This paper presents a robot solution that allows to automatically reach a set of goals attributed to a robot. The challenge is to design autonomous robots assigned to perform missions without a predefined plan. We address the stochastic salesman problem where the goal is to visit a set of points of interest. A stochastic Road-Map is defined as a topological representation of an unstructured environment with uncertainty on the path achievement. The Road-Map allows us to split deliberation and reactive control. The proposed decision making uses a computation of Markov Decision Processes (MDPs) in order to plan all the reactive tasks to perform while there are goals not yet reached. Finally, from a brief explanation on how the approach could be extend to multi-robot missions, experiments in real conditions permit to evaluate the proposed architecture for multi-robot stochastic salesmen missions.
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