情報誘導による部分観測連続環境でのロボット最大値探索・サンプリング
Information-Guided Robotic Maximum Seek-and-Sample in Partially Observable Continuous Environments
未知で部分観測な連続環境のグローバル最大値を探索しサンプル採取するMSS問題をPOMDPとして定式化し、情報理論的報酬と連続観測モンテカルロ木探索で解くプランナPLUMESを提案した。
著者: Genevieve Flaspohler, Victoria Preston, Anna P. M. Michel, Yogesh Girdhar, Nicholas Roy
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
We present PLUMES, a planner to localizing and collecting samples at the global maximum of an a priori unknown and partially observable continuous environment. The "maximum-seek-and-sample" (MSS) problem is pervasive in the environmental and earth sciences. Experts want to collect scientifically valuable samples at an environmental maximum (e.g., an oil-spill source), but do not have prior knowledge about the phenomenon's distribution. We formulate the MSS problem as a partially-observable Markov decision process (POMDP) with continuous state and observation spaces, and a sparse reward signal. To solve the MSS POMDP, PLUMES uses an information-theoretic reward heuristic with continous-observation Monte Carlo Tree Search to efficiently localize and sample from the global maximum. In simulation and field experiments, PLUMES collects more scientifically valuable samples than state-of-the-art planners in a diverse set of environments, with various platforms, sensors, and challenging real-world conditions.
