CS 326A: Motion Planning ai.stanford.edu/~latombe/cs326/2007/index.htm Probabilistic Roadmaps: Sampling Strategies

Two Types of Strategies  Where to sample new milestones?  Sampling strategy  Which milestones to connect?  Connection strategy  Goal: Minimize roadmap size to correctly answer motion-planning queries

PRM planners work well because, in practice, free spaces have favorable visibility properties (expansiveness). However, even when visibility properties are favorable, they are usually not uniformly favorable  Non-uniform sampling strategies can significantly speed-up PRM planners small visibility sets small lookout sets good visibility poor visibility

 But how to identify poor visibility regions? ― What is the source of information?  Robot and workspace geometry ― How to exploit it?  Workspace-guided strategies  Filtering strategies  Adaptive strategies  Deformation strategies

 Workspace-guided strategies Identify narrow passages in the workspace and map them into the configuration space  Filtering strategies Sample many configurations, find interesting patterns, and retain only promising configurations  Adaptive strategies Adjust the sampling distribution (  ) on the fly  Deformation strategies Deform the free space, e.g., to widen narrow passages

Multi- vs. Single-Query Roadmaps  Multi-query roadmaps  Pre-compute roadmap  Re-use roadmap for answering queries  The roadmap must cover the free space well  Single-query roadmaps  Compute a roadmap from scratch for each new query  Often roadmap consists of 2 trees rooted at the query configurations