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Robot navigation algorithms using learned spatial graphs†

Published online by Cambridge University Press:  09 March 2009

S. S. Iyengar ,
C. C. Jorgensen ,
S. V. N. Rao  and
C. R. Weisbin
S. S. Iyengar
Affiliation:
Dept. of Computer Science, Louisiana State Univ., Baton Rouge, LA 70803 (USA).
C. C. Jorgensen
Affiliation:
Engineering Physics and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (USA).
S. V. N. Rao
Affiliation:
Dept. of Computer Science, Louisiana State Univ., Baton Rouge, LA 70803 (USA).
C. R. Weisbin
Affiliation:
Engineering Physics and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (USA).
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Summary

Finding optimal paths for robot navigation in a known terrain has been studied for some time but, in many important situations, a robot would be required to navigate in completely new or partially explored terrain. We propose a method of robot navigation which requires no pre-learned model, makes maximal use of available information, records and synthesizes information from multiple journeys, and contains concepts of learning that allow for continuous transition from local to global path optimality. The model of the terrain consists of a spatial graph and a Voronoi diagram. Using acquired sensor data, polygonal boundaries containing perceived obstacles shrink to approximate the actual obstacles surfaces, free space for transit is correspondingly enlarged, and additional nodes and edges are recorded based on path intersections and stop points. Navigation planning is gradually accelerated with experience since improved global map information minimizes the need for further sensor data acquisition. Our method currently assumes obstacle locations are unchanging, navigation can be successfully conducted using two-dimensional projections, and sensor information is precise.

Information

Type
Article
Information
Robotica , Volume 4 , Issue 2 , April 1986 , pp. 93 - 100
Copyright
Copyright © Cambridge University Press 1986

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