Abstract U.S Army’s mission is to develop, integrate, and sustain the right technology solutions for all manned and unmanned ground vehicles, and mobility is a key requirement for all ground vehicles. Mobility focuses on ground vehicles’ capabilities that enable them to be deployable worldwide, operationally mobile in all environments, and protected from symmetrical and asymmetrical threats. In order for military ground vehicles to operate in any combat zone, the planners require a mobility map that gives the maximum predicted speeds on these off-road terrains. In the past, empirical and semi-empirical techniques (Ahlvin and Haley, 1992; Haley et al., 1979) were used to predict vehicle mobility on off-road terrains such as the NATO Reference Mobility Model (NRMM). Because of its empirical nature, the NRMM method cannot be extrapolated to new vehicle designs containing advanced technologies, nor can it be applied to lightweight robotic vehicles. The mobility map is a function of different parameters such as terrain topology and profile, soil type (mud, snow, sand, etc.), vegetation, obstacles, weather conditions, and vehicle type and characteristics. A physics-based method such as the discrete element method (DEM) (Dasch et al., 2016) was identified by the NATO Next Generation NRMM Team as a potential high fidelity method to model the soil. This method allows the capture of the soil deformation as well as its non-linear behavior. Hence it allows the simulation of the vehicle on any off-road terrain and have an accurate mobility map generated. The drawback of the DEM method is the required simulation time. It takes several weeks to generate the mobility map because of the large number of soil particles (millions) even while utilizing high performance computing. One approach to reduce the computational time is to use machine learning algorithms to predict the mobility map. Machine learning (Boutell et al., 2004; Burges, 1998; Barber et al., 1997) can lead to very accurate mobility predictions over a wide range of terrains. Machine learning is divided into two categories: the supervised and the unsupervised learning. Supervised learning requires the training data to be labeled into predetermined classes, while the unsupervised learning does not require the training data to be labeled. Machine learning can help generate mobility maps using trained models created from a minimum number of simulation runs. In this study different supervised machine learning algorithms such as the support vector machine (SVM), the nearest neighbor classifier (k-NN), decision trees, and boosting methods were used to create trained models labeled as 2 classes for the ‘go/no-go’ map, 5 classes for the 5-speed map, and 7 classes for the 7-speed map. The trained models were created from the physics-based simulation runs of a nominal wheeled vehicle traversing on a cohesive soil.

中文翻译：

---

使用机器学习算法高效生成准确的移动地图

摘要 美国陆军的使命是为所有有人和无人地面车辆开发、整合和维持正确的技术解决方案，而机动性是所有地面车辆的关键要求。机动性侧重于地面车辆的能力，使它们能够在全球范围内部署，在所有环境中操作移动，并免受对称和非对称威胁。为了让军用地面车辆能够在任何战区作战，规划人员需要一张机动图，以提供在这些越野地形上的最大预测速度。过去，经验和半经验技术（Ahlvin 和 Haley，1992 年；Haley 等人，1979 年）用于预测越野地形上的车辆机动性，例如 NATO 参考机动性模型 (NRMM)。由于其经验性质，NRMM 方法不能外推到包含先进技术的新车辆设计，也不能应用于轻型机器人车辆。移动性地图是不同参数的函数，例如地形拓扑和剖面、土壤类型（泥、雪、沙等）、植被、障碍物、天气条件以及车辆类型和特征。北约下一代 NRMM 团队确定了一种基于物理的方法，例如离散元法 (DEM)（Dasch 等人，2016 年），作为一种潜在的高保真土壤建模方法。该方法允许捕获土壤变形及其非线性行为。因此，它允许在任何越野地形上模拟车辆，并生成准确的移动地图。DEM 方法的缺点是所需的模拟时间。即使在使用高性能计算的情况下，由于大量的土壤颗粒（数百万个），生成迁移率图也需要数周时间。减少计算时间的一种方法是使用机器学习算法来预测移动地图。机器学习（Boutell 等人，2004 年；Burges，1998 年；Barber 等人，1997 年）可以在各种地形上进行非常准确的移动性预测。机器学习分为两类：有监督学习和无监督学习。监督学习需要将训练数据标记为预定的类别，而无监督学习不需要对训练数据进行标记。机器学习可以帮助使用由最少数量的模拟运行创建的训练模型生成移动地图。在本研究中，使用不同的监督机器学习算法，例如支持向量机 (SVM)、最近邻分类器 (k-NN)、决策树和提升方法来创建标记为 2 类的训练模型，用于“go/no” -go' 地图，5 个等级用于 5 速地图，7 个等级用于 7 速地图。训练有素的模型是根据在粘性土壤上行驶的标称轮式车辆的基于物理学的模拟运行创建的。