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Carnegie Mellon Robotics Institute

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Abstract

Current methods for off-road navigation using vehicle and terrain models to predict future vehicle response are limited by the accuracy of the models they use and can suffer if the world is unknown or if conditions change and the models become inaccurate. In this paper, an adaptive approach is presented that closes the loop around the vehicle predictions. This approach is applied to an autonomous vehicle driving through unknown terrain with varied vegetation. Features are extracted from range points from forward looking sensors. These features are used by a locally weighted learning module to predict the load-bearing surface, which is often hidden by vegetation. The true surface is then found when the vehicle drives over that area, and this feedback is used to improve the model. Results using real data show improved predictions of the load-bearing surface and successful adaptation to changing conditions.

Keywords

field robotics, rough terrain, navigation, learning

Notes

Sponsor: John Deere

Associated Center(s) / Consortia: National Robotics Engineering Center

Associated Project(s): Autonomous Agricultural Spraying and Vehicle Safeguarding

Number of pages: 6

Text Reference

Carl Wellington and Anthony (Tony) Stentz, "Learning Predictions of the Load-Bearing Surface for Autonomous Rough-Terrain Navigation in Vegetation," International Conference on Field and Service Robotics, July, 2003, pp. 49-54.

BibTeX Reference

@inproceedings{Wellington_2003_4540,
   author = "Carl Wellington and Anthony (Tony) Stentz",
   title = "Learning Predictions of the Load-Bearing Surface for Autonomous Rough-Terrain Navigation in Vegetation",
   booktitle = "International Conference on Field and Service Robotics",
   pages = "49-54",
   month = "July",
   year = "2003",
}

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