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We propose the Hit-and-Run algorithm for planning and sampling problems in non-convex spaces.
Efficient monte-carlo procedures for generating points uniformly distributed over bounded regions
R.L. Smith · 1984
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Equilibrium configurations of defective crystals
I. Fonseca and G. Parry · 1992
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Random walks in a convex body and an improved volume algorithm
László Lovász and Miklós Simonovits · 1993
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Rapidly-exploring random trees: A new tool for path planning
Steven M. LaValle · 1998
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Hit-and-run mixes fast
László Lovász · 1999
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Randomized kinodynamic planning
Steven M. LaValle and James J. Kuffner Jr · 2001
Cited alongside, same era.
Geometric random walks: A survey
Santosh Vempala · 2005
Cited alongside, same era.
Hit-and-run from a corner
László Lovász and Santosh Vempala · 2006
Cited alongside, same era.
The geometry of logconcave functions and sampling algorithms
László Lovász and Santosh Vempala · 2007
Cited alongside, same era.
A bi-lipschitz continuous, volume preserving map from the unit ball onto a cube
J. A. Griepentrog, W. Höppner, H-C. Kaiser, and J. Rehberg · 2008
Cited alongside, same era.
Extending rapidly-exploring random trees for asymptotically optimal anytime motion planning
Y. Abbasi-Yadkori, J. Modayil, and Cs. Szepesvari · 2010
Later among the works it cites.
Thin partitions: Isoperimetric inequalities and sampling algorithms for some nonconvex families
Karthekeyan Chandrasekaran, Daniel Dadush, and Santosh Vempala · 2010
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Incremental sampling-based algorithms for optimal motion planning, 2010
S. Karaman and E. Frazzoli · 2010
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Sampling-based algorithms for optimal motion planning
Sertac Karaman and Emilio Frazzoli · 2011
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