用矩形走廊分解环境，实现非完整移动机器人的快速高效路径规划。
Fast Motion Planning for Non-Holonomic Mobile Robots via a Rectangular Corridor Representation of Structured Environments
- 将复杂环境分解为重叠矩形走廊，大幅压缩搜索空间。
- 在线生成近似最优的可执行轨迹，计算效率显著提升。
- 适用于大规模复杂结构化场景，适合实际机器人部署。
我们提出一个完整的框架，用于在高度复杂但结构化的环境中对非完整自主移动机器人进行快速运动规划。传统基于栅格的规划器存在可扩展性问题，而许多具有运动学可行性的规划器因搜索空间复杂度高导致计算负担沉重。为克服这些局限，我们的方法引入一种确定性的自由空间分解，构建出由重叠矩形走廊组成的紧凑图结构。该方法在不牺牲路径分辨率的前提下显著减少搜索空间。框架随后通过寻找一系列矩形并使用解析式规划器生成近似时间最优且满足运动学约束的轨迹，实现高效的在线运动规划。我们在大量仿真和真实机器人上验证了该框架的有效性，代码已开源。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。