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Overview
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Technical Program
Approach
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Learning Applied to Ground Robots (LAGR)
Approach:
The program will proceed in two eighteen -month phases. In Phase I, learned navigation methods will be developed in a sub-scale test environment using small vehicles equipped with a standard computing and sensor suite. In Phase II, methods developed in Phase I will be ported to the Spinner vehicle, and development of learning methods will continue using the small vehicles, with new results ported to Spinner as they become available.
Progress will be measured quantitatively through a series of competitions conducted at the LAGR Test Facilities (LTF). These monthly competitions will measure the ability of the performer systems to learn from experience.
Developers will send their developed control software in the form of object code to the LTF. There, operators will load the software onto a vehicle functionally identical to the GFE vehicles distributed to developers. Then, the operators will command that vehicle to travel from a start waypoint to a goal waypoint through an obstacle-rich environment, and measure the performance of the system on multiple runs.
It is expected that performance will improve from one run to the next as the performer systems become familiar with the terrain and obstacles on the course. The systems will be able to store information gathered during a run using non-volatile storage on the vehicle. This information will be available so that later runs may profit from the experience gained in earlier runs.
In addition to the Learning from Experience activity, other competitions will measure the ability of the performer systems to learn from examples. These competitions will take place about every six months.
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