Most autonomous navigation systems assume wheeled robots are rigid bodies and their 2D planar workspaces can be divided into free spaces and obstacles. However, recent wheeled mobility research, showing that wheeled platforms have the potential of moving over vertically challenging terrain (e.g., rocky outcroppings, rugged boulders, and fallen tree trunks), invalidate both assumptions. Navigating off-road vehicle chassis with long suspension travel and low tire pressure in places where the boundary between obstacles and free spaces is blurry requires precise 3D modeling of the interaction between the chassis and the terrain, which is complicated by suspension and tire deformation, varying tire-terrain friction, vehicle weight distribution and momentum, etc. In this paper, we present a learning approach to model wheeled mobility, i.e., in terms of vehicle-terrain forward dynamics, and plan feasible, stable, and efficient motion to drive over vertically challenging terrain without rolling over or getting stuck. We present physical experiments on two wheeled robots and show that planning using our learned model can achieve up to 60% improvement in navigation success rate and 46% reduction in unstable chassis roll and pitch angles.

翻译：大多数自主导航系统假设轮式机器人为刚体，且其二维平面工作空间可划分为自由空间和障碍物。然而，近年来的轮式移动性研究表明，轮式平台具备在垂直挑战性地形（例如凸岩、崎岖巨石和倒伏树干）上移动的潜力，这一发现推翻了上述两项假设。在障碍物与自由空间边界模糊的环境中对具有长悬挂行程和低胎压的越野车辆底盘进行导航，需要精确建模底盘与地形的三维相互作用，而悬挂和轮胎形变、轮胎-地形摩擦变化、车辆重量分布及动量等因素使其复杂化。本文提出一种学习方法来建模轮式移动性（即车辆-地形正向动力学），并规划可行、稳定且高效的运动方式，以驱动车辆穿越垂直挑战性地形而避免侧翻或卡滞。我们针对两台轮式机器人开展了物理实验，结果表明，使用所学习模型进行规划，可使导航成功率提升高达60%，同时将底盘不稳定的侧倾和俯仰角降低46%。