Using data collected from the Meili-I analog crewed mission hosted on a remote Scottish island during August 2023, we analyze GNSS traverse tracks to assess walking velocity in relation to terrain slope. A series of data sampling tests to derive models using a generalized form of Tobler's Hiking Function indicates these models are only applicable to a similar resolution at which they were derived. Deriving walking velocity at 20-s intervals suggests a linear relationship between walking velocity and slope is useful for grid walking algorithms, but longer sampling intervals (>120 s) indicate a greater sensitivity to slope, likely recording long-period affects of exhaustion from prolonged ascending/descending of slopes. Findings are constrained by the limitations of environmental variables during the mission, including variable weather conditions and increasing familiarity with terrain. Applying calibrated hiking functions to grid walking algorithms (i.e., Dijkstra's algorithm) offers time-optimal paths useful for walk-back contingency planning but is unsuited for planning exploration geology traverses.

中文翻译：

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Tobler 的远足函数在行星勘探数据驱动遍历建模中的应用


利用 2023 年 8 月在苏格兰一个偏远岛屿上托管的 Meili-I 模拟载人任务收集的数据，我们分析了 GNSS 横移轨迹，以评估与地形坡度相关的步行速度。使用 Tobler 的 Hiking Function 的广义形式得出模型的一系列数据采样测试表明，这些模型仅适用于派生它们的相似分辨率。以 20 秒的间隔推导出步行速度表明步行速度和坡度之间的线性关系对于网格步行算法很有用，但较长的采样间隔 （x3E120 s） 表示对坡度的敏感性更高，可能记录了长时间上升/下降斜坡造成的疲惫的长期影响。发现受到任务期间环境变量限制的限制，包括多变的天气条件和对地形的日益熟悉。将校准的远足函数应用于网格行走算法（即 Dijkstra 算法）提供了时间最佳路径，可用于步行应急计划，但不适合规划勘探地质穿越。