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In this paper, we extend the recent body of work on planning underuncertainty to include the fact that sensors may not provide any measurementowing to misdetection. This is caused either by adverse environmentalconditions that prevent the sensors from making measurements or by thefundamental limitations of the sensors. Examples include RF-based rangingdevices that intermittently do not receive the signal from beacons because ofobstacles; the misdetection of features by a camera system in detrimentallighting conditions; a LIDAR sensor that is pointed at a glass-based materialsuch as a window, etc.  The main contribution of this paper is twofold. We first show that it ispossible to obtain an analytical bound on the performance of a state estimatorunder sensor misdetection occurring stochastically over time in theenvironment. We then show how this bound can be used in a sample-based pathplanning algorithm to produce a path that trades off accuracy and robustness.Computational results demonstrate the benefit of the approach and comparisonsare made with the state of the art in path planning under state uncertainty.

Robust Belief Roadmap: Planning Under Intermittent Sensing