断続的センシング下のロバスト信念ロードマップ
Robust Belief Roadmap: Planning Under Intermittent Sensing
センサが時折検出失敗する環境での不確実性下計画のため、状態推定性能の解析的限界を導出し、それをサンプルベース経路計画に組み込んで精度とロバスト性を両立する経路を生成する手法を提案した。
著者: Shaunak D. Bopardikar, Brendan J. Englot, Alberto Speranzon
分類: cs.RO
原文アブストラクト
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.
