This paper addresses the challenges of real-time, large-scale, and near-optimal multi-agent pathfinding (MAPF) through enhancements to the recently proposed LaCAM* algorithm. LaCAM* is a scalable search-based algorithm that guarantees the eventual finding of optimal solutions for cumulative transition costs. While it has demonstrated remarkable planning success rates, surpassing various state-of-the-art MAPF methods, its initial solution quality is far from optimal, and its convergence speed to the optimum is slow. To overcome these limitations, this paper introduces several improvement techniques, partly drawing inspiration from other MAPF methods. We provide empirical evidence that the fusion of these techniques significantly improves the solution quality of LaCAM*, thus further pushing the boundaries of MAPF algorithms.

翻译：本文针对实时、大规模、近最优的多智能体路径规划（MAPF）挑战，通过改进近期提出的LaCAM*算法来应对。LaCAM*是一种可扩展的基于搜索的算法，能够保证最终找到累积转移成本的最优解。尽管该算法展现出卓越的规划成功率，超越了多种最先进的MAPF方法，但其初始解质量远非最优，且收敛至最优解的速度较慢。为克服这些局限，本文引入了多项改进技术，部分灵感来源于其他MAPF方法。我们通过实验证明，这些技术的融合显著提升了LaCAM*的解质量，从而进一步拓展了MAPF算法的性能边界。