We study the problem of bipedal robot navigation in complex environments with uncertain and rough terrain. In particular, we consider a scenario in which the robot is expected to reach a desired goal location by traversing an environment with uncertain terrain elevation. Such terrain uncertainties induce not only untraversable regions but also robot motion perturbations. Thus, the problems of terrain mapping and locomotion stability are intertwined. We evaluate three different kernels for Gaussian process (GP) regression to learn the terrain elevation. We also learn the motion deviation resulting from both the terrain as well as the discrepancy between the reduced-order Prismatic Inverted Pendulum Model used for planning and the full-order locomotion dynamics. We propose a hierarchical locomotion-dynamics-aware sampling-based navigation planner. The global navigation planner plans a series of local waypoints to reach the desired goal locations while respecting locomotion stability constraints. Then, a local navigation planner is used to generate a sequence of dynamically feasible footsteps to reach local waypoints. We develop a novel trajectory evaluation metric to minimize motion deviation and maximize information gain of the terrain elevation map. We evaluate the efficacy of our planning framework on Digit bipedal robot simulation in MuJoCo.

翻译：研究了在具有不确定性和崎岖地形的复杂环境中双足机器人导航的问题。具体考虑了一个场景：机器人需穿越地形高程不确定的环境，到达目标位置。此类地形不确定性不仅导致不可通行区域，还会引起机器人运动扰动。因此，地形映射与运动稳定性问题相互交织。我们评估了三种不同的高斯过程回归核函数来学习地形高程。同时，结合地形以及用于规划的简化棱柱倒立摆模型与全身运动动力学之间的差异，学习了运动偏差。提出了一种层次化的运动动力学感知采样导航规划器：全局导航规划器在满足运动稳定性约束的前提下，规划一系列局部航点以到达目标位置；局部导航规划器则生成动态可行的步态序列以到达局部航点。提出了一种新颖的轨迹评估指标，旨在最小化运动偏差并最大化地形高程地图的信息增益。在MuJoCo的Digit双足机器人仿真中验证了规划框架的有效性。