Motion planning in off-road environments requires reasoning about both the
geometry and semantics of the scene (e.g., a robot may be able to drive through
soft bushes but not a fallen log). In many recent works, the world is
classified into a finite number of semantic categories that often are not
sufficient to capture the ability (i.e., the speed) with which a robot can
traverse off-road terrain. Instead, this work proposes a new representation of
traversability based exclusively on robot speed that can be learned from data,
offers interpretability and intuitive tuning, and can be easily integrated with
a variety of planning paradigms in the form of a costmap. Specifically, given a
dataset of experienced trajectories, the proposed algorithm learns to predict a
distribution of speeds the robot could achieve, conditioned on the environment
semantics and commanded speed. The learned speed distribution map is converted
into costmaps with a risk-aware cost term based on conditional value at risk
(CVaR). Numerical simulations demonstrate that the proposed risk-aware planning
algorithm leads to faster average time-to-goals compared to a method that only
considers expected behavior, and the planner can be tuned for slightly slower,
but less variable behavior. Furthermore, the approach is integrated into a full
autonomy stack and demonstrated in a high-fidelity Unity environment and is
shown to provide a 30\% improvement in the success rate of navigation.

本文提出了一种基于机器人速度的可穿越性新表示方法，该方法可通过对数据的学习获得，可以轻松地与各种规划范例相结合，并且在成本图的形式下提供风险感知成本项。数值模拟表明，该风险感知规划算法导致平均到达目标的时间更快，并且 Planner 可以调节为稍慢但方差较低的行为。此外，该方法在一个高度真实的 Unity 环境中得到了展示，并表明其在导航成功率方面提供了 30％的改进。