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Path Planning with Hallucinated Worlds

Bart Nabbe, Sanjiv Kumar and Martial Hebert
Conference Paper, Carnegie Mellon University, Proceedings: IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 4, pp. 3123 - 3130, October, 2004

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Abstract

We describe an approach that integrate mid-range sensing into a dynamic path planning algorithm. The algorithm is based on measuring the reduction in path cost that would be caused by taking a sensor reading from candidate locations. The planner uses this measure in order to decide where to take the next sensor reading. Ideally, one would like to evaluate a path based on a map that is as close as possible to the true underlying world. In practice, however, the map is only sparsely populated by data derived from sensor readings. A key component of the approach described in this paper is a mechanism to infer (or “hallucinate”) more complete maps from sparse sensor readings. We show how this hallucination mechanism is integrated with the planner to produce better estimates of the gain in path cost occurred when taking sensor readings. We show results on a real robot as well as a statistical analysis on a large set of randomly generated path planning problems on elevation maps from real terrain.

BibTeX Reference
@conference{Nabbe-2004-9054,
title = {Path Planning with Hallucinated Worlds},
author = {Bart Nabbe and Sanjiv Kumar and Martial Hebert},
booktitle = {Proceedings: IEEE/RSJ International Conference on Intelligent Robots and Systems},
keyword = {Outdoor Navigation, Dynamic planning, data hallucination, mid-range sensing},
sponsor = {Army Research Labs Robotics Collaborative Technology Alliance},
publisher = {IEEE},
grantID = {DAAD 19-01-2-0012},
school = {Robotics Institute , Carnegie Mellon University},
month = {October},
year = {2004},
volume = {4},
pages = {3123 - 3130},
address = {Pittsburgh, PA},
}
2017-09-13T10:43:46+00:00