その場観測と遠隔観測を統合する先読み型惑星探査計画
Non-myopic Planetary Exploration Combining In Situ and Remote Measurements
惑星ローバーが分光計によるその場観測と軌道からの遠隔観測を組み合わせて環境モデルを効率的に改善するため、MDP定式化とモンテカルロ木探索に基づく計画法を提案し、Cupriteのハイパースペクトル画像で評価した。
著者: Suhit Kodgule, Alberto Candela, David Wettergreen
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Remote sensing can provide crucial information for planetary rovers. However, they must validate these orbital observations with in situ measurements. Typically, this involves validating hyperspectral data using a spectrometer on-board the field robot. In order to achieve this, the robot must visit sampling locations that jointly improve a model of the environment while satisfying sampling constraints. However, current planners follow sub-optimal greedy strategies that are not scalable to larger regions. We demonstrate how the problem can be effectively defined in an MDP framework and propose a planning algorithm based on Monte Carlo Tree Search, which is devoid of the common drawbacks of existing planners and also provides superior performance. We evaluate our approach using hyperspectral imagery of a well-studied geologic site in Cuprite, Nevada.
