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Traversability Analysis and Path Planning for a Planetary Rover

Traversability Analysis and Path Planning for a Planetary Rover A method of analyzing three-dimensional data such as might be produced by stereo vision or a laser range finder in order to plan a path for a vehicle such as a Mars rover is described. In order to produce robust results from data that is sparse and of varying accuracy, the method takes into account the accuracy of each data point, as represented by its covariance matrix. It computes estimates of smoothed and interpolated height, slope, and roughness at equally spaced horizontal intervals, as well as accuracy estimates of these quantities. From this data, a cost function is computed that takes into account both the distance traveled and the probability that each region is traversable. A parallel search algorithm that finds the path of minimum cost also is described. Examples using real data are presented. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Autonomous Robots Springer Journals

Traversability Analysis and Path Planning for a Planetary Rover

Gennery, Donald
Autonomous Robots , Volume 6 (2) – Sep 29, 2004
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References (37)

Publisher
Springer Journals
Copyright
Copyright © 1999 by Kluwer Academic Publishers
Subject
Engineering; Robotics and Automation; Artificial Intelligence (incl. Robotics); Computer Imaging, Vision, Pattern Recognition and Graphics; Control, Robotics, Mechatronics
ISSN
0929-5593
eISSN
1573-7527
DOI
10.1023/A:1008831426966
Publisher site
See Article on Publisher Site

Abstract

A method of analyzing three-dimensional data such as might be produced by stereo vision or a laser range finder in order to plan a path for a vehicle such as a Mars rover is described. In order to produce robust results from data that is sparse and of varying accuracy, the method takes into account the accuracy of each data point, as represented by its covariance matrix. It computes estimates of smoothed and interpolated height, slope, and roughness at equally spaced horizontal intervals, as well as accuracy estimates of these quantities. From this data, a cost function is computed that takes into account both the distance traveled and the probability that each region is traversable. A parallel search algorithm that finds the path of minimum cost also is described. Examples using real data are presented.

Journal

Autonomous Robots – Springer Journals

Published: Sep 29, 2004

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