AI 中文总结
MeshSIPP提出一种利用原语空间束传播与轻量级检查的高效格点规划方法，在动态环境中实现完备且最优的搜索，相比最先进方法提速达3倍。
AI 中文摘要
在动态环境中的自主导航需要计算满足非完整运动约束的时空轨迹。当移动障碍物的轨迹是可预测的或已知时，一种有前景的方法是结合由预计算的可行运动原语构建的状态格点与安全间隔路径规划——一种具有强理论保证的基于搜索的算法。虽然这种方法能生成可行路径，但平滑导航所需的丰富原语集会导致较大的分支因子，当与时间相关的障碍物间隔耦合时，这种代价变得高昂。为此，我们提出了MeshSIPP，一种高效的规划器，通过利用许多原语扫过相同区域因此可以一起验证这一事实，消除了计算瓶颈。MeshSIPP将原语作为空间束传播，使用轻量级包围区间检查对其进行筛选，并将昂贵的精确出发时间搜索推迟到原语到达其终止状态。一种时间感知的剪枝规则还会在搜索早期丢弃冗余的时空分支。我们证明了所得搜索是完备且最优的。在超过6000个基准实例和实时ROS 2模拟上的广泛实验表明，MeshSIPP相比最先进的时空规划器实现了高达3倍的加速。
英文摘要
Autonomous navigation in dynamic environments requires computing spatiotemporal trajectories that satisfy non-holonomic motion constraints. When the trajectories of the moving obstacles are predictable or known, a promising approach is to rely on the combination of state lattices constructed from precomputed feasible motion primitives and Safe Interval Path Planning -- a search-based algorithm with strong theoretical guarantees. While this approach yields feasible paths, the rich primitive sets needed for smooth navigation induce a large branching factor, which becomes costly when coupled with time-dependent obstacle intervals. To this end, we present MeshSIPP, an efficient planner that removes the computational bottleneck by exploiting the fact that many primitives sweep the same regions and can therefore be validated together. MeshSIPP propagates primitives as spatial bundles, screens them with lightweight bounding-interval checks, and defers the expensive exact departure-time search until a primitive reaches its terminal state. A time-aware pruning rule additionally discards redundant space-time branches early in the search. We prove that the resulting search is complete and optimal. Extensive experiments over more than 6,000 benchmark instances and real-time ROS~2 simulations show that MeshSIPP achieves up to a 3$\times$ speedup over state-of-the-art spatiotemporal planners.
