  In this paper, we extend the recent body of work on planning under
uncertainty to include the fact that sensors may not provide any measurement
owing to misdetection. This is caused either by adverse environmental
conditions that prevent the sensors from making measurements or by the
fundamental limitations of the sensors. Examples include RF-based ranging
devices that intermittently do not receive the signal from beacons because of
obstacles; the misdetection of features by a camera system in detrimental
lighting conditions; a LIDAR sensor that is pointed at a glass-based material
such as a window, etc.
  The main contribution of this paper is twofold. We first show that it is
possible to obtain an analytical bound on the performance of a state estimator
under sensor misdetection occurring stochastically over time in the
environment. We then show how this bound can be used in a sample-based path
planning algorithm to produce a path that trades off accuracy and robustness.
Computational results demonstrate the benefit of the approach and comparisons
are made with the state of the art in path planning under state uncertainty.

Robotics