Targeting with PRM

HPRM: a hierarchical PRM

We introduce a hierarchical variant of the probabilistic roadmap method for motion planning. By recursively refining an initially sparse sampling in neighborhoods of the C-obstacle boundary, our algorithm generates a smaller roadmap that is more likely to find narrow passages than uniform sampling. We analyze the failure probability and computation time, relating them to path length, path clear...

Generalizing the Analysis of PRM

This paper presents a novel analysis of the probabilistic roadmap method (PRM) for path planning. We formulate the problem in terms of computing the transitive closure of a relation over a probability space and give a bound in terms of the number of intermediate points for some path and the probability of choosing a point from a certain set. Explicit geometric assumptions are not necessary to c...

Path Planning Using Lazy PRM

Randomized motion planning for car-like robots with C-PRM

In this work, we propose a new approach for motion planning for nonholonomic car-like robots which is based on a Customizable PRM (C-PRM). A major advantage of our approach is that it enables the same roadmap to be efficiently utilized for car-like robots with different turning radii, which need not be known before query time. Our C-PRM-based approach first builds a so-called control roadmap wh...

Generalizing the Analysis of PRM