Due to sensor limitations, environments that off-road mobile robots operate in are often only partially observable. As the robots move throughout the environment and towards their goal, the optimal route is continuously revised as the sensors perceive new information. In traditional autonomous navigation architectures, a regional motion planner will consume the environment map and output a trajectory for the local motion planner to use as a reference. Due to the continuous revision of the regional plan guidance as a result of changing map information, the reference trajectories which are passed down to the local planner can differ significantly across sequential planning cycles. This rapidly changing guidance can result in unsafe navigation behavior, often requiring manual safety interventions during autonomous traversals in off-road environments. To remedy this problem, we propose Temporally-Sampled Efficiently Adaptive State Lattices (TSEASL), which is a regional planner arbitration architecture that considers updated and optimized versions of previously generated trajectories against the currently generated trajectory. When tested on a Clearpath Robotics Warthog Unmanned Ground Vehicle as well as real map data collected from the Warthog, results indicate that when running TSEASL, the robot did not require manual interventions in the same locations where the robot was running the baseline planner. Additionally, higher levels of planner stability were recorded with TSEASL over the baseline. The paper concludes with a discussion of further improvements to TSEASL in order to make it more generalizable to various off-road autonomy scenarios.

翻译：由于传感器限制，越野移动机器人运行的环境通常仅为部分可观测。随着机器人在环境中移动并朝向目标前进，当传感器感知到新信息时，最优路径会被持续修正。在传统的自主导航架构中，区域运动规划器会消耗环境地图并输出一条轨迹，供局部运动规划器作为参考使用。由于地图信息变化导致区域规划指引持续更新，传递给局部规划器的参考轨迹在连续规划周期之间可能存在显著差异。这种快速变化的指引可能导致不安全的导航行为，在越野环境的自主穿越过程中常常需要人工安全干预。为解决此问题，我们提出了时序采样高效自适应状态栅格（TSEASL），这是一种区域规划器仲裁架构，该架构将先前生成轨迹的更新优化版本与当前生成轨迹进行综合考量。在Clearpath Robotics Warthog无人地面车辆以及从Warthog采集的真实地图数据上进行测试的结果表明，运行TSEASL时，机器人在基线规划器曾需要人工干预的相同位置未再需要人工干预。此外，与基线方法相比，TSEASL记录了更高的规划器稳定性水平。本文最后讨论了TSEASL的进一步改进方向，以增强其在各类越野自主场景中的泛化能力。