Navigating an arbitrary-shaped ground robot safely in cluttered environments remains a challenging problem. The existing trajectory planners that account for the robot's physical geometry severely suffer from the intractable runtime. To achieve both computational efficiency and Continuous Collision Avoidance (CCA) of arbitrary-shaped ground robot planning, we proposed a novel coarse-to-fine navigation framework that significantly accelerates planning. In the first stage, a sampling-based method selectively generates distinct topological paths that guarantee a minimum inflated margin. In the second stage, a geometry-aware front-end strategy is designed to discretize these topologies into full-state robot motion sequences while concurrently partitioning the paths into SE(2) sub-problems and simpler R2 sub-problems for back-end optimization. In the final stage, an SVSDF-based optimizer generates trajectories tailored to these sub-problems and seamlessly splices them into a continuous final motion plan. Extensive benchmark comparisons show that the proposed method is one to several orders of magnitude faster than the cutting-edge methods in runtime while maintaining a high planning success rate and ensuring CCA.

翻译：在杂乱环境中安全导航任意形状地面机器人仍是一个具有挑战性的问题。现有考虑机器人物理几何形状的轨迹规划器普遍面临计算时间难以处理的困境。为实现任意形状地面机器人规划的计算效率与连续避障（CCA），本文提出了一种新颖的由粗到精的导航框架，可显著加速规划过程。在第一阶段，基于采样的方法选择性地生成具有最小膨胀裕度的不同拓扑路径。在第二阶段，设计了一种几何感知的前端策略，将这些拓扑结构离散化为全状态机器人运动序列，同时将路径分割为SE(2)子问题与更简单的R2子问题以供后端优化。在最后阶段，基于SVSDF的优化器针对这些子问题生成定制化轨迹，并将其无缝拼接为连续的最终运动规划。大量基准测试表明，所提方法在运行时间上比前沿方法快一至数个数量级，同时保持较高的规划成功率并确保连续避障。