Computational Geometric Learning On the Power of Manifold Samples in Exploring Configuration Spaces and the Dimensionality of Narrow Passages

Abstract

We extend our study of Motion Planning via Manifold Samples (MMS), a general algorithmic framework that combines geometric methods for the exact and complete analysis of low-dimensional configuration spaces with sampling-based approaches that are appropriate for higher dimensions. The framework explores the configuration space by taking samples that are low… (More)

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