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Carnegie Mellon Robotics Institute

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Abstract

Autonomous navigation in vegetation is challenging because the vegetation often hides the load-bearing surface which is used for evaluating the safety of potential actions. It is difficult to design rules for finding the true ground height in vegetation from forward looking sensor data, so we use an online adaptive method to automatically learn this mapping through experience with the world. This approach has been implemented on an autonomous tractor and has been tested in a farm setting. We describe the system and provide examples of finding obstacles and improving roll predictions in the presence of vegetation. We also show that the system can adapt to new vegetation conditions.

Keywords

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, "Online Adaptive Rough-Terrain Navigation in Vegetation," Proceedings of the IEEE International Conference on Robotics and Automation, April, 2004, pp. 96 - 101.

BibTeX Reference

@inproceedings{Wellington_2004_4669,
   author = "Carl Wellington and Anthony (Tony) Stentz",
   title = "Online Adaptive Rough-Terrain Navigation in Vegetation",
   booktitle = "Proceedings of the IEEE International Conference on Robotics and Automation",
   pages = "96 - 101",
   month = "April",
   year = "2004",
   volume = "1",
}

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