December 10, 2015

Stanford team develops software to predict and prevent drone collisions

Stanford engineers are developing software to predict and prevent collisions
of unmanned aircraft, including delivery drones, in congested urban airspace.

(December 10, 2015)  When Jeff Bezos unveiled his vision of drones delivering packages to Amazon customers during a 60 Minutes segment in late 2013, it caught many people as science fiction. Scarcely two years later, drones are poised to become a technology for not just delivering packages, but monitoring agriculture, gathering news in urban environments and even conducting search and rescue missions.

But before drone aviation can become pervasive, a new infrastructure must be developed to define low-altitude avenues of flight, regulate traffic in congested areas and prevent collisions.

On this front, the Stanford Intelligent Systems Laboratory (SISL) is part of a broad partnership led by NASA Ames to create an unmanned aerial system traffic management system, or UTM, to manage the expected surge in unmanned flights.

"UTM is meant to fulfill a lot of the functions of air traffic control, but it will be in the cloud and largely automated," said SISL Director Mykel Kochenderfer, an assistant professor of aeronautics and astronautics.

NASA envisions that the UTM system will be able to support the orchestration of a huge number of drone operations without air traffic control operators monitoring each and every vehicle in the air. A key attribute of this system will involve automated conflict avoidance – software that can alert multiple drones when a collision is possible, and calculate the maneuvers necessary to avoid it.

Kochenderfer recently coauthored a new paper with mechanical engineering graduate student Hao Yi Ong in which they detail a conflict-avoidance algorithm that, when implemented within the UTM system, will minimize the threat of low-altitude, unmanned collisions.

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