Distance-Optimal Navigation in an Unknown Environment Without Sensing Distances
| Autor: | Rafael Murrieta-Cid, Steven M. LaValle, Benjamín Tovar |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
business.industry
Monte Carlo localization Robotics Mobile robot Mobile robot navigation Computer Science Applications Computer Science::Robotics Euclidean distance Control and Systems Engineering Robot Cartesian coordinate robot Computer vision Motion planning Artificial intelligence Electrical and Electronic Engineering business Mathematics |
| Zdroj: | IEEE Transactions on Robotics. 23:506-518 |
| ISSN: | 1552-3098 |
| DOI: | 10.1109/tro.2007.898962 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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