Efficient Nearest Neighbor Searching for Motion Planning

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Efficient Nearest Neighbor Searching for Motion
Planning

• Anna Atramentov
• Dept. of Computer Science
• Iowa State University
• Ames, IA, USA

• Steven M. LaValle
• Dept. of Computer Science
• University of Illinois
• Urbana, IL, USA

Support provided in part by an NSF CAREER award.
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Motivation
In motion planning the following algorithms rely
heavily on nearest neighbor algorithms

• PRM-based methods
• RRT-based methods

Nearest neighbor searching is a fundamental
problem in many applications

• Statistics
• Pattern recognition
• Machine Learning

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Basic Motion Planning Problem

• Given
• 2D or 3D world
• Geometric models of a robot and obstacles
• Configuration space
• Initial and goal configurations
• Task
• Compute a collision free path that connects
  initial and goal configurations

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Probabilistic roadmap approaches(Kavraki,
Svestka, Latombe, Overmars, 1994)

• The precomputation phase consists of the
  following steps
• Generate vertices in configuration space at
  random
• Connect close vertices
• Return resulting graph

• The query phase
• Connect initial and goal to graph
• Search the graph

Obstacle-Based PRM (Amato, Wu, 1996)
Sensor-based PRM (Yu, Gupta, 1998) Gaussian PRM
(Boor, Overmars, van der Stappen, 1999) Medial
axis PRMs (Wilmarth, Amato, Stiller, 1999
Psiula, Hoff, Lin, Manocha, 2000 Kavraki,
Guibas, 2000) Contact space PRM (Ji, Xiao,
2000) Closed-chain PRMs (LaValle, Yakey,
Kavraki, 1999 Han, Amato 2000) Lazy PRM
(Bohlin, Kavraki, 2000) PRM for changing
environments (Leven, Hutchinson, 2000)
Visibility PRM (Simeon, Laumond, Nissoux, 2000).
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Rapidly-exploring random tree approaches

• GENERATE_RRT(xinit, K, ?t)
• T.init(xinit)
• For k 1 to K do
• xrand ? RANDOM_STATE()
• xnear ? NEAREST_NEIGHBOR(xrand, T)
• u ? SELECT_INTPUT(xrand, xnear)
• xnew ? NEW_STATE(xnear, u, ?t)
• T.add_vertex(xnew)
• T.add_edge(xnear, xnew, u)
• Return T

xrand
xnew
xnear
The result is a tree rooted at xinit
LaValle, 1998 LaValle, Kuffner, 1999, 2000
Frazzoli, Dahleh, Feron, 2000 Toussaint, Basar,
Bullo, 2000 Vallejo, Jones, Amato, 2000 Strady,
Laumond, 2000 Mayeux, Simeon, 2000 Karatas,
Bullo, 2001 Li, Chang, 2001 Kuffner, Nishiwaki,
Kagami, Inaba, Inoue, 2000, 2001 Williams, Kim,
Hofbaur, How, Kennell, Loy, Ragno, Stedl,
Walcott, 2001 Carpin, Pagello, 2002.
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Goals

• Existing nearest neighbor packages

• ANN (U. of Maryland)

• Ranger (SUNY Stony Brook)

Problem They only work for Rn. Configuration
spaces that usually arise in motion planning are
products of R, S1 and projective spaces.
Theoretical results
P. Indyk, R. Motwani, 1998 P. Indyk, 1998, 1999
Problem Difficulty of implementation
Our goal
Design simple and efficient algorithm for finding
nearest neighbor in these topological spaces
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Literature on NN searching

• It is very well studied problem
• Kd-tree approach is very simple and efficient

• T. Cover, P. Hart, 1967
• D. Dobkin, R. Lipton, 1976
• J. Bentley, M. Shamos, 1976
• S. Arya, D. Mount,  1993, 1994
• M. Bern, 1993
• T. Chan,  1997
• J. Kleinberg,  1997
• K. Clarkson, 1988, 1994, 1997
• P. Agarwal, J. Erickson, 1998
• P. Indyk, R. Motwani, 1998
• E. Kushilevitz, R. Ostrovsky, Y. Rabani, 1998
• P. Indyk, 1998, 1999
• A. Borodin, R. Ostrovsky, Y. Rabani,  1999

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Problem Formulation

• Given a d-dimensional manifold, T, represented as
  a polygonal schema, and a set of data points in
  T.
• Preprocess these points so that, for any query
  point q ? T, the nearest data point to q can be
  found quickly.

The manifolds of interest

• Euclidean one-space, represented by (0,1) ? R.
• Circle, represented by 0,1, in which 0 ? 1 by
  identification.
• P3, represented by 0, 13 with antipodal points
  identified.

Examples of 4-sided polygonal schemas
cylinder
torus
projective plane
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Example a torus
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Algorithm presentation

• Overview of the kd-tree algorithm
• Modification of kd-tree algorithm to handle
  topology
• Analysis of the algorithm
• Experimental results

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Kd-trees
The kd-tree is a powerful data structure that is
based on recursively subdividing a set of points
with alternating axis-aligned hyperplanes. The
classical kd-tree uses O(dn lgn) precomputation
time, O(dn) space and answers queries in time
logarithmic in n, but exponential in d.
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Kd-trees. Construction
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Kd-trees. Query
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Algorithm Presentation
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Analysis of the Algorithm
Proposition 1. The algorithm correctly returns
the nearest neighbor.
Proof idea The points of kd-tree not visited by
an algorithm will always be further from the
query point then some point already visited.
Proposition 2. For n points in dimension d, the
construction time is O(dn lgn), the space is
O(dn), and the query time is logarithmic in n,
but exponential in d.
Proof idea This follows directly from the
well-known complexity of the basic kd-tree.
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Experiments
For 50,000 data points 100 queries were made
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ExperimentsPRM method
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ExperimentsRRT method
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ExperimentsRRT method
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Conclusion

• We extended kd-tree to handle topology of the
  configuration space
• We have presented simple and efficient algorithm
• We have developed software for this algorithm
  which will be included in Motion Strategy Library
  (http//msl.cs.uiuc.edu/msl/)

Future Work

• Extension to more efficient kd-trees
• Extension to different topological spaces
• Extension to different metric spaces