Characterization of planetary soils in past space missions has been a task confined mostly to specific instruments developed to assess the terrain. However, the non-standard techniques introduced by Sojourner Pathfinder and Mars Exploration Rovers (MER), have shown that analytical modeling of the wheel-soil interactions, combined with suspension-related telemetry acquired from the rovers, was sufficient to reveal natural restrictions of Mars terrains and strength properties, without the need of in-situ samplers. The progress on this field established since then is briefly summarized in this paper. Although dozens of approaches for in-situ characterization of planetary grounds with terramechanics have evolved over the years (defined here as inverse terramechanics estimators, or IT), most techniques are achievable in controlled environments but unsuitable for application in planetary rovers. This is because they generally require intensive onboard processing to obtain teal-time estimations like slip, which is not straightforward to perform in real conditions (MER must stop all functions to perform visual odometry). Consequently, beyond previous MER approaches, detection of martian or lunar terrain constants will not be realizable with most proposed empirical methods and available rover hardware. However, novel developments based in machine learning (ML) can quickly change this paradigm. ML-regression models can provide the available measurements (e.g. slip) for IT-approaches to work, even in challenging conditions such as dim light or eclipses. Nevertheless, ML-based methods typically do not include uncertainty and may require to incorporate gaussian process regression models to perform better. Hence we recommend novel research directions for IT to become achievable in practical applications. We further discuss how methods for planetary soil strength detection by IT have grouped over the years into basically three classifications (pure-empirical, learning-based, or combined frameworks), and relate the advantages of each according to the environment.

在过去的太空任务中，对行星土壤进行表征一直是一项主要限于为评估地形而开发的特定仪器的任务。然而，Sojourner Pathfinder 和火星探索漫游者 (MER) 引入的非标准技术表明，轮土相互作用的分析建模，结合从漫游者获得的与悬架相关的遥测，足以揭示火星的自然限制地形和强度属性，无需现场采样器。本文简要总结了此后建立的该领域的进展。尽管多年来使用地球力学对行星地面进行原位表征的数十种方法已经发展（此处定义为逆地球力学估计器，或 IT），大多数技术可以在受控环境中实现，但不适用于行星探测器。这是因为它们通常需要密集的机载处理才能获得滑移等滑行时间估计，这在实际条件下并不容易执行（MER 必须停止所有功能才能执行视觉里程计）。因此，除了以前的 MER 方法之外，使用大多数建议的经验方法和可用的漫游车硬件将无法实现火星或月球地形常数的检测。然而，基于机器学习 (ML) 的新发展可以迅速改变这种范式。ML 回归模型可以为 IT 方法的工作提供可用的测量（例如滑移），即使在具有挑战性的条件下，例如昏暗的光线或日食。尽管如此，基于机器学习的方法通常不包括不确定性，可能需要结合高斯过程回归模型才能更好地执行。因此，我们推荐新的研究方向，使 IT 能够在实际应用中实现。我们进一步讨论了多年来通过 IT 检测行星土壤强度的方法如何基本上分为三类（纯经验、基于学习或组合框架），并根据环境关联每种方法的优势。