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：非结构化越野环境下的地形可通行性研究，传统上依赖语义分类、资源密集型动力学模型或纯几何方法来预测车辆与地形的相互作用。在低速行驶时这些影响并不显著，但当全尺寸系统以7-10米/秒的运行速度行驶在不平坦地形时，将面临安全关键性挑战。本研究特别关注丘陵、坡道和沟渠等不平坦地形。这些常见的高风险几何结构若处理不当，可能导致车辆失控并对乘客造成严重伤害。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。基于该框架，我们推导出两个基本约束，分别侧重于缓解侧翻和跨越沟渠故障，同时可在基于样本的模型预测控制（MPC）框架中完全并行化实现。此外，我们介绍了规划与控制系统设计，在MPC中实现并行化约束，并利用底层控制器满足激进驾驶需求，无需预先获取环境及其动力学信息。通过真实世界实验和对丘陵与沟渠的穿越测试，我们证明该方法捕捉到了不平坦地形下安全与激进自主驾驶的基本要素。与基于几何的方法相比，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提升高达22%。