Predicting the Performance of Randomized Parallel Search: An Application to Robot Motion Planning

Authors:
Daniel J. Challou;Maria Gini;Vipin Kumar;George Karypis
Affiliations:
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, U.S.A.;Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, U.S.A./ e-mail: gini@cs.umn.edu;Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, U.S.A.;Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, U.S.A.
Venue:
Journal of Intelligent and Robotic Systems
Year:
2003

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Abstract

In this paper we discuss methods for predicting the performance of any formulation of randomized parallel search, and propose a new performance prediction method that is based on obtaining an accurate estimate of the k-processor run-time distribution. We show that the k-processor prediction method delivers accurate performance predictions and demonstrate the validity of our analysis on several robot motion planning problems.