Terrain traversability in unstructured off-road autonomy has traditionally relied on semantic classification, resource-intensive dynamics models, or purely geometry-based methods to predict vehicle-terrain interactions. While inconsequential at low speeds, uneven terrain subjects our full-scale system to safety-critical challenges at operating speeds of 7--10 m/s. This study focuses particularly on uneven terrain such as hills, banks, and ditches. These common high-risk geometries are capable of disabling the vehicle and causing severe passenger injuries if poorly traversed. We introduce a physics-based framework for identifying traversability constraints on terrain dynamics. Using this framework, we derive two fundamental constraints, each with a focus on mitigating rollover and ditch-crossing failures while being fully parallelizable in the sample-based Model Predictive Control (MPC) framework. In addition, we present the design of our planning and control system, which implements our parallelized constraints in MPC and utilizes a low-level controller to meet the demands of our aggressive driving without prior information about the environment and its dynamics. Through real-world experimentation and traversal of hills and ditches, we demonstrate that our approach captures fundamental elements of safe and aggressive autonomy over uneven terrain. Our approach improves upon geometry-based methods by completing comprehensive off-road courses up to 22% faster while maintaining safe operation.

翻译：在非结构化越野自主导航中，地形可通行性传统上依赖于语义分类、资源密集型的动力学模型或纯几何方法来预测车辆与地形的相互作用。虽然在低速行驶时影响甚微，但崎岖地形使我们的全尺寸系统在7-10米/秒的运行速度下面临安全关键挑战。本研究特别关注丘陵、斜坡和沟渠等崎岖地形。这些常见的高风险几何特征若通过不当，可能导致车辆瘫痪并造成乘员严重伤害。我们提出了一种基于物理学的框架，用于识别地形动力学中的可通行性约束。利用该框架，我们推导出两个基本约束条件，每个约束都侧重于减轻侧翻和越沟失效风险，并在基于采样的模型预测控制（MPC）框架中实现完全并行化。此外，我们介绍了规划与控制系统的设计，该系统在MPC中实现了并行化约束，并利用底层控制器满足激进驾驶需求，且无需环境及其动力学的先验信息。通过丘陵与沟渠的实际实验与穿越测试，我们证明该方法能捕捉崎岖地形下安全激进自主驾驶的基本要素。相较于基于几何的方法，我们的方法在保持安全运行的同时，将综合越野路线的完成速度最高提升22%。