Learning Rough-Terrain Autonomous Navigation

Autonomous navigation by a mobile robot through natural, unstructured terrain is one of the premier challenges in field robotics. The DARPA UPI program was tasked with advancing the state of the art in robust autonomous performance through challenging and widely varying environments. In order to accomplish this goal, machine learning techniques were heavily utilized to provide robust and adaptive performance, while simultaneously reducing the required development and deployment time. This paper describes the autonomous system, Crusher, developed for the UPI program, and the learning approaches that aided in its successful performance.