Map Learning for Mobile Robotics
This project emphasizes the use of evidence grids - probabilistic
representations of occupancy - in robotic localization and navigation.
Our approach constructs a separate evidence grid for each distinct
place using sonar and/or laser sensors, then retrieves and matches
these grids later during place recognition. Experiments with both
a Nomad 200 and simulated robots suggest this approach is robust
with respect to short-term and long-term changes in the environment.
In more recent work, we have developed Magellan, an architecture for
mobile robotics that embedded these place descriptions in a learned
topological network, which it uses for path planning and execution
monitoring. Magellan also incorporates a module for continuous
localization, which it uses to correct position estimates as it
moves through the environment. Experimental studies with Magellan
in two robotics laboratories - at the Navy AI Center and at Stanford
University - suggested that the same software works well in quite
This work was funded by Grant N00014-94-1-0505 from the Intelligent
Systems Program, Office of Naval Research. The project involved a
collaboration with researchers at the Navy AI Center and, though
the ISLE portion has wound down, they continue to build on the
ideas we developed jointly.
Researchers who have contributed to this project include
Karl Pfleger, and
Mehran Sahami at
ISLE and Stanford, as well as
Bill Adams, and
John Grefenstette at the
Navy AI Center.
The Nomad 200 robot used in our experiments on place learning and
Yamauchi, B., Langley, P., Schultz, A. C., Grefenstette, J., &
Adams, W. (1998).
Magellan: An integrated adaptive architecture for mobile robotics
(Technical Report 98-2). Institute for the Study of Learning and
Expertise, Palo Alto, CA.
Yamauchi, B., & Langley, P. (1997).
Place recognition in dynamic environments.
Journal of Robotic Systems, 14, 107-120.
Langley, P., Pfleger, K., & Sahami, M. (1997).
Lazy acquisition of place knowledge.
Artificial Intelligence Review, 11, 315-342.
Yamauchi, B., & Langley, P. (1996).
Place learning in dynamic real-world environments.
Proceedings of RoboLearn-96: International Workshop for Learning
in Autonomous Robots (pp. 123-129). Key West, FL.
Langley, P., & Pfleger, K. (1995).
Case-based acquisition of place knowledge.
Proceedings of the Twelfth International Conference on Machine Learning
(pp. 244-352). Lake Tahoe, CA: Morgan Kaufmann.
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