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 underground lava tube.

中文翻译：

---

步骤：随机越野性能评估和安全越野导航规划

尽管地面机器人自主权已在结构化和受控环境中得到广泛使用，但是在未知和越野地形中的自主权仍然是一个难题。极端，越野和非结构化环境（例如未开发的荒野，山洞和瓦砾）为自主导航带来了独特且具有挑战性的问题。为了解决这些问题，我们提出了一种评估可穿越性并实时规划安全，可行和快速的轨迹的方法。我们的方法被称为STEP（随机可穿越性评估和计划），它基于：1）快速的不确定性感知映射和可穿越性评估，2）使用条件风险值（CVaR）进行尾部风险评估，3）使用基于顺序二次规划（SQP）的模型预测控制（MPC）进行有效的风险和约束感知运动动力学规划。我们在仿真中分析了我们的方法，并验证了其在探索极端地形（包括地下熔岩管）的带轮和有腿机器人平台上的有效性。