  This paper addresses local path re-planning for $n$-dimensional systems by
introducing an informed sampling scheme and cost function to achieve collision
avoidance with minimum deviation from an (optimal) nominal path. The proposed
informed subset consists of the union of ellipsoids along the specified nominal
path, such that the subset efficiently encapsulates all points along the
nominal path. The cost function penalizes large deviations from the nominal
path, thereby ensuring current safety in the face of potential collisions while
retaining most of the overall efficiency of the nominal path. The proposed
method is demonstrated on scenarios related to the navigation of autonomous
marine crafts.
