We study a path planning problem where the possible move actions are represented as a finite set of motion primitives aligned with the grid representation of the environment. That is, each primitive corresponds to a short kinodynamically-feasible motion of an agent and is represented as a sequence of the swept cells of a grid. Typically heuristic search, i.e. A*, is conducted over the lattice induced by these primitives (lattice-based planning) to find a path. However due to the large branching factor such search may be inefficient in practice. To this end we suggest a novel technique rooted in the idea of searching over the grid cells (as in vanilla A*) simultaneously fitting the possible sequences of the motion primitives into these cells. The resultant algorithm, MeshA*, provably preserves the guarantees on completeness and optimality, on the one hand, and is shown to notably outperform conventional lattice-based planning (x1.5 decrease in the runtime), on the other hand. Moreover, we suggest an additional pruning technique that additionally decreases the search space of MeshA*. The resultant planner is combined with the regular A* to retain completeness and is shown to further increase the search performance at the cost of negligible decrease of the solution quality.

翻译：本研究探讨一种路径规划问题，其中可能的移动动作由一组与栅格化环境表示对齐的有限运动基元集合表示。每个基元对应智能体的一段短时运动学可行轨迹，并表示为栅格中被扫掠单元的序列。传统方法通常在这些基元诱导的格点结构上进行启发式搜索（即A*算法，称为基于格点的规划）以寻找路径。然而，由于较大的分支因子，此类搜索在实际应用中可能效率低下。为此，我们提出一种基于栅格单元搜索（如经典A*算法）的新技术，同时将可能的运动基元序列适配到这些单元中。所得算法MeshA*一方面被证明能保持完备性与最优性保证，另一方面在运行时间上显著优于传统基于格点的规划方法（运行时间减少1.5倍）。此外，我们提出一种额外的剪枝技术，进一步缩减MeshA*的搜索空间。最终规划器与常规A*算法结合以保持完备性，实验表明该方法能以可忽略的解质量损失为代价，进一步提升搜索性能。