the university of north carolina at chapel hill a simple path non-existence algorithm using...

the University of North Carolina at CHAPEL HILL

A Simple Path Non-Existence Algorithm using C-obstacle Query

http://gamma.cs.unc.edu/nopath

Liang-Jun Zhang University of North Carolina - Chapel Hill

Young J. Kim EWHA Womans University, Korea

Dinesh Manocha University of North Carolina - Chapel Hill

the University of North Carolina at CHAPEL HILL

Motion Planning

Initial

Goal

Obstacle

To find a path

Robot

72 DOFCourtesy of P. Isto and M. Saha, 2006

Goal

Initial

Obstacle

To report no path

Robot

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Path Non-existence Problem

ObstacleObstacle

GoalInitial

Robot

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Previous Work

• Exact Motion Planning♦ Exact cell decomposition [Schwartz et

al. 83]♦ Roadmap [Canny 88]♦ Criticality based method [Latombe 99]

♦ Implementation challenges♦ Special and simple objects

• Ladders, sphere, convex shapes

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Previous Work

• Approximation Cell Decomposition♦ [Lozano-Pérez 83], [Zhu et al. 91],

[Latombe 91]♦ Relatively easy to implement

♦ Combinatorial complexity of cell decomposition

♦ Computational issue for labelling the cells during cell decomposition

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Previous Work

• Probabilistic Sampling Based Approach♦ [Kavarki et al. 96] [LaValle et al. 98],

[Choset et al. 05], [LaValle 06]♦ Simple and widely used

♦ May not be terminated when non-path exists

♦ Difficult for narrow passage

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Previous Work

• Path non-existence for special cases♦ Planar section, [Basch et al. 01], [Bretl et al.

04]

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Main Results

• Efficient cell labelling algorithm♦ Workspace-based♦ C-obstacle query using generalized

penetration depth

• Improved cell decomposition algorithm ♦ Simple ♦ Efficient for path non-existence

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Path Non-existence Problem

qgoal

• More difficult than finding a path♦ To check all possible

paths

• Identify a region in C-obstacle ♦ separating qinit and qgoal

qinit

Configuration space

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C-obstacle Query

• Whether a primitive lies entirely in C-obstacle?♦ Usually a cell

• Useful for path non-existence

qgoal

qinit

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full mixed

empty

• [Lozano-Pérez 83]• [Zhu et al. 91]• [Latombe 91]

Cell Decomposition for Path Non-existence

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Cell Decomposition for Path Non-existence

Connectivity Graph Guiding Path

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Cell Decomposition for Path Non-existence

Connectivity graph is not connected

No path!

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Previous Work on C-obstacle Query

• Explicit free space computation♦ Exponential complexity [Sacks 99, Sharir 97]♦ Hard in practice: degeneracy

• Check against every C-surface♦ [Latombe 91, Zhu et al. 91]♦ C-surface enumeration♦ To deal with non-linear C-surfaces

• Workspace distance computation ♦ [Paden 89]♦ Overly conservative

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C-obstacle QueryA Collision Detection Problem

•Does the cell lie inside C-obstacle?

• Do robot and obstacle intersect at all configurations?

Obstacle

WorkspaceConfiguration space

?Robot

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Clearance VS ‘Forbiddance’

• Separation distance

• Clearance

• Penetration Depth

• ‘Forbiddance’

PDd

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q

C-obstacle Query Algorithm

• Penetration Depth♦ Extent of interpenetration

between robot and obstacle

• Motion Bound♦ Extent of the motion that robot

can make.

• Is Penetration Depth > Motion Bound?

Robot A(q)

PD

Cell

Obstacle

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Translational Penetration Depth: PDt

• Minimum translation to separate A, B ♦ [Dobkin 93, Agarwal 00,

Bergen 01, Kim 02]

• PDt: not applicable♦ The robot is allowed to both

translate and rotate. ♦ Undergoing rotation, A may

‘escape’ from B more easily

B

A

A’

A

B

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Generalized Penetration Depth:

PDg

• Take into account translational and rotational motion ♦ [L. Zhang, Y. Kim, G. Varadhan, D. Manocha,

ACM Solid and Physical Modeling 06]♦ Trajectory length ♦ Distance metric Dg

♦ Min/Max operations

Trajectory length A(q0)

A(q1)

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PDg Computation

• Difficult for non-convex objects

• Theorem: for convex objects, PDg = PDt

• Convex/Convex♦ Known efficient PDt algorithms directly applicable♦ [Dobkin 93, Agarwal 00, Bergen 01, Kim 02]

• Non-Convex / Non-Convex♦ A lower bound on PDg based on convex decomposition

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C-obstacle Query

Is Penetration Depth > Motion Bound?

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Motion Bound

• ♦ [Schwarzer, Saha, Latombe

04]

• ♦ Achieved by any diagonal

line segment, e.g. qa,c

bqaq,( , )a bq qMB A

Cell

,( , ) max ( , )a bq qMB A C MB A

bq

cq

Configuration space

qa

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Free Cell Query

• Separation distance describes the clearance• If Separation Distance ≥ Motion Bound

the robot can not intersect with the obstacle♦ The cell lies inside free space

d

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Experimental Results C-obstacle Query Computation

Faces # of A 28 8,452 304

Faces # of B 1,692 336 304

Per C-obstacle

query

1.901 (ms) 6.127 (ms) 4.112 (ms)

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Experimental Results Path Non-existence

• 2D rigid robots with 3-DOF♦ 2 translational DOF and 1 rotational DOF

B1B2

B3

B4

A

A'

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Two-gear Example

no path!

Cells in C-obstacle

Initial

Goal

Roadmap in F

Video 3.356s

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Performance of Two-gear Example

# of C-obstacle queries

30K

# of free cell queries

32K

# of iterations 41

Total timing 3.356s

Free cell queries 0.858s

C-obstacle queries 0.827s

Graph searching 0.466s

Subdivision 1.205s

# of total cells 28K

# of total free cells 2K

# of total c-obstacle cells

12K

# of total mixed cells 14K

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Five-gear Example

Cells in C-obstacle

Initial

Goal

Roadmap in F

6.317sNo path!

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Narrow PassageModified Five-gear Example

Total timing 85s

# of C-obstacle queries

176K

# of total cells 168K

Video

Initial

Goal

roadmap in free space

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2D Puzzle

No path!

7.9s

Narrow passage

15.8s

B1B2

B3

B4

Initial

GoalB1B2

B4

A

A'

VideoRemoved

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Conclusion

• C-obstacle query is essential for deciding path non-existence

• Efficient C-obstacle and free cell queries♦ Workspace-based♦ Using generalized penetration depth and

separation distance computation

• Improved cell decomposition algorithm♦ Simple♦ Efficient for path non-existence

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Limitations

• C-obstacle & free cell queries are conservative

• Can not deal with compliant motion planning

• Current implementation of cell decomposition is limited to 3-DOF robots

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Future Work

• Higher DOF motion planning♦ 6 DOF rigid robot♦ C-obstacle & free cell queries are

applicable♦ Combinatorial complexity of cell

decomposition

• Hybrid planner♦ To combine with sampling based

approach

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Acknowledgements

• Army Research Office, DARPA/REDCOM, NSF, ONR, Intel Corporation

• KRF, STAR program of MOST, Ewha SMBA consortium, ITRC program, Korea

• Mink2D, Tel Aviv University

• GAMMA Group, UNC Chapel Hill

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Thank you!

Any Questions?

http://gamma.cs.unc.edu/nopath

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Min over every path connecting q0 and q1

Max trajectory length for distinct points

Dg(q0, q1) =

Dg Metric in C-space

X

Yθ

q1q0

l1

l2

Motion Paths in C-Space Trajectory length

A(q0)

A(q1)

Dg(q0,q1)

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min({ ( , ) | int( ( )) })ggPD D A B 0q q q

PDg definition

The minimum Dg distance over all possible collision-free configurationsA

B

PDg

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Lower Bound on PDg

1. Convex decomposition 2. Eliminate non-overlapping pairs

3. PDt for overlapping pairs

4. LB(PDg) = Max over all PDts

PDt

PDt

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Performance of Five-gear Example

Total timing 6.317s

# of total cells 39K

# of C-obstacle queries

41K

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Compared with Star-shaped roadmap

• Pros♦ Simpler than the star-shaped test♦ Need not capture the intra-connectivity♦ More likely to be extended for higher DOF

• Cons♦ More conservative