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A flexible approach like this comes with a high level of guarantees that the software will work, even in the face of uncertainties with both the surroundings and the hardware itself. The algorithm can easily be extended to drones of different sizes and payloads, as well as ground vehicles and walking robots.
As for the environment, imagine the drone choosing between making a forceful roll maneuver that will avoid a tree by a large margin, versus flying straight and avoiding a tree by a small amount.
“A traditional approach might prefer the first since avoiding obstacles by a significant amount seems ‘safer,’” Majumdar says. “But a move like that actually may be riskier because it’s more susceptible to wind gusts. Our method makes these decisions in real-time, which is critical if we want drones to move out of the labs and operate in real-world scenarios.”
A clear path to avoiding obstacles
CSAIL researchers have been working on this problem for many years. Professor Nick Roy has been honing algorithms for drones to develop maps and avoid objects in real-time; in November a team led by PhD student Andrew Barry published a video demonstrating algorithms that allow a drone to dart between trees at speeds of 30 miles per hour.
While these two drones cannot travel quite as fast as Barry’s, their maneuvers are generally more complex, meaning that they can navigate in smaller, denser environments.
“Enabling dynamic flight of small, off-the-shelf quadcopters is a marvelous achievement, and one that has many potential applications,” Yu says. “With additional development, I can imagine these machines being used as probes in hard-to-reach places, from exploring caves to doing search-and-rescue in collapsed buildings.”
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