.

The test flights, which did result in a few crashes, were made at a disused air hangar at Otis Air National Guard Base, Cape Cod, Massachusetts, which had been converted into a maze using boxes and simulated walls. Here, three research teams used a commercial DJI Flamewheel 450 airframe equipped with E600 motors, 12-in propellers, and a 3DR Pixhawk autopilot. In addition, it carried a full load of high-definition onboard cameras, LIDAR, sonar, inertial measurement units, and other sensors.

For the first tests, the team flew the UAV at both high and low speeds with the operator acting only as an observer. DARPA says now that the initial data collection phase is complete, the obstacle course will be made more complicated and realistic. The hope is that the algorithms may one day be adapted not only to UAVs, but to ground and marine vehicles in GPS-degraded or denied environments.

"We're excited that we were able to validate the airspeed goal during this first-flight data collection," says Micire. "The fact that some teams also demonstrated basic autonomous flight ahead of schedule was an added bonus. The challenge for the teams now is to advance the algorithms and onboard computational efficiency to extend the UAVs' perception range and compensate for the vehicle's' mass to make extremely tight turns and abrupt maneuvers at high speeds."

The video below shows the quadcopter blasting through the course.

Source: DARPA

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