  An asymptotically optimal sampling-based planner employs sampling to solve
robot motion planning problems and returns paths with a cost that converges to
the optimal solution cost, as the number of samples approaches infinity. This
comprehensive article covers the theoretical characteristics of asymptotic
optimality of motion planning algorithms, and traces its origins, analysis
models, practical performance, extensions, and applications.
