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Proceedings Paper

Robot path planning algorithm using fuzzy goal programming
Author(s): Sunil U. Mohandas; Advait M. Mogre; Robert W. McLaren
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Paper Abstract

The robot path planning problem involves finding an acceptable path from the current state to the destination state by defining an obstacle-free path. In the literature, many path planning algorithms which are based on local or global minimization techniques have been discussed. These algorithms assume that the configuration of the work environment is known precisely. However, in reality, a precise description and location of obstacles might be difficult to acquire, represent and incorporate into a planning methodology. Even if a precise description of the work environment is desirable, such a description might not be obtainable because of insufficient knowledge of the environment. Also, noise in the measuring or sensing devices used would contribute to the imprecision in the description. Thus, the path planning problem should take into account an imprecise or "fuzzy” environment. An approach to treat an imprecisely described environment is presented in this paper. The proposed approach models every object in the work environment as a fuzzy obstacle. A 2-dimensional fuzzy obstacle is approximated as a convex polygon or as a circle. The lack of precision in defining the boundary of an obstacle is depicted by a membership function that indicates a degree to which a point in the work-space belongs to the obstacle. The uncertainty in the location of the obstacle is incorporated in the algorithm by assigning a fuzzy weight to the obstacle. A high value of the weight implies more uncertainty in the description and vice versa. In fuzzy goal programing formulation, a decision function as a fuzzy set is constructed which represents an aggregate of the goals of the problem. A path planning algorithm which uses local gradient information of the decision function is presented. Three examples of two-dimensional configuration are given to illustrate the fuzzy goal programming approach.

Paper Details

Date Published: 1 January 1990
PDF: 11 pages
Proc. SPIE 1293, Applications of Artificial Intelligence VIII, (1 January 1990); doi: 10.1117/12.21094
Show Author Affiliations
Sunil U. Mohandas, Univ. of Missouri/Columbia (United States)
Advait M. Mogre, Univ. of Missouri/Columbia (United States)
Robert W. McLaren, Univ. of Missouri/Columbia (United States)

Published in SPIE Proceedings Vol. 1293:
Applications of Artificial Intelligence VIII
Mohan M. Trivedi, Editor(s)

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