/UGCV PerceptOR Integrated

UGCV PerceptOR Integrated

Portrait of UGCV PerceptOR Integrated
Heads: John Bares and Anthony (Tony) Stentz
Contact: Anthony (Tony) Stentz
Homepage
Last Project Publication Year: 2010

The UPI (UGCV PerceptOR Integrated) program integrates and enhances the results from UGCV and PerceptOR to increase the speed and autonomy of unmanned ground vehicles operating in complex terrain.

By combining the inherent mobility of Spinner and
<../crusher/index.htm>Crusher with advanced perception techniques including the use of learning and prior terrain data, the UPI program stresses system design across vehicle, sensors and software so that the strengths of one component compensate for the weaknesses of another.

Created as a Future Combat Systems (FCS) technology feed program, test results from the UPI program are advancing efforts on many other autonomous vehicle programs, including the Armed Reconnaissance Vehicle and the
<../ans/index.htm>Autonomous Navigation System.

Displaying 20 Publications
Online Learning Techniques for Improving Robot Navigation in Unfamiliar Domains
Boris Sofman

PhD Thesis, Tech. Report, CMU-RI-TR-10-43, Robotics Institute, Carnegie Mellon University, December, 2010
Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain
David Silver, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Journal Article, International Journal of Robotics Research, Vol. 29, No. 12, pp. 1565 - 1592, October, 2010
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

Magazine Article, IEEE Robotics & Automation Magazine, Vol. 17, No. 2, pp. 74-84, June, 2010
Anytime Online Novelty Detection for Vehicle Safeguarding
Boris Sofman, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Conference Paper, IEEE International Conference on Robotics and Automation, May, 2010
Bandit-Based Online Candidate Selection for Adjustable Autonomy
Boris Sofman, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Conference Paper, 7th International Conferences on Field and Service Robotics, July, 2009
Learning to search: Functional gradient techniques for imitation learning
Nathan Ratliff, David Silver and J. Andrew (Drew) Bagnell

Journal Article, Autonomous Robots, Vol. 27, No. 1, pp. 25-53, July, 2009
Receding Horizon Model-Predictive Control for Mobile Robot Navigation of Intricate Paths
Thomas Howard, Colin Green and Alonzo Kelly

Conference Paper, Proceedings of the 7th International Conferences on Field and Service Robotics, July, 2009
Anytime Online Novelty Detection for Vehicle Safeguarding
Boris Sofman, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Tech. Report, CMU-RI-TR-09-17, Robotics Institute, Carnegie Mellon University, April, 2009
State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments
Thomas Howard, Colin Green, David Ferguson and Alonzo Kelly

Journal Article, Journal of Field Robotics, Vol. 25, No. 7, pp. 325-345, June, 2008
Toward Optimal Sampling in the Space of Paths
Colin Green and Alonzo Kelly

Conference Paper, 13th International Symposium of Robotics Research, November, 2007
State Space Sampling of Feasible Motions for High Performance Mobile Robot Navigation in Highly Constrained Environments
Thomas Howard, Colin Green and Alonzo Kelly

Conference Paper, Proceedings of the 6th International Conferences on Field and Service Robotics, July, 2007
Vegetation Detection for Driving in Complex Environments
David Bradley, Ranjith Unnikrishnan and J. Andrew (Drew) Bagnell

Conference Paper, IEEE International Conference on Robotics and Automation, April, 2007
Boosting Structured Prediction for Imitation Learning
Nathan Ratliff, David Bradley, J. Andrew (Drew) Bagnell and Joel Chestnutt

Conference Paper, Advances in Neural Information Processing Systems 19, January, 2007
A Generative Model of Terrain for Autonomous Navigation in Vegetation
Carl Wellington, Aaron Courville and Anthony (Tony) Stentz

Journal Article, The International Journal of Robotics Research, Vol. 25, No. 12, pp. 1287 - 1304, December, 2006
Improving Robot Navigation Through Self-Supervised Online Learning
Boris Sofman, Ellie Lin Ratliff, J. Andrew (Drew) Bagnell, John Cole, Nicolas Vandapel and Anthony (Tony) Stentz

Journal Article, Journal of Field Robotics, Vol. 23, No. 12, December, 2006
Optimal Sampling In the Space of Paths: Preliminary Results
Colin Green and Alonzo Kelly

Tech. Report, CMU-RI-TR-06-51, Robotics Institute, Carnegie Mellon University, November, 2006
Experimental Analysis of Overhead Data Processing To Support Long Range Navigation
David Silver, Boris Sofman, Nicolas Vandapel, J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz

Conference Paper, IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 2443 - 2450, October, 2006
Improving Robot Navigation Through Self-Supervised Online Learning
Boris Sofman, Ellie Lin Ratliff, J. Andrew (Drew) Bagnell, Nicolas Vandapel and Anthony (Tony) Stentz

Conference Paper, Proceedings of Robotics: Science and Systems, August, 2006
Terrain Classification from Aerial Data to Support Ground Vehicle Navigation
Boris Sofman, J. Andrew (Drew) Bagnell, Anthony (Tony) Stentz and Nicolas Vandapel

Tech. Report, CMU-RI-TR-05-39, Robotics Institute, Carnegie Mellon University, January, 2006

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