path planning in expansive c-spaces d. hsuj.-c. latomber. motwani cs dept., stanford university,...
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![Page 1: Path Planning in Expansive C-Spaces D. HsuJ.-C. LatombeR. Motwani CS Dept., Stanford University, 1997](https://reader036.vdocuments.net/reader036/viewer/2022062516/56649d815503460f94a66dbf/html5/thumbnails/1.jpg)
Path Planning in Expansive C-Path Planning in Expansive C-SpacesSpaces
D. Hsu J.-C. Latombe R. Motwani
CS Dept., Stanford University, 1997
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What we Want: Good Connectivity
For each connected component of the free space, there should beonly one connected component of the roadmap.
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What we Want: Good Coverage
Given a pre-computed roadmap, it should be easy to connect newstart and goal configurations.
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Main Result
If the C-space is expansive, then we can build a roadmapthat has both good connectivity and good coverage.
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Definition - -goodness
A free configuration q is -good if it sees an -fraction of thevolume of the free space F.
F is -good if every free configuration is -good.
qA is 1-good
qB is ½-good
F is ½-good
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Definition - Lookout of a Subset
The -lookout of a subset S of F is the subset of points of S that seea -fraction of the volume of F\S.
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Definition - Expansiveness
A free space F is expansive if every subset S of F has a large lookout.
More formally:The free space F is (, , )-expansive if:1. F is -good2. For every subset S of F, the volume of a -lookout of S is an
-fraction of the volume of S.
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Main Result
If the C-space is expansive, then we can build a roadmapthat has both good connectivity and good coverage.
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Definition: Linking Sequence
Pt+1 is chosen from the lookout of the subset of points seen byp0, p1, …, pt.
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Linking Sequences in Expansive Spaces
Any milestone of a roadmap is likely to have a linking sequence ofarbitrary length t, provided the roadmap is big enough.
For large t, the linking sequence of any milestone spans a large fraction of the volume of F.
Hence, the intersection of two linking sequences is likely to containa milestone of the roadmap.
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Theorem 1: Roadmap Connectivity
The probability that a roadmap fails to achieve good connectivityin an expansive space decreases exponentially with the number of milestones.
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Theorem 2: Roadmap Coverage
The probability that a roadmap fails to achieve good coveragein an expansive space decreases exponentially with the number ofmilestones.
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In Practice…
How to build linking sequences ?
Problem: lookouts cannot be easily computed.
However, we know that lookouts occupy a large fraction of the free space.
Hence, linking sequences can be found by sampling uniformly at random, and by keeping only those points that see a large portion of the free space.
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The New Planner
We grow two trees from qinit and qgoal, respectively.
New nodes are selected by sampling uniformly at random around thealready existing nodes.
We incorporate the nodes that are most likely to see a large portionof the free space.
A path is found when the two trees can be connected.
qinitqgoal
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The Weight Function
We incorporate the nodes that are most likely to see a large portionof the free space.
For each node x of the tree T, w(x) is equal to the number of samplednodes of T that lie in a fixed neighborhood of x.
Selecting nodes with probability 1/w(x) ensures the tree spreads uniformly in the free space.
xw(x)=3
T
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The Expansion Algorithm
Pick a node x from T with probability 1/w(x)Sample K points from a fixed neighborhood of xFor each sampled configuration y, retain y with probability 1/w(y)If:
1. y is retained2. y has no collision3. x and y see each other
Then, place an edge between x and y
x
T
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The Expansion Algorithm
Pick a node x from T with probability 1/w(x)Sample K points from a fixed neighborhood of xFor each sampled configuration y, retain y with probability 1/w(y)If:
1. y is retained2. y has no collision3. x and y see each other
Then, place an edge between x and y
x
T
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The Expansion Algorithm
Pick a node x from T with probability 1/w(x)Sample K points from a fixed neighborhood of xFor each sampled configuration y, retain y with probability 1/w(y)If:
1. y is retained2. y has no collision3. x and y see each other
Then, place an edge between x and y
x
Ty
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The Expansion Algorithm
Pick a node x from T with probability 1/w(x)Sample K points from a fixed neighborhood of xFor each sampled configuration y, retain y with probability 1/w(y)If:
1. y is retained2. y has no collision3. x and y see each other
Then, place an edge between x and y
x
Ty
![Page 20: Path Planning in Expansive C-Spaces D. HsuJ.-C. LatombeR. Motwani CS Dept., Stanford University, 1997](https://reader036.vdocuments.net/reader036/viewer/2022062516/56649d815503460f94a66dbf/html5/thumbnails/20.jpg)
The Connection Algorithm
For every x in Tinit and y in Tgoal such that dist(x,y)<L do:If x and y see each other, then connect x and y
qinitqgoal
Tinit Tgoal
xy
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The Connection Algorithm
For every x in Tinit and y in Tgoal such that dist(x,y)<L do:If x and y see each other, then connect x and y
qinitqgoal
Tinit Tgoal
xy
![Page 22: Path Planning in Expansive C-Spaces D. HsuJ.-C. LatombeR. Motwani CS Dept., Stanford University, 1997](https://reader036.vdocuments.net/reader036/viewer/2022062516/56649d815503460f94a66dbf/html5/thumbnails/22.jpg)
The Connection Algorithm
For every x in Tinit and y in Tgoal such that dist(x,y)<L do:If x and y see each other, then connect x and y
qinitqgoal
Tinit Tgoal
xy
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Example
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Conclusion
If the C-space is expansive, then we can efficiently build a roadmapthat has both good connectivity and good coverage.
Suggested improvements:Find a parametrization of the C-space that maximizes expansivenessApply geometric transforms that increase expansivenessDecompose the free space into expansive components