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Flying Fast and Low Among Obstacles: Methodology and Experiments

Sebastian Scherer, Sanjiv Singh, Lyle J. Chamberlain and Mike Elgersma
Journal Article, The International Journal of Robotics Research, Vol. 27, No. 5, pp. 549-574, May, 2008

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Safe autonomous flight is essential for widespread acceptance of aircraft that must fly close to the ground. We have developed a method of collision avoidance that can be used in three dimensions in much the same way as autonomous ground vehicles that navigate over unexplored terrain. Safe navigation is accomplished by a combination of online environmental sensing, path planning and collision avoidance. Here we outline our methodology and report results with an autonomous helicopter that operates at low elevations in uncharted environments, some of which are densely populated with obstacles such as buildings, trees and wires. We have recently completed over 700 successful runs in which the helicopter traveled between coarsely specified waypoints separated by hundreds of meters, at speeds of up to 10 m s? at elevations of 5?1 m above ground level. The helicopter safely avoids large objects such as buildings and trees but also wires as thin as 6 mm. We believe this represents the first time an air vehicle has traveled this fast so close to obstacles. The collision avoidance method learns to avoid obstacles by observing the performance of a human operator.

The final, definitive version of this paper has been published in The International Journal of Robotics Research, 27/5, 5/2008 by Sage Publications Ltd, All rights reserved. ?SAGE Publications Ltd, year of publication. It is available at: http://online.sagepub.com/

author = {Sebastian Scherer and Sanjiv Singh and Lyle J. Chamberlain and Mike Elgersma},
title = {Flying Fast and Low Among Obstacles: Methodology and Experiments},
journal = {The International Journal of Robotics Research},
year = {2008},
month = {May},
volume = {27},
number = {5},
pages = {549-574},
keywords = {Aerial robotics, Learning},
} 2018-10-04T11:21:03-04:00