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Distance-Optimal Navigation in an Unknown Environment without Sensing Distances
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| Venue: | IEEE TRANSACTIONS ON ROBOTICS |
| Citations: | 42 - 16 self |
BibTeX
@MISC{Tovar_distance-optimalnavigation,
author = {Benjamín Tovar and Rafael Murrieta-cid and Steven M. LaValle},
title = { Distance-Optimal Navigation in an Unknown Environment without Sensing Distances},
year = {}
}
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Abstract
This paper considers what can be accomplished using a mobile robot that has limited sensing. For navigation and mapping, the robot has only one sensor, which tracks the directions of depth discontinuities. There are no coordinates, and the robot is given a motion primitive that allows it to move toward discontinuities. The robot is incapable of performing localization or measuring any distances or angles. Nevertheless, when dropped into an unknown planar environment, the robot builds a data structure, called the Gap Navigation Tree, which enables it to navigate optimally in terms of Euclidean distance traveled. In a sense, the robot is able to learn the critical information contained in the classical shortest-path roadmap, although surprisingly it is unable to extract metric information. We prove these results for the case of a point robot placed into a simply connected, piecewise-analytic planar environment. The case of multiply connected environments is also addressed, in which it is shown that further sensing assumptions are needed. Due to the limited sensor given to the robot, globally optimal navigation is impossible; however, our approach achieves locally optimal (within a homotopy class) navigation, which is the best that is theoretically possible under this robot model.
Keyphrases
unknown environment ieee transaction robotics distance-optimal navigation gap navigation tree minimal sensing homotopy class limited sensor motion primitive information space critical information map building shortest path index term visibility robot model mobile robot data structure unknown planar environment point robot piecewise-analytic planar environment classical shortest-path roadmap euclidean distance sensing assumption depth discontinuity metric information optimal navigation
