Robust Belief Roadmap: Planning Under Intermittent Sensing

TL;DR

This paper introduces a method for planning robot paths that accounts for intermittent sensor failures, providing bounds on estimation performance and improving robustness compared to existing approaches.

Contribution

It presents an analytical bound on estimator performance under stochastic sensor misdetection and integrates this into a path planning algorithm for enhanced robustness.

Findings

Bound on estimator performance under sensor misdetection

Sample-based planning algorithm that balances accuracy and robustness

Improved path planning results compared to state-of-the-art methods

Abstract

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.