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Autonomous landing at unprepared sites by a full-scale helicopter

Sebastian Scherer, Lyle J. Chamberlain and Sanjiv Singh
Journal Article, Carnegie Mellon University, Robotics and Autonomous Systems, September, 2012

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Abstract

Helicopters are valuable since they can land at unprepared sites; however, current unmanned helicopters are unable to select or validate landing zones (LZs) and approach paths. For operation in unknown terrain it is necessary to assess the safety of a LZ. In this paper, we describe a lidar-based perception system that enables a full-scale autonomous helicopter to identify and land in previously unmapped terrain with no human input. We describe the problem, real-time algorithms, perception hardware, and results. Our approach has extended the state of the art in terrain assessment by incorporating not only plane fitting, but by also considering factors such as terrain/skid interaction, rotor and tail clearance, wind direction, clear approach/abort paths, and ground paths. In results from urban and natural environments we were able to successfully classify LZs from point cloud maps. We also present results from 8 successful landing experiments with varying ground clutter and approach directions. The helicopter selected its own landing site, approaches, and then proceeds to land. To our knowledge, these experiments were the first demonstration of a full-scale autonomous helicopter that selected its own landing zones and landed.

BibTeX Reference
@article{Scherer-2012-7581,
title = {Autonomous landing at unprepared sites by a full-scale helicopter},
author = {Sebastian Scherer and Lyle J. Chamberlain and Sanjiv Singh},
booktitle = {Robotics and Autonomous Systems},
keyword = {UAV, Rotorcraft, 3D perception, Lidar, Landing zone selection},
notes = {The authorative version of this article is available at http://www.sciencedirect.com/science/article/pii/S0921889012001509},
publisher = {Elsevier},
school = {Robotics Institute , Carnegie Mellon University},
month = {September},
year = {2012},
address = {Pittsburgh, PA},
}
2017-09-13T10:39:41+00:00