Learning for Autonomous Navigation: Advances in Machine Learning for Rough Terrain Mobility

J. Andrew (Drew) Bagnell, David Bradley, David Silver, Boris Sofman, and Anthony (Tony) Stentz
IEEE Robotics & Automation Magazine


Download
  • Adobe portable document format (pdf) (6MB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
Autonomous navigation by a mobile robot through natural, unstructured terrain is one of the premier challenges in field robotics. Tremendous advances in autonomous navigation have been made recently in field robotics. Machine learning has played an increasingly important role in these advances. The Defense Advanced Research Projects Agency (DARPA) UGCV-Perceptor Integration (UPI) program was conceived to take a fresh approach to all aspects of autonomous outdoor mobile robot design, from vehicle design to the design of perception and control systems with the goal of achieving a leap in performance to enable the next generation of robotic applications in commercial, industrial, and military applications. The essential problem addressed by the UPI program is to enable safe autonomous traverse of a robot from Point A to Point B in the least time possible given a series of waypoints in complex, unstructured terrain separated by 0.2-2 km. To accomplish this goal, machine learning techniques were heavily used to provide robust and adaptive performance, while simultaneously reducing the required development and deployment time. This article describes the autonomous system, Crusher, developed for the UPI program and the learning approaches that aided in its successful performance.

Keywords
Machine Learning, Autonomous Navigation

Notes
Associated Center(s) / Consortia: National Robotics Engineering Center
Associated Project(s): Crusher and UGCV PerceptOR Integrated

Text Reference
J. Andrew (Drew) Bagnell, David Bradley, David Silver, Boris Sofman, and Anthony (Tony) Stentz, "Learning for Autonomous Navigation: Advances in Machine Learning for Rough Terrain Mobility," IEEE Robotics & Automation Magazine

BibTeX Reference
@{Bagnell_2010_6942,
   author = "J. Andrew (Drew) Bagnell and David Bradley and David Silver and Boris Sofman and Anthony (Tony) Stentz",
   title = "Learning for Autonomous Navigation: Advances in Machine Learning for Rough Terrain Mobility",
   booktitle = "IEEE Robotics & Automation Magazine",
   pages = "74-84",
   month = "June",
   year = "2010",
   volume = "17",
   number = "2",
}