Abstract
We present an intelligent path finder which is capable of gaining and augmenting its operational skill in guiding a mobile robot navigating in unexplored environments. Rather than rendering the robot system to acquaintance with the specific environment, the robot is trained to acquire generic knowledge about path planning under various circumstances. The robot learns to determine a best direction of movement by means of interactive instruction in different environment situations. A pattern matching and state space transition scheme of learning is implemented.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brooks, R. A., “Solving the Find-Path Problem by Good Representation of Free-Space”, IEEE Trans. Systems, Man, and Cybernetics, Vol. SMC-13, NO. 3, pp. 190–197.
Chattergy, R., “Some Heuristics for the Navigation of a Robot”, The international Journal of Robotics Research, Vol.4, No.1, Spring 1985, pp. 59–66.
Crowley, J. L., “Motion for an Intelligent Mobile Robot”, IEEE First Conference on Artificial Intelligence Application, Danver, December 1984, pp. 51–56.
Iyengar, S. S., et al, “Learning Navigation Paths for a Robot in Unexplored Terrain”, IEEE Second Conference on Artificial Intelligence Applications, 1985, pp. 148–155.
Khatib, O., “Real-time Obstacle Avoidance for Manipulators and Mobile Robots”, The international Journal of Robotics Research, Vol.5, No.1, Spring 1986, pp. 90–98.
Weisbin, C. R., et al., “Autonomous Mobile Robot Navigation and Learning”, IEEE COMPUTER, June 1989, pp. 29–35.
Zhu, Q., “Self-Learning Expert Systems”, Proceedings of the 1st Annual ESD/SMI Expert System Conference, Dearborn, MI, June 1987, pp. 129–142.
Zhu, Q., “An Interactive Refutation Learning Structure for Skill Acquisition in Knowledge-Based CAD Systems”, Proceedings of International Conference on CAD/CAM Robotics & Factories of the Future, Southfield, MI, August 1988, Vol. 2, pp. 170–174.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1991 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhu, Q., Shi, D., Tang, S. (1991). Knowledge Augmentation Via Interactive Learning in a Path Finder. In: Dwivedi, S.N., Verma, A.K., Sneckenberger, J.E. (eds) CAD/CAM Robotics and Factories of the Future ’90. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-84338-9_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-84338-9_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-84340-2
Online ISBN: 978-3-642-84338-9
eBook Packages: Springer Book Archive