Non-myopic Planetary Exploration Combining In Situ and Remote Measurements
Non-myopic Planetary Exploration Combining In Situ and Remote Measurements
复制标题
原位与遥测相结合的非近视行星探索
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
David S. Wettergreen
中科院分区：
文献类型：
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作者：
Suhit Kodgule;A. Candela;David S. Wettergreen
遥感测量可以提供有关行星表面材料特性的重要信息，但其应用受到空间分辨率的限制，当组成材料以更精细的尺度混合时，通常每像素数十米。因此，轨道观测必须通过地面光谱仪的现场测量来验证。在行星探索中，这意味着漫游车必须访问选定的位置，这些位置共同改进环境模型并满足移动性和采样约束。在这种情况下使用的传统规划方法遵循次优贪婪策略，无法扩展到大面积。我们展示了如何在马尔可夫决策过程框架中有效地定义问题，并提出一种基于蒙特卡罗树搜索的规划算法，该算法高效但没有这些缺点，从而提供卓越的性能。我们使用内华达州赤铜矿一个经过充分研究的地质地点的高光谱图像来评估我们的方法。
Remote sensing measurements can provide crucial information about the material properties of a planetary surface but their application is limited by their spatial resolution, typically tens of meters per pixel, when constituent materials are mixed at much finer scale. Consequently the orbital observations must be validated with in situ measurements from a spectrometer on the ground. In planetary exploration this means that a rover must visit selected locations that jointly improve a model of the environment and satisfy mobility and sampling constraints. Conventional planning methods used in this situation follow sub-optimal greedy strategies that are not scalable to large areas. We show how the problem can be effectively defined in a Markov Decision Process framework and propose a planning algorithm based on Monte Carlo Tree Search, which is efficient but devoid of these drawbacks thereby providing superior performance. We evaluate our approach using hyperspectral imagery of a well-studied geologic site in Cuprite, Nevada.