Efficiently guiding robots to their destinations while avoiding obstacles is a fundamental challenge in robotics. In practical scenarios, robots are often required to operate within predefined safe areas and be able to deviate from their primary paths during emergencies to follow alternative routes to safety centers. Planning with safety zones, however, introduces additional challenges as the planner must ensure that a safety center can be reached from each intermediate point along the primary path. This paper proposes a novel path planner that considers safety zones, each defined by a central point and a radius. Our approach efficiently plans the primary path to the goal while also generating an alternative route to a safety center for each intermediate point. Leveraging sampling techniques, we construct a comprehensive roadmap with navigation routes and identify safe locations meeting the distance requirements for reaching a safety center. Subsequently, we search the safe portion of the roadmap to find an optimal path to the goal and alternative routes to safety centers. The efficiency of our approach is validated through simulated experiments conducted in complex 2D and 3D environments, utilizing both car and blimp robot models.