Modeling the precise dynamics of off-road vehicles is a complex yet essential task due to the challenging terrain they encounter and the need for optimal performance and safety. Recently, there has been a focus on integrating nominal physics-based models alongside data-driven neural networks using Physics Informed Neural Networks. These approaches often assume the availability of a well-distributed dataset; however, this assumption may not hold due to regions in the physical distribution that are hard to collect, such as high-speed motions and rare terrains. Therefore, we introduce a physics-informed data augmentation methodology called PIAug. We show an example use case of the same by modeling high-speed and aggressive motion predictions, given a dataset with only low-speed data. During the training phase, we leverage the nominal model for generating target domain (medium and high velocity) data using the available source data (low velocity). Subsequently, we employ a physics-inspired loss function with this augmented dataset to incorporate prior knowledge of physics into the neural network. Our methodology results in up to 67% less mean error in trajectory prediction in comparison to a standalone nominal model, especially during aggressive maneuvers at speeds outside the training domain. In real-life navigation experiments, our model succeeds in 4x tighter waypoint tracking constraints than the Kinematic Bicycle Model (KBM) at out-of-domain velocities.

翻译：摘要：精确建模越野车辆的动力学是一项复杂但至关重要的任务，这源于其面临的复杂地形以及对最优性能和安全性的需求。近年来，人们开始关注利用物理信息神经网络将基于标称物理模型的系统与数据驱动的神经网络相结合。这类方法通常假设可以获取分布良好的数据集；然而，由于物理分布中存在难以采集的区域（例如高速运动和罕见地形），这一假设可能不成立。为此，我们提出了一种名为PIAug的物理信息数据增强方法。通过一个使用案例——在仅包含低速数据的数据集上实现高速及剧烈运动预测，我们展示了该方法的应用。在训练阶段，我们利用标称模型基于可用源数据（低速）生成目标域（中高速）数据；随后，采用基于物理的损失函数结合增强数据集，将先验物理知识注入神经网络。实验表明，与单一标称模型相比，我们的方法在轨迹预测中的平均误差降低达67%，尤其在超出训练域速度的剧烈机动中表现显著。在真实导航实验中，我们的模型在域外速度下实现了比运动学自行车模型（KBM）严格4倍的航点跟踪约束。