This paper describes adaptive path planning, a novel approach to path planning for car-like mobile robots. Instead of creating a new plan from scratch, whenever changes in the environment invalidate the current plan, the adaptive path planner attempts to adapt the old plan to the new situation. The paper proposes an efficient representation for path that is easily amendable to adaptation. Associated with the path planner is a set of repair strategies. These repair strategies are local methods to fix a plan to compensate for object movement in the domain. The repair strategies are specific and have a high probability of being able to fix a plan. An empirical evaluation shows that adaptive path planning is suitable to highly dynamic domains, such as RoboCup. Adaptive path planning reduces the cumulative planning time by a factor of 2:7 compared to Bicchi's planner. At the same time, the quality of the plans generated by the adaptive path planner were similar to those generated by Bicchi's planner.