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Carnegie Mellon Robotics Institute

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Abstract

In this paper, we explore the problem of three-dimensional motion planning in highly cluttered and unstructured outdoor environments. Because accurate sensing and modeling of obstacles is notoriously difficult in such environments, we aim to build computational tools that can handle large point data sets (e.g. LADAR data). Using a priori aerial data scans of forested environments, we compute a network of free space bubbles forming safe paths within environments cluttered with tree trunks, branches and dense foliage. The network (roadmap) of paths is used for efficiently planning paths that consider obstacle clearance information. We present experimental results on large point data sets typical of those faced by Unmanned Aerial Vehicles, but also applicable to ground-based robots navigating through forested environments.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Field Robotics Center

Associated Project(s): CTA Robotics

Number of pages: 6

Text Reference

Nicolas Vandapel, James Kuffner, and Omead Amidi, "Planning 3-D Path Networks in Unstructured Environments," Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), April, 2005, pp. 4624 - 4629.

BibTeX Reference

@inproceedings{Vandapel_2005_4962,
   author = "Nicolas Vandapel and James Kuffner and Omead Amidi",
   title = "Planning 3-D Path Networks in Unstructured Environments",
   booktitle = "Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)",
   pages = "4624 - 4629",
   month = "April",
   year = "2005",
}

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