Although ground robotic autonomy has gained widespread usage in structured and controlled environments, autonomy in unknown and off-road terrain remains a difficult problem. Extreme, off-road, and unstructured environments such as undeveloped wilderness, caves, and rubble pose unique and challenging problems for autonomous navigation. To tackle these problems we propose an approach for assessing traversability and planning a safe, feasible, and fast trajectory in real-time. Our approach, which we name STEP (Stochastic Traversability Evaluation and Planning), relies on: 1) rapid uncertainty-aware mapping and traversability evaluation, 2) tail risk assessment using the Conditional Value-at-Risk (CVaR), and 3) efficient risk and constraint-aware kinodynamic motion planning using sequential quadratic programming-based (SQP) model predictive control (MPC). We analyze our method in simulation and validate its efficacy on wheeled and legged robotic platforms exploring extreme terrains including an abandoned subway and an underground lava tube.

翻译：虽然地面机器人自主在结构化和受控制的环境中得到了广泛使用,但未知和偏僻地形的自主性仍然是一个困难的问题,极端的、越野的和无结构化的环境,如未开发的荒野、洞穴和碎石,对自主航行构成了独特而具有挑战性的问题。为了解决这些问题,我们提议了一种办法,用以评估可穿越性,并规划一个安全、可行和快速的实时轨道。我们称之为STOECE(可变性评估和规划)的方法依赖于:1)快速的不确定性绘图和可移动性评价;2)利用条件值值-风险(CVaR)和3)利用基于连续的二次二次二次二次四极方案(SQP)模型预测控制(MPC)来进行尾尾端风险评估。我们分析了我们模拟和验证其在探索极端地形(包括废弃的地铁和地下熔岩管)的轮式和腿式机器人平台上的效率的方法。