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This paper focuses on finding robust paths for a robotic system by taking into account the state uncertainty and the probability of collision. We are interested in dealing with intermittent exteroceptive measurements (e.g., collected from vision). We assume that these cues provide reliable measurements that will update a state estimation algorithm wherever they are available. The planner has to manage two tasks: reaching the goal in a minimum time and collecting sufficient measurements to reachdoi:10.1109/lra.2018.2883375 fatcat:uoxdabliwbbxxk6v2ztepo3lnu