Robust Belief Roadmap: Planning Under Intermittent Sensing
This addresses path planning for robots or autonomous systems in environments with unreliable sensors, representing an incremental advance in planning under uncertainty.
The paper tackles planning under uncertainty with intermittent sensor misdetection by deriving an analytical performance bound for state estimators and integrating it into a sample-based path planning algorithm to balance accuracy and robustness, showing benefits in computational results compared to state-of-the-art methods.
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.