For robot control, Stochastic roadmap simulation[1] is inspired by probabilistic roadmap[2] methods (PRM) developed for robot motion planning.
The main idea of these methods is to capture the connectivity of a geometrically complex high-dimensional space by constructing a graph of local paths connecting points randomly sampled from that space.
Each vertex v is a randomly sampled conformation in C. Each (directed) edge from vertex vi to vertex vj carries a weight Pij , which represents the probability that the molecule will move to conformation vj , given that it is currently at vi.
Ensemble properties of molecular motion (e.g., probability of folding (PFold), escape time in ligand-protein binding) is computed efficiently and accurately with stochastic roadmap simulation.
PFold values are computed using the first step analysis of Markov chain theory.