让机器人学会在复杂地形中判断何时加速跳跃，提升越野导航效率与安全。
Learning When to Jump for Off-road Navigation
- 提出运动感知的可通行性表示MAT，用速度相关的高斯函数建模地形代价。
- 在线规划中实现毫秒级代价更新，实测路径绕行减少75%。
- 适合需要快速穿越障碍的无人车/机器人系统应用。
低速并不总意味着安全，例如跨越沟壑时，低速易陷住，而高速可控跳跃反而更安全。这需要显式建模复杂运动动态的路径规划，但现有方法多仅基于位置或固定速度规划。为此，我们提出运动感知可通行性（MAT）表示，显式建模地形代价与实际机器人运动的关系。MAT不赋予单个标量可通行性分数，而是将每个地形区域建模为速度相关的高斯函数。在线规划中，将代价计算分为两步：(1) 通过一次前向传播从感知信息预测地形依赖的高斯参数；(2) 通过评估这些函数，高效更新因当前动态推断出的新速度下的地形代价，无需重复推理。我们构建了集成MAT的系统，在多种模拟与真实障碍环境中进行评估。结果表明，MAT实现实时效率，显著提升越野导航性能，路径绕行减少75%，同时在复杂地形中保持安全。
原文摘要 · Abstract (English)
Low speed does not always guarantee safety in off-road driving. For instance, crossing a ditch may be risky at a low speed due to the risk of getting stuck, yet safe at a higher speed with a controlled, accelerated jump. Achieving such behavior requires path planning that explicitly models complex motion dynamics, whereas existing methods often neglect this aspect and plan solely based on positions or a fixed velocity. To address this gap, we introduce Motion-aware Traversability (MAT) representation to explicitly model terrain cost conditioned on actual robot motion. Instead of assigning a single scalar score for traversability, MAT models each terrain region as a Gaussian function of velocity. During online planning, we decompose the terrain cost computation into two stages: (1) predict terrain-dependent Gaussian parameters from perception in a single forward pass, (2) efficiently update terrain costs for new velocities inferred from current dynamics by evaluating these functions without repeated inference. We develop a system that integrates MAT to enable agile off-road navigation and evaluate it in both simulated and real-world environments with various obstacles. Results show that MAT achieves real-time efficiency and enhances the performance of off-road navigation, reducing path detours by 75% while maintaining safety across challenging terrains.
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