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

Alexander Gutierrez, Tugrul Galatali, Juan Pablo Gonzalez, Christopher Urmson, and William (Red) L. Whittaker
tech. report CMU-RI-TR-05-54, Robotics Institute, Carnegie Mellon University, December, 2005


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Abstract
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.


Keywords
Preplanning, Red Team, Grand Challenge, planning

Notes
Associated Center(s) / Consortia: Field Robotics Center
Associated Project(s): Autonomous Ground Vehicle Design

Text Reference
Alexander Gutierrez, Tugrul Galatali, Juan Pablo Gonzalez, Christopher Urmson, and William (Red) L. Whittaker, "Preplanning for high performance autonomous traverse of desert terrain exploiting a priori knowledge to optimize speeds and to detail paths," tech. report CMU-RI-TR-05-54, Robotics Institute, Carnegie Mellon University, December, 2005

BibTeX Reference
@techreport{Gutierrez_2005_5282,
   author = "Alexander Gutierrez and Tugrul Galatali and Juan Pablo Gonzalez and Christopher Urmson and William (Red) L. Whittaker",
   title = "Preplanning for high performance autonomous traverse of desert terrain exploiting a priori knowledge to optimize speeds and to detail paths",
   booktitle = "",
   institution = "Robotics Institute",
   month = "December",
   year = "2005",
   number= "CMU-RI-TR-05-54",
   address= "Pittsburgh, PA",
}