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Learning Applied to Ground Robots (LAGR)
Vision:
Current systems for autonomous ground robot navigation typically rely on hand-crafted, hand-tuned algorithms for the tasks of obstacle detection and avoidance. While current systems may work well in open terrain or on roads with no traffic, performance falls short in obstacle-rich environments. In LAGR, algorithms will be created that learn how to navigate based on their own experience and by mimicking human teleoperation. It is expected that systems developed in LAGR will provide a performance breakthrough in navigation through complex terrain.
The overall autonomous performance of a robotic ground vehicle also depends on that vehicle's inherent mobility: the greater the vehicle's inherent mobility, the fewer objects that will act as obstacles. In order to create vehicles with high inherent mobility, DARPA developed the Unmanned Ground Combat Vehicle (UGCV) program. One of the vehicles produced by that program, Spinner, uses its terrain-adaptability and strength to traverse terrain that would stop most other vehicles. In LAGR, learning-based perception and navigation will be combined with the inherent mobility of Spinner to yield an autonomous vehicle with extraordinary capability.
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