Robust belief roadmap: Planning under uncertain and intermittent sensing

This paper considers the problem of planning a path for an autonomous vehicle from a start to a goal location in presence of sensor intermittency modeled as a stochastic process, in addition to process and measurement noise. The aim is to plan a path that minimizes the localizational uncertainty for the vehicle upon arriving at the goal location. The main contribution of this paper is two-fold. We first show that it is possible to obtain an analytical bound on the performance of a state estimator under sensor misdetection (intermittency) occurring stochastically over time. We then use this bound in a sample-based path planning algorithm to produce a path that trades off accuracy and robustness. This extends 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 or the misdetection of features by a camera system in detrimental lighting conditions. Computational results demonstrate the benefit of the approach and comparisons are made with the state of the art in path planning in belief space.