For the design of space missions in the Moon and planets, analysis of mobility in robots is crucial and poor planning has led to abortion of missions in the past. To mitigate the risk of mission failure, improved algorithms relying intrinsically on fusing visual odometry with other sensory inputs are developed for slip detection and navigation. However, these approaches are significantly expensive computationally and difficult to meet for future space exploration robots. Hence, today the central question in the field is how to develop a novel framework for in situ estimation of rover mobility with available space hardware and low-computational demanding terramechanics predictors. Ranging from pure simulations up to experimentally validated studies, this paper surveys dozens of existing methodologies for detection of vehicle motion performance (wheel forces and torques), surface hazards (slip-sinkage) and other parameters (soil strenght constants) using classical terramechanics maps, and compare them with novel approaches introduced by machine learning, allowing to establish future directions of research towards distributed exteroceptive and proprioceptive sensing for visionless exploration in dynamic environments. To avoid making it challenging to collect all relevant studies expeditiously, we propose a global classification of terramechanics according most common practices in the field, allowing to form an structured framework that condense most works in the domain within three estimator categories (direct/forward or inverse terramechanics, and slip estimators). Likewise, from the experiences collected in previous MER (Mars Exploration Rover) missions, five overlooked problems are documented that will need to be addressed in next generation of planetary vehicles, along three research questions and few hypothesis that will pave the road towards future applications of machine learning-based terramechanics.

对于月球和行星空间任务的设计，对机器人移动性的分析至关重要，而规划不善曾导致任务流产。为了降低任务失败的风险，开发了本质上依赖于融合视觉里程计与其他感官输入的改进算法，用于滑动检测和导航。然而，这些方法在计算上非常昂贵，并且难以满足未来的空间探索机器人。因此，今天该领域的核心问题是如何利用可用的空间硬件和低计算要求的地形力学预测器开发一种新的框架，用于原位估计漫游车的移动性。从纯模拟到经过实验验证的研究，本文调查了使用经典地形力学地图检测车辆运动性能（车轮力和扭矩）、表面危害（滑塌）和其他参数（土壤强度常数）的数十种现有方法，并将它们与机器学习引入的新方法进行比较，允许为动态环境中的无视觉探索建立分布式外感知和本体感知的未来研究方向。为了避免快速收集所有相关研究变得困难，我们根据该领域最常见的实践提出了一个全球地形力学分类，允许形成一个结构化的框架，将领域中的大多数工作浓缩在三个估计器类别（直接/正向或逆向）中地力学和滑移估计器）。同样地，