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Multivalue coding: application to autonomous robots

Published online by Cambridge University Press:  09 March 2009

A. Pruski
A. Pruski
Affiliation:
Laboratoire d'Automatique et d'Electronique Industrielles, University of Metz, Ile du Saulcy, 57045 METZ Cedex 1 (France)
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Summary

The paper describes a free space modeling method by multivalue coding. Each code defines some numerical values representing a set of cells from a grid. The idea consists in using the grid as a Karnaugh board whose rows and columns are binary coded rather than Gray coded. This operating method allows to define, for each code, its grid location and allows numerical comparison in order to locate a code relatively to another. This aspect is helpful for path planning. The free space model is represented by a switching function or a tree to which boolean algebra rules and mathematic operations are applied. We describe an application to mobile robot path planning.

Keywords

ModelingPath planningMobile robotCoding
Type
Article
Information
Robotica , Volume 10 , Issue 2 , March 1992 , pp. 125 - 133
Copyright
Copyright © Cambridge University Press 1992

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References

1.Brooks, R.A., “Solving the find path problem by good representation of free space” IEEE Trans, on Syst. and Cyber. SMC-13, 190–197 (1983).Google Scholar
2.Rueb, K.D., “Structuring free space as an Hypergraph for roving robot path planning and navigation” IEEE Trans, on Pattern Analys. and Mach. Intel. PAMI-9, No. 2, 263–272 (1987).Google ScholarPubMed
3.Chatilla, R., “Système de navigation pour robot mobile autonome: modélisation et processus de décision” Thèse de DDI, Toulouse (1981).Google Scholar
4.Pruski, A. & Boschian, V. “Grid modelisation for autonomous robot” Proc of the IFAC-INCOM'89 Madrid (1989) pp. 227–231.Google Scholar
5.Singh, J.S., “Robot path planning using intersecting convex shapes: analysis and simulation” IEEE J. of Robotics and Automation RA-3, No. 2101–107 (1987).CrossRefGoogle Scholar
6.Samet, H., “Region representation quatrees from binary arrays” Comp. Graphics and Image Processing 13, 88–93 (1980).CrossRefGoogle Scholar
7.Samet, H., “Neighbor finding techniques for images representation by quadtrees” Comp. Graphics and Image Processing 18, 37–57 (1982).CrossRefGoogle Scholar
8.Lozano-Perez, T. & Wesley, M., “An algorithm for planning collision-free paths among polyedral obstacles” Commun. ACM 22, 560–570 (1979).CrossRefGoogle Scholar
9.Sharir, M., “Efficients algorithms for planning purely translational collision-free motion and three dimensions” IEEE Proc. of the IEEE Cond. on Robotics and Automation 1326–1331 (1987).Google Scholar
10.Nilsson, N.J., “Principles of Artificial Intelligence” (Toga publishing 1980).Google Scholar
11.Pruski, A., “Multirobot path planning among moving obstacles using multivalue codes” Proc of the IEEE Conf. on CIMTroy, USA (1990) pp. 588–592.Google Scholar
12.Pruski, A., “Multivalue Coding. Application to Autonomous Robot path planning with rotations” Proc of the IEEE Conf. on Robotics and AutomationSacramento, USA (1991) pp. 694–699.Google Scholar