1. UOBPRM: A Uniformly Distributed Obstacle-Based PRM
Hsin-Yi (Cindy) Yeh1, Shawna Thomas1, David Eppstein2 and Nancy M. Amato1 1 Parasol Lab, Department of Computer Science and Engineering, Texas A&M University 2 Computer Science Department, University of California, Irvine
UOBPRM generates configurations by finding intersections between a set of uniformly distributed fixed-length segments and C-obstacle surfaces.
UOBPRM nodes are guaranteed to be uniformly distributed on C-obstacle boundaries. Other obstacle-based methods such as OBPRM, Gaussian sampling, and Bridge test sampling are unable to make this claim.
We show that UOBPRM outperforms other obstacle-based sampling methods by producing a more uniform distribution on C-obstacle surfaces and requiring less time to solve a given query.