高效生成任意形状机器人运动轨迹，兼顾速度与避障安全。
Efficient Swept Volume-Based Trajectory Generation for Arbitrary-Shaped Ground Robot Navigation
- 分阶段规划：先生成拓扑路径，再分解为简化子问题
- 运行时间快于顶尖方法1到多个数量级，成功率高
- 适合对实时性要求高的复杂环境移动机器人
在复杂环境中安全导航任意形状的地面机器人仍具挑战。现有考虑机器人物理几何的轨迹规划方法普遍存在计算耗时过长的问题。为此，我们提出一种新的粗到精导航框架，显著提升规划效率。第一阶段采用采样方法生成具有最小膨胀裕度的拓扑路径；第二阶段设计几何感知前端策略，将路径离散化为完整状态运动序列，并将其分解为SE(2)子问题和更简单的R2子问题，供后端优化；第三阶段使用基于SVSDF的优化器为各子问题生成轨迹，并无缝拼接成连续最终运动规划。大量基准测试表明，该方法运行时间比当前先进方法快1至多个数量级，同时保持高成功率并确保连续碰撞规避（CCA）。
原文摘要 · Abstract (English)
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.
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