Quasi-randomized path planning

Micliael S. Branicky; Steven M. LaValle; Kari Olson; Libo Yang

doi:10.1109/ROBOT.2001.932820

Quasi-randomized path planning

Micliael S. Branicky, Steven M. LaValle, Kari Olson, Libo Yang

Research output: Contribution to journal › Article › peer-review

Abstract

We propose the use of quasi-random sampling techniques for path planning in high-dimensional configuration spaces. Following similar trends from related numerical computation fields, we show several advantages offered by these techniques in comparison to random sampling. Our ideas are evaluated in the context of the probabilistic roadmap (PRM) framework. Two quasi-random variants of PRM-based planners are proposed: 1) a classical PRM with quasi-random sampling, and 2) a quasi-random Lazy-PRM. Both have been implemented, and are shown through experiments to offer some performance advantages in comparison to their randomized counterparts.

Original language	English (US)
Pages (from-to)	1481-1487
Number of pages	7
Journal	Proceedings - IEEE International Conference on Robotics and Automation
Volume	2
DOIs	https://doi.org/10.1109/ROBOT.2001.932820
State	Published - 2001
Externally published	Yes

ASJC Scopus subject areas

Software
Control and Systems Engineering
Artificial Intelligence
Electrical and Electronic Engineering

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10.1109/ROBOT.2001.932820

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Quasi-randomized path planning

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