Locally-optimal navigation in multiply-connected environments without geometric maps

In this paper we present an algorithm to build a sensor-based, dynamic data structure useful for robot navigation in an unknown, multiply-connected planar environment. This data structure offers a robust framework for robot navigation, avoiding the need of a complete geometric map or explicit localization, by building a minimal representation based entirely on critical events in online sensor measurements made by the robot. There are two sensing requirements for the robot: it must detect when it is close to the walls, to perform wall-following reliably, and it must be able to detect discontinuities in depth information. It is also assumed that the robot is able to drop, detect and recover a marker. The navigation paths generated are optimal up to the homotopy class to which the paths belong, even though no distance information is measured.

[1]  Leonidas J. Guibas,et al.  Visibility Queries in Simple Polygons and Applications , 1998, ISAAC.

[2]  Steven M. LaValle,et al.  Optimal navigation and object finding without geometric maps or localization , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[3]  Kenneth Y. Goldberg,et al.  Orienting polygonal parts without sensors , 1993, Algorithmica.

[4]  Leonidas J. Guibas,et al.  The Robot Localization Problem , 1995, SIAM J. Comput..

[5]  Steven M. LaValle,et al.  A pursuit-evasion BUG algorithm , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[6]  Wolfram Burgard,et al.  Robust Monte Carlo localization for mobile robots , 2001, Artif. Intell..

[7]  Ehud Rivlin,et al.  Sensory-based motion planning with global proofs , 1997, IEEE Trans. Robotics Autom..

[8]  Michel Pocchiola,et al.  Minimal Tangent Visibility Graphs , 1996, Comput. Geom..

[9]  Matthew T. Mason,et al.  An exploration of sensorless manipulation , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[10]  Rudolf Fleischer,et al.  Optimal Robot Localization in Trees , 2001, Inf. Comput..

[11]  Gregory Dudek,et al.  Using Local Information in a Non-Local Way for Mapping Graph-Like Worlds , 1993, IJCAI.

[12]  Keiji Nagatani,et al.  Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization , 2001, IEEE Trans. Robotics Autom..

[13]  Gaurav S. Sukhatme,et al.  Incremental online topological map building with a mobile robot , 1999, Optics East.

[14]  Vladimir J. Lumelsky,et al.  Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape , 1987, Algorithmica.

[15]  E. Rimon,et al.  A new range-sensor based globally convergent navigation algorithm for mobile robots , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[16]  Héctor H. González-Baños,et al.  Real-time combinatorial tracking of a target moving unpredictably among obstacles , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[17]  Bruce Randall Donald,et al.  Sensor interpretation and task-directed planning using perceptual equivalence classes , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[18]  Vladimir J. Lumelsky,et al.  Provable strategies for vision-guided exploration in three dimensions , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[19]  Steven M. LaValle,et al.  Visibility-based pursuit-evasion: the case of curved environments , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).