On the use of randomized low-discrepancy sequences in sampling-based motion planning

Authors:
Abraham Sánchez;Maria A. Osorio
Affiliations:
Facultad de Ciencias de la Computación, BUAP, Puebla, Pue., México;Facultad de Ciencias de la Computación, BUAP, Puebla, Pue., México
Venue:
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Year:
2005

Citing 7
Cited 0

Random number generation and quasi-Monte Carlo methods

Random number generation and quasi-Monte Carlo methods
Quasi-Monte Carlo integration

Journal of Computational Physics
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator

ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Randomized query processing in robot path planning

Journal of Computer and System Sciences
OBPRM: an obstacle-based PRM for 3D workspaces

WAFR '98 Proceedings of the third workshop on the algorithmic foundations of robotics on Robotics : the algorithmic perspective: the algorithmic perspective
Randomized single-query motion planning in expansive spaces

Randomized single-query motion planning in expansive spaces
Randomized Halton sequences

Mathematical and Computer Modelling: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper shows the performance of randomized low-discre-pancy sequences compared with others low-discrepancy sequences. We used two motion planning algorithms to test this performance: the expansive planner proposed in [1], [2] and SBL [3] . Previous research already showed that the use of deterministic sampling outperformed PRM approaches [4], [5], [6]. Experimental results show performance advantages when we use randomized Halton and Sobol sequences over Mersenne-Twister and the linear congruential generators used in random sampling.