This paper deals with the generation of safe tasks for displacement missions of a nonholonomic mobile robot in a mapped indoor environment. The goal of this study is to plan safe actions (path-following) as well as observations (we call it local maps federation [LMF]), leading the robot to configurations where pertinent features can be sensed, thus attaining best localization relative to its environment. A path-planning method dealing with uncertainties is proposed, where both uncertainties in localization and in control of a nonholonomic mobile robot are managed. Maps uncertainties are handled using the local map concept, which is introduced as a set of the best landmarks used for planning and executing robust motion movements. The safeness of the proposed method is due to the mixing between the planning phase and the navigation phase.