  We describe a general probabilistic framework to address a variety of
Frechet-distance optimization problems. Specifically, we are interested in
finding minimal bottleneck-paths in $d$-dimensional Euclidean space between
given start and goal points, namely paths that minimize the maximal value over
a continuous cost map. We present an efficient and simple sampling-based
framework for this problem, which is inspired by, and draws ideas from,
techniques for robot motion planning. We extend the framework to handle not
only standard bottleneck pathfinding, but also the more demanding case, where
the path needs to be monotone in all dimensions. Finally, we provide
experimental results of the framework on several types of problems.
