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：本文通过改进近期提出的LaCAM*算法，应对实时、大规模且近似最优的多智能体路径规划（MAPF）挑战。LaCAM*是一种可扩展的搜索型算法，能保证最终找到累积转移成本的最优解。尽管该算法在规划成功率上表现卓越，超越多种前沿MAPF方法，但其初始解质量远非最优，且收敛至最优解的速度缓慢。为克服这些局限，本文引入多项改进技术，部分灵感源于其他MAPF方法。我们通过实证表明，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。