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 ap approach in several aspects. We present an ANN-accelerated RNG construction algorithm to achieve near logarithmic running time in each iteration of the RNG expansion. Two probabilistic termination conditions of the RNG construction algorithm are presented and analyzed. To help overcome the difficulty of narrow corridors, we also introduce a randomized perturbation algorithm to enhance the sampling quality. Our implementation illustrates a significant performance improvement.