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Path Planning Using Lazy PRM

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No preprocessing. Ideally... Planning time should relate to the difficulty of ... Designed to answer many queries; require preprocessing ( the learning phase ) ... – PowerPoint PPT presentation

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Title: Path Planning Using Lazy PRM


1
Path Planning Using Lazy PRM
  • Robert Bohlin, Lydia E. Kavraki
  • ICRA 2000, to appear

2
Single Query Path Planning
  • No preprocessing
  • Ideally...
  • Planning time should relate to the difficulty of
    the task
  • Planning time should relate to the desired
    quality of the returned path
  • The planner should use information from previous
    queries

3
Does PRM work?
  • The classical PRM
  • has successfully solved problems with many-dofs,
    but...
  • Designed to answer many queries require
    preprocessing ( the learning phase )
  • heavily rely on fast collision checking

4
Variations of PRM
  • Distribute nodes close to the boundary
  • Hsu et al (1998) penetration into obstacles
  • OBPRM (Amato) rays from collision points
  • Boor et al (1999) distribute points in pairs
  • Wilmarth et al (1999) retract to medial axis
  • Build trees from both directions
  • Hsu et al (1997) expansive space
  • LaValle Kuffner (1999) attractor

5
A New Principle
  • Why difficult for PRM
  • Too much time spent on planning local paths that
    will not appear in the final path
  • Lazy PRM
  • Minimize collision checks!

6
How Does It Work?
7
Node Enhancement
  • Recall distribute points close to the boundary
  • Strategy pick those edges across the boundary,
    use the midpoints as seeds

Free
Obstacle
Obstacle
8
A Trivial Example
  • 1. Generate nodes randomly

G
S
9
A Trivial Example
  • 2. Build the initial roadmap

G
S
10
A Trivial Example
  • 3. Find a shortest path

G
S
11
A Trivial Example
  • 4. Check collisions

G
S
12
A Trivial Example
  • 5. Node Enhancement

G
S
13
A Trivial Example
  • 6. Find a new shortest path

G
S
14
A Few Details
  • Order of collision checking
  • Node first, edges next
  • End-nodes first
  • Edge checking
  • coarse-to-fine
  • Avoid node clustering
  • use an edge in node enhancement only if both ends
    are not from seeds

15
More Details
  • Completeness
  • half of the new nodes are uniformly distributed
  • Multiple Queries
  • Scaling
  • weighted Euclidean metric

16
Experimental Results
  • Distribution of cost
  • Collision checking 80
  • Map building 18
  • Path searching 2
  • 26 of the collision checks are on the final
    path!!
  • Vs. a naïve PRM a factor of 100-200

17
Discussion
  • Ninit, initial nodes, is a key parameter
  • Robustness ( worst-case performance )
  • Q is Lazy PRM all we need?
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