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.

翻译：本文提出了一套完整的快速运动规划框架，适用于在高度复杂但结构化的环境中运行的非完整自主移动机器人。传统的基于栅格的规划器在可扩展性方面存在局限，而许多考虑运动学可行性的规划器则因其搜索空间复杂度而带来沉重的计算负担。为克服这些限制，我们的方法引入了一种确定性自由空间分解技术，构建出由重叠矩形走廊组成的紧凑图表示。该方法能在不牺牲路径分辨率的前提下，显著缩减搜索空间。该框架随后通过在线运动规划，寻找矩形序列并利用解析规划器生成近似时间最优且运动学可行的轨迹，从而实现了大规模导航的高效解决方案。我们通过大量仿真实验和实体机器人验证了该框架的有效性。相关实现已作为开源软件公开发布。