An improved random neighborhood graph approach

As a general framework to determine a collision-free feedback motion strategies, the Random Neighborhood Graph (RNG) approach [19] defines a global navigation function over an approximate representation of the free configuration. In this paper, we improve the RNG approach in several aspects. We present an ANN-accelerated RNG construction a1gorithm to achieve near 1ogarithmic running time in each iteration of the RNG expansion. Two probabilistic termination conditions of the RNG constructibn a1gorithm are presented and analyzed. To help overcome the difficulty of narrow corridors, we also introduce a randoniized perturbation algorithm to enhance the sampling quality. Our implementation illustrates a significant performance improvement.