动态环境中的运动基元路径规划：格点上的SIPP
Path Planning with Motion Primitives in Dynamic Environments: SIPP on Lattices
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中文总结 AI 辅助
本文提出一种在状态格点上使用运动基元的SIPP改进算法，通过栅格化动态障碍物时空扫掠体积，在保持可达性的同时生成动力学平滑、物理可执行的路径，优于传统网格规划器。
中文摘要 AI 辅助
在动态环境中进行自主导航是一项关键挑战，尤其是在与其他移动代理共享空间且已知其未来轨迹的情况下。传统的基于网格的规划器能够高效地找到无碰撞路径，但它们在$2^k$连通网格上依赖停止-转向机制，产生的分段线性轨迹对于受微分约束的机器人而言，在动力学上高度次优。本文提出了一种在状态格点上运行的安全区间路径规划（SIPP）的改进版本，利用预计算的、动力学平滑的运动基元。为了高效处理动态环境，我们直接将移动障碍物的时空扫掠体积栅格化到网格上，将网格单元视为空间原子单元，其分辨率通常由固有的定位噪声决定。我们在多种拓扑环境中，对基于格点的方法与$2^k$连通网格规划器进行了全面的比较分析。我们的评估考虑了广泛的性能指标，包括规划时间、路径角度性、累积航向变化（角度-长度比）和弯曲能量。结果表明，虽然格点搜索的扩展状态空间增加了计算开销，但它产生的轨迹具有显著更优的动力学特性。具体而言，我们的方法实现了与高连通网格相当的可达性，同时确保路径平滑、连续且物理上可执行，可直接用于实际部署。
英文摘要
Autonomous navigation in dynamic environments is a critical challenge, particularly when spaces are shared with other mobile agents whose future trajectories are known. While traditional grid-based planners efficiently find collision-free paths, their reliance on stop-and-turn mechanics over $2^k$-connected grids produces piecewise-linear trajectories that are kinodynamically highly sub-optimal for differentially constrained robots. In this paper, we present an adaptation of Safe Interval Path Planning (SIPP) that operates on state lattices, utilizing precomputed, kinodynamically smooth motion primitives. To efficiently handle dynamic environments, we rasterize the spatiotemporal swept volumes of moving obstacles directly onto the grid, treating grid cells as atomic units of space, whose resolution is typically dictated by inherent localization noise. We perform a comprehensive comparative analysis between our lattice-based approach and $2^k$-connected grid planners across diverse topological environments. Our evaluation considers a broad spectrum of performance metrics, including planning time, path angularity, cumulative heading change (angle-over-length), and bending energy. The results demonstrate that while the expanded state space of lattice-based search increases computational overhead, it yields trajectories with significantly superior kinodynamic properties. Specifically, our method achieves a reachability comparable to highly connected grids while ensuring smooth, continuous, and physically executable paths ready for real-world deployment.
