Principles of artificial intelligence
Principles of artificial intelligence
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Editorial for Journal of Field Robotics—Special Issue on the DARPA Grand Challenge: Editorial
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Learning and prediction of slip from visual information: Research Articles
Journal of Field Robotics - Special Issue on Space Robotics
Visual detection of novel terrain via two-class classification
Proceedings of the 2009 ACM symposium on Applied Computing
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Learning to visually predict terrain properties for planetary rovers
Learning to visually predict terrain properties for planetary rovers
Journal of Field Robotics - Vehicle–Terrain Interaction for Mobile Robots
Online terrain parameter estimation for wheeled mobile robots with application to planetary rovers
IEEE Transactions on Robotics
Vibration-based terrain classification for planetary exploration rovers
IEEE Transactions on Robotics
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In future planetary exploration missions, improvements in autonomous rover mobility have the potential to increase scientific data return by providing safe access to geologically interesting sites that lie in rugged terrain, far from landing areas. To improve rover-based terrain sensing, this paper proposes a self-supervised learning framework that will enable a robotic system to learn to predict mechanical properties of distant terrain, based on measurements of mechanical properties of similar terrain that has been traversed previously. In this framework, a proprioceptive terrain classifier is used to distinguish terrain classes based on features derived from rover–terrain interaction, and labels from this classifier are used to train an exteroceptive (i.e., vision-based) terrain classifier. Once trained, the vision-based classifier is able to recognize similar terrain classes in stereo imagery. This paper presents two distinct proprioceptive classifiers—a novel approach based on optimization of a traction force model and a previously described approach based on wheel vibration—as well as a vision-based terrain classification approach suitable for environments with unexpected appearances. The high accuracy of the self-supervised learning framework and its supporting algorithms is demonstrated using experimental data from a four-wheeled robot in an outdoor Mars-analogue environment. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.