This paper presents a learning-based approach to consider the effect of unobservable world states in kinodynamic motion planning in order to enable accurate high-speed off-road navigation on unstructured terrain. Existing kinodynamic motion planners either operate in structured and homogeneous environments and thus do not need to explicitly account for terrain-vehicle interaction, or assume a set of discrete terrain classes. However, when operating on unstructured terrain, especially at high speeds, even small variations in the environment will be magnified and cause inaccurate plan execution. In this paper, to capture the complex kinodynamic model and mathematically unknown world state, we learn a kinodynamic planner in a data-driven manner with onboard inertial observations. Our approach is tested on a physical robot in different indoor and outdoor environments, enables fast and accurate off-road navigation, and outperforms environment-independent alternatives, demonstrating 52.4% to 86.9% improvement in terms of plan execution success rate while traveling at high speeds.

中文翻译：

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学习逆动力学原理，以在非结构化地形上进行精确的高速越野导航

本文提出了一种基于学习的方法，考虑了运动学运动计划中不可观察的世界状态的影响，以便在非结构化地形上实现精确的高速越野导航。现有的运动动力学运动计划器要么在结构化且均质的环境中运行，因此无需明确考虑地形与车辆的交互作用，也无需假设一组离散的地形类别。但是，在非结构化的地形上操作时，尤其是在高速行驶时，即使环境中的微小变化也会被放大，并导致计划执行不准确。在本文中，为了捕获复杂的动力学模型和数学上未知的世界状态，我们以数据驱动的方式通过机载惯性观测学习了动力学设计器。