We present a method to generate a robot control strategy that maximizes the\nprobability to accomplish a task. The task is given as a Linear Temporal Logic\n(LTL) formula over a set of properties that can be satisfied at the regions of\na partitioned environment. We assume that the probabilities with which the\nproperties are satisfied at the regions are known, and the robot can determine\nthe truth value of a proposition only at the current region. Motivated by\nseveral results on partitioned-based abstractions, we assume that the motion is\nperformed on a graph. To account for noisy sensors and actuators, we assume\nthat a control action enables several transitions with known probabilities. We\nshow that this problem can be reduced to the problem of generating a control\npolicy for a Markov Decision Process (MDP) such that the probability of\nsatisfying an LTL formula over its states is maximized. We provide a complete\nsolution for the latter problem that builds on existing results from\nprobabilistic model checking. We include an illustrative case study.\n