让轮式机器人穿越看似无法通过的陡峭地形，提升导航效率与稳定性。
Traverse the Non-Traversable: Estimating Traversability for Wheeled Mobility on Vertically Challenging Terrain
- 基于历史车地交互数据，用数据驱动方法识别看似不可行的垂直挑战地形中的可行路径
- 在真实机器人上使规划性能、效率和稳定性分别提升50%、26.7%和9.2%
- 可直接部署于小型机器人，支持采样与非采样两种路径规划模式
现有可通行性估计方法将非铺装地形分为可通行（如沥青、碎石、草地）与不可通行（如巨石、植被、沟壑）区域，并指导后续规划器在可通行区域生成轨迹。然而，最新研究表明，轮式机器人可穿越垂直挑战性地形（如尺寸与车辆相当的崎岖巨石），而这些区域在现有方法中会被判定为不可通行。为此，本文提出新的「穿越不可通行」（TNT）可通行性估计器，基于历史运动学-动力学车辆-地形交互数据，以数据驱动方式识别看似不可通行的垂直挑战地形中的可行路径。TNT 能高效引导下游含高精度六自由度（6-DoF）动力学模型的采样式规划器，实现小型机器人平台上的实时部署。此外，估计结果还可作为代价地图，用于无需采样的全局与局部路径规划。实验表明，在物理机器人平台上，TNT 将规划性能、效率和稳定性分别提升了50%、26.7%和9.2%。
原文摘要 · Abstract (English)
Most traversability estimation techniques divide off-road terrain into traversable (e.g., pavement, gravel, and grass) and non-traversable (e.g., boulders, vegetation, and ditches) regions and then inform subsequent planners to produce trajectories on the traversable part. However, recent research demonstrated that wheeled robots can traverse vertically challenging terrain (e.g., extremely rugged boulders comparable in size to the vehicles themselves), which unfortunately would be deemed as non-traversable by existing techniques. Motivated by such limitations, this work aims at identifying the traversable from the seemingly non-traversable, vertically challenging terrain based on past kinodynamic vehicle-terrain interactions in a data-driven manner. Our new Traverse the Non-Traversable(TNT) traversability estimator can efficiently guide a down-stream sampling-based planner containing a high-precision 6-DoF kinodynamic model, which becomes deployable onboard a small-scale vehicle. Additionally, the estimated traversability can also be used as a costmap to plan global and local paths without sampling. Our experiment results show that TNT can improve planning performance, efficiency, and stability by 50%, 26.7%, and 9.2% respectively on a physical robot platform.
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