We present a path planner capable of efficient and real-time handling of known and unknown obstacles in highly dynamic workspaces. Known obstacles are acquired offline and stored in a world model, unknown obstacles are acquired online by one or multiple sensors. This is a typical situation for many applications. The method presented here exploits this distinction by building a static roadmap based on known obstacle information. This enables efficient path planning and real-time performance using bounded lazy evaluation thus reducing the number of costly collision test. The dynamics of the workspace are addressed by invalidation/revalidation of roadmap edges based on sensoric input. Several revalidation strategies are evaluated. The proposed path planner is probabilistically complete and utilizes global environment information to assure goal arrival, if the goal is reachable. Our approach is realized using standard PC hardware with computational requirements allowing real-time performance. Experimental results show the validity of our approach.