  Probabilistic sampling methods have become very popular to solve single-shot
path planning problems. Rapidly-exploring Random Trees (RRTs) in particular
have been shown to be efficient in solving high dimensional problems. Even
though several RRT variants have been proposed for dynamic replanning, these
methods only perform well in environments with infrequent changes. This paper
addresses the dynamic path planning problem by combining simple techniques in a
multi-stage probabilistic algorithm. This algorithm uses RRTs for initial
planning and informed local search for navigation. We show that this
combination of simple techniques provides better responses to highly dynamic
environments than the RRT extensions.
