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：高速越野环境中的自主驾驶具有广泛的应用潜力，但车辆与地形之间复杂的相互作用带来了诸多挑战。在此类环境中，车辆需要能够预测自身运动轨迹，并依据地形高程变化等环境动态主动调整控制策略。为此，我们提出一种基于本体感知与外部感知信息融合的地形感知运动动力学模型学习方法。该模型可对六自由度运动进行可靠预测，甚至能在训练过程中无需真实地面接触力数据的情况下估算接触相互作用。通过设计合适的代价函数——对模型预测中具有不稳定运动、不安全相互作用及高不确定性的采样轨迹施加惩罚——我们得以构建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-控制器组合优于基线方法，能够在无控制失效的情况下实现鲁棒的高速行驶性能。