Carnegie Mellon University
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Preplanning for high performance autonomous traverse of desert terrain exploiting a priori knowledge to optimize speeds and to detail paths

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journal contribution
posted on 2005-12-01, 00:00 authored by Alexander Gutierrez, Tugrul Galatali, Juan Pablo Gonzalez, Chris Urmson, William Whittaker

Good human drivers adjust radii, favor lanes and inherently set speeds while racing. They gracefully enter and exit turns, and "read the terrain" or use foreknowledge of the course to slow down for harsh terrain features. Robots do not yet do this without the benefit of preplanning. This paper describes technologies and methodologies for preplanning including: path detailing, speed setting, terrain knowledge, and verification.

The result of preplanning is the generation of two high performance, successful routes for two autonomous robots in the 2005 Grand Challenge traverse of 132 miles in about 7 hours.


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