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This paper studies a distributed path planning problem: how can a sensor network help to navigate a robot to its desired goal in a distributed manner. We consider the case where each sensor node is equipped with sophisticated sensors capable of giving a map for its sensing region. We propose a distributed sampling based planning framework (Distributed PRM), where every sensor node creates a local roadmap in its locally-sensed environment; these local roadmaps are "stitched" together by passing messages among nodes and form a larger implicit roadmap without having a global representation. Based on the implicit roadmap, a feasible path is computed in a distributed manner, and the robot moves along the path by interacting with sensor nodes, each of which gives a portion of the path within the local environment of the node. Preliminary simulations show the proposed framework is able to solve path planning problem with low communication overhead.