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Learning Applied to Ground Robots (LAGR)
Background:
Most current systems for autonomous ground vehicle navigation perform the following algorithmic sequence: First, a 3D model of the world is created for the space in the vicinity of the vehicle. Stereo cameras or laser rangefinders (LADAR) are usually used for this purpose. Next, pattern recognition algorithms identify particular kinds of obstacles that exist in the 3D model. Then, the 3D model and the identified obstacles are projected onto a 2D map that specifies areas that are either safe or dangerous for the vehicle to traverse. Using this map, a path-planning algorithm determines the best route for the vehicle to follow. Finally, commands are sent to actuators to move the vehicle in the direction specified by the path planner.
Because of the inherent range limitations of both stereo and LADAR, current systems tend to be "near-sighted," and are unable to make good judgments about the terrain beyond the local neighborhood of the vehicle. This near-sightedness often causes the vehicles to get caught in cul-de-sacs that could have been avoided if the vehicle had access to information about the terrain at greater distances. Furthermore, the pattern recognition algorithms tend to be non-adaptive and tuned for particular classes of obstacles. The result is that most current systems do not learn from their own experience, so that they may repeatedly lead a vehicle into the same obstacle, or unnecessarily avoid a class of "traversable obstacles" such as tall weeds.
LAGR will address the shortcomings of current robotic ground vehicle autonomous navigation systems through an emphasis on learned autonomous navigation.
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