通过结构化环境的矩形走廊表示实现非完整移动机器人的快速运动规划
Fast Motion Planning for Non-Holonomic Mobile Robots via a Rectangular Corridor Representation of Structured Environments
AI总结：
本研究提出了一种基于矩形走廊表示的高效运动规划框架，用于非完整移动机器人在复杂结构化环境中的快速导航。
摘要：
我们提出了一种完整的框架，用于在高度复杂但结构化的环境中实现非完整自主移动机器人的快速运动规划。传统的网格基规划器在可扩展性方面存在困难，而许多在运动学上可行的规划器由于搜索空间的复杂性而带来显著的计算负担。为克服这些限制，我们的方法引入了一种确定性的自由空间分解，创建了重叠矩形走廊的紧凑图。这种方法能够在不牺牲路径分辨率的情况下显著减少搜索空间。该框架通过查找一系列矩形并使用分析规划器生成近似时间最优且在运动学上可行的轨迹来进行在线运动规划。结果是一种高效的大型导航解决方案。我们通过广泛的仿真和物理机器人验证了该框架。该实现作为开源软件公开可用。
英文摘要：
We present a complete framework for fast motion planning of non-holonomic autonomous mobile robots in highly complex but structured environments. Conventional grid-based planners struggle with scalability, while many kinematically-feasible planners impose a significant computational burden due to their search space complexity. To overcome these limitations, our approach introduces a deterministic free-space decomposition that creates a compact graph of overlapping rectangular corridors. This method enables a significant reduction in the search space, without sacrificing path resolution. The framework then performs online motion planning by finding a sequence of rectangles and generating a near-time-optimal, kinematically-feasible trajectory using an analytical planner. The result is a highly efficient solution for large-scale navigation. We validate our framework through extensive simulations and on a physical robot. The implementation is publicly available as open-source software.
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