Autonomous off-road navigation requires robots to estimate terrain traversability from onboard sensors and plan motion accordingly. Conventional approaches typically rely on sampling-based planners such as MPPI to generate short-term control actions that aim to minimize traversal time and risk measures derived from the traversability estimates. These planners can react quickly but optimize only over a short look-ahead window, limiting their ability to reason about the full path geometry, which is important for navigating in challenging off-road environments. Moreover, they lack the ability to adjust speed based on the terrain-induced vibrations, which is important for smooth navigation on challenging terrains. In this paper, we introduce TRAIL (Traversability with an Implicit Learned Representation), an off-road navigation framework that leverages an implicit neural representation to model terrain properties as a continuous field that can be queried at arbitrary locations. This representation yields spatial gradients that enable integration with a novel gradient-based trajectory optimization method that adapts the path geometry and speed profile based on terrain traversability.

翻译：自主越野导航要求机器人通过机载传感器估计地形可通行性并据此规划运动。传统方法通常依赖基于采样的规划器（如MPPI）生成短期控制动作，旨在最小化穿越时间及基于可通行性估计推导的风险度量。此类规划器虽能快速响应，但仅能在有限的前瞻窗口内进行优化，限制了其对完整路径几何的推理能力——这在复杂越野环境中至关重要。此外，它们缺乏根据地貌诱发振动调整速度的能力，而这对于挑战性地形的平稳导航具有重要意义。本文提出TRAIL（基于隐式学习表示的可通行性建模框架），该越野导航框架利用隐式神经表示将地形属性建模为连续场，可在任意空间位置进行查询。该表示生成的空间梯度可与新型梯度轨迹优化方法相结合，从而根据地形的可通行性自适应调整路径几何与速度曲线。