Although autonomy has gained widespread usage in structured and controlled environments, robotic autonomy in unknown and off-road terrain remains a difficult problem. Extreme, off-road, and unstructured environments such as undeveloped wilderness, caves, rubble, and other post-disaster sites pose unique and challenging problems for autonomous navigation. Based on our participation in the DARPA Subterranean Challenge, we propose an approach to improve autonomous traversal of robots in subterranean environments that are perceptually degraded and completely unknown through a traversability and planning framework called STEP (Stochastic Traversability Evaluation and Planning). We present 1) rapid uncertainty-aware mapping and traversability evaluation, 2) tail risk assessment using the Conditional Value-at-Risk (CVaR), 3) efficient risk and constraint-aware kinodynamic motion planning using sequential quadratic programming-based (SQP) model predictive control (MPC), 4) fast recovery behaviors to account for unexpected scenarios that may cause failure, and 5) risk-based gait adaptation for quadrupedal robots. We illustrate and validate extensive results from our experiments on wheeled and legged robotic platforms in field studies at the Valentine Cave, CA (cave environment), Kentucky Underground, KY (mine environment), and Louisville Mega Cavern, KY (final competition site for the DARPA Subterranean Challenge with tunnel, urban, and cave environments).

翻译：尽管自主性已在结构化和受控环境中得到广泛应用，但机器人在未知越野地形中的自主导航仍是一个难题。极端、非结构化环境（如未开发荒野、洞穴、碎石堆及其他灾后场景）对自主导航提出了独特且具有挑战性的问题。基于我们在DARPA地下挑战赛中的参与经验，我们提出了一种名为STEP（随机可通行性评估与规划）的可通行性与规划框架，以提升机器人在感知退化且完全未知的地下环境中的自主穿越能力。本文提出：1）快速的不确定性感知地图构建与可通行性评估；2）基于条件风险价值（CVaR）的尾部风险评估；3）基于序列二次规划（SQP）模型预测控制（MPC）的高效风险与约束感知的运动规划；4）应对可能导致失败的突发场景的快速恢复行为；5）四足机器人的风险自适应步态调整。我们通过轮式与足式机器人平台在加利福尼亚州瓦伦丁洞穴（洞穴环境）、肯塔基州地下矿区（矿井环境）及路易斯维尔巨型洞穴（DARPA地下挑战赛决赛场地，包含隧道、城区与洞穴环境）的实地实验，展示并验证了丰富的实验结果。