In robotic applications, the control, and actuation deal with a continuous description of the system and environment, while high-level planning usually works with a discrete description. This paper considers the problem of bridging the low-level control and high-level planning for robotic systems via sensor data. In particular, we propose a discretization algorithm that identifies free polytopes via lidar point cloud data. A transition graph is then constructed where each node corresponds to a free polytope and two nodes are connected with an edge if the two corresponding free polytopes intersect. Furthermore, a distance measure is associated with each edge, which allows for the assessment of quality (or cost) of the transition for high-level planning. For the low-level control, the free polytopes act as a convenient encoding of the environment and allow for the planning of collision-free trajectories that realizes the high-level plan. The results are demonstrated in high-fidelity ROS simulations and experiments with a drone and a Segway.

中文翻译：

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基于激光雷达的移动机器人规划探索和离散化

在机器人应用程序中，控制和致动处理系统和环境的连续描述，而高级计划通常使用离散的描述。本文考虑了通过传感器数据将机器人系统的低层控制与高层规划联系起来的问题。特别是，我们提出了一种离散化算法，该算法可通过激光雷达点云数据识别自由多面体。然后构造一个过渡图，其中每个节点对应一个自由多面体，并且如果两个相应的自由多面体相交，则两个节点与一条边连接。此外，距离度量与每个边缘相关联，这允许评估高级规划的过渡质量（或成本）。对于低层控制，自由的多面体可作为环境的便捷编码，并允许规划实现高层次计划的无碰撞轨迹。结果在无人机和Segway的高保真ROS模拟和实验中得到了证明。