High-speed autonomous driving in off-road environments has immense potential for various applications, but it also presents challenges due to the complexity of vehicle-terrain interactions. In such environments, it is crucial for the vehicle to predict its motion and adjust its controls proactively in response to environmental changes, such as variations in terrain elevation. To this end, we propose a method for learning terrain-aware kinodynamic model which is conditioned on both proprioceptive and exteroceptive information. The proposed model generates reliable predictions of 6-degree-of-freedom motion and can even estimate contact interactions without requiring ground truth force data during training. This enables the design of a safe and robust model predictive controller through appropriate cost function design which penalizes sampled trajectories with unstable motion, unsafe interactions, and high levels of uncertainty derived from the model. We demonstrate the effectiveness of our approach through experiments on a simulated off-road track, showing that our proposed model-controller pair outperforms the baseline and ensures robust high-speed driving performance without control failure.

翻译：在非结构化环境中实现高速自主驾驶虽具有广泛的应用潜力，却因车辆与地形相互作用的复杂性而面临诸多挑战。在此类环境中，车辆需预先感知地形高程等环境变化，预测自身运动状态并主动调整控制策略。为此，我们提出一种融合本体感知与外感受信息的地形感知运动动力学模型学习方法。该模型不仅能可靠预测六自由度运动，更能在无需训练过程中依赖真实力学数据的情况下估计接触交互状态。通过设计包含不稳定运动惩罚、不安全接触约束及基于模型不确定度惩罚的代价函数，我们构建了安全鲁棒的模型预测控制器。在模拟越野赛道上的实验表明，所提出的模型-控制器联合框架相较基准方法表现更优，且能在无控制失效的前提下保证鲁棒的高速行驶性能。