workspace-based connectivity oracle an adaptive sampling strategy for prm planning hanna kurniawati...
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Workspace-based Connectivity Oracle
An Adaptive Sampling Strategy for PRM Planning
Hanna Kurniawati and David HsuPresented by Nicolas Lee and Stephen Russell
Outline
• Introduction/Motivation
• WCO Planner
• Constructing a component sampler
• Ensemble sampler
• Results
Introduction
• Standard Probabilistic Road Map (PRM)– Two phases: construction and query– Construction creates map, R, that tries to
accurately model connectivity of C– Query tries to connect start/goal locations to R
Motivation
• Performance depends on quality of R– Coverage and connectivity
• Algorithm struggles with narrow passages in C• Other sampling strategies:
– Dynamic: Machine learning/adaptive hybrid– Workspace information: Identifying important regions
in W• e.g. Workspace Importance Sampling (WIS) focuses on
regions with small local feature size
WCO Foundations
• Proposition:
If two configurations q, q’ є C are connected by a path in Fc , then for any point f in a robot, Pf(q) and Pf(q’), the projections of q and q’ in W, are connected by a path in Fw
WCO
• Distinct components of R may in fact lie in the same connected component of Fc
• Examine workspace paths for multiple feature points and construct sampler for each f
• Search for channels in W and adapt distribution to sample more densely in regions covered by these channels
Workspace Connectivity
• Decomposition T of Fw into non-overlapping cells– Create adjacency grid GT of T
• Consider two milestones, m and m’, and projections onto W, Pf(m) є t and Pf(m’) є t’
• Find workspace channel, λ: set of nodes in GT connecting t and t’
• Lf( λ) suggests a region of Fc for sampling
Example
(a) Milestones projected to decomposed workspace
(b) Adjacency graph GT
(c) Channel graph G’
Component Sampler Algorithm1. Given f, sample configuration q based on sampling distribution over
T2. If q is collision free, then3. Insert q into R as new milestone m4. Nm, set of neighbors5. for each m’ є Nm do6. if m є Ri and m’ є Rj, then7. connect if possible8. Project m to W9. Update label sets for affected T10. Delete paths in G’ connecting terminals with same label set11. Let t є T containing Pf(m). Perform breadth-first search and
stop when reaching first terminal t’ ≠ t12. Add path from t→t’ to G’ if they have different label sets13. Update the sampling distribution
Ensemble Sampler Algorithm
1. Initialize pi = 1/K for i = 0, 1, …, K-12. for t = 1, 2, … do3. Pick a component sampler si with probability pi
4. Sample a new configuration q using the component
sampler picked5. If a new milestone m is added to the roadmap R
then6. Update the distribution for each
component sampler si
7. Update the probabilities pi
Probability Update
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• Ensemble sampler performs almost as well as the best component sampler
• Kinematic constraints taken into account through higher probability in overlapping lifted channels
Choosing Feature Points
• Must be representative of the robot
• Use vertices of convex hull and centroid for each rigid link of a robot
Test Configurations
Comparison With Other Samplers
• WCO has better sampling in channel regions without too many samples elsewhere
• In many cases, run time is cut in half compared to the best of the other three samplers
Limitations - 2 Bars Example
Conclusion
• WCO is an adaptive sampling strategy for PRM planning
• Using AHS, combine information from workspace geometry and sampling history
• In trials, WCO outperformed strategies which only use workspace information OR dynamic sampling