Online Adaptive Rough-Terrain Navigation in Vegetation
Conference Paper, Proceedings of (ICRA) International Conference on Robotics and Automation, Vol. 1, pp. 96 - 101, April, 2004
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.
BibTeX
@conference{Wellington-2004-8898,author = {Carl Wellington and Anthony (Tony) Stentz},
title = {Online Adaptive Rough-Terrain Navigation in Vegetation},
booktitle = {Proceedings of (ICRA) International Conference on Robotics and Automation},
year = {2004},
month = {April},
volume = {1},
pages = {96 - 101},
keywords = {rough-terrain, navigation, learning},
}
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.