Optimal Multi-Robot Path Planning (MRPP) has garnered significant attention due to its many applications in domains including warehouse automation, transportation, and swarm robotics. Current MRPP solvers can be divided into reduction-based, search-based, and rule-based categories, each with their strengths and limitations. Regardless of the methodology, however, the issue of handling dense MRPP instances remains a significant challenge, where existing approaches generally demonstrate a dichotomy regarding solution optimality and efficiency. This study seeks to bridge the gap in optimal MRPP resolution for dense, highly-entangled scenarios, with potential applications to high-density storage systems and traffic congestion control. Toward that goal, we analyze the behaviors of SOTA MRPP algorithms in dense settings and develop two hybrid algorithms leveraging the strengths of existing SOTA algorithms: DCBS (database-accelerated enhanced conflict-based search) and SCBS (sparsified enhanced conflict-based search). Experimental validations demonstrate that DCBS and SCBS deliver a significant reduction in computational time compared to existing bounded-suboptimal methods and improve solution quality compared to existing rule-based methods, achieving a desirable balance between computational efficiency and solution optimality. As a result, DCBS and SCBS are particularly suitable for quickly computing good-quality solutions for multi-robot routing in dense settings

翻译：最优多机器人路径规划（MRPP）因其在仓储自动化、交通运输及群体机器人等领域的广泛应用而备受关注。现有MRPP求解器可分为基于归约、基于搜索和基于规则三类，各自具有优势与局限性。然而，无论采用何种方法，处理密集型MRPP实例仍是一个重大挑战——现有方法在解的最优性与求解效率之间普遍存在二元对立现象。本研究致力于弥合密集、高度纠缠场景下最优MRPP求解的鸿沟，其潜在应用涵盖高密度存储系统与交通拥堵控制。为此，我们分析了先进MRPP算法在密集环境中的行为特性，并基于现有先进算法的优势开发了两种混合算法：DCBS（数据库加速增强型冲突搜索）与SCBS（稀疏化增强型冲突搜索）。实验验证表明，相较于现有有界次优性方法，DCBS与SCBS大幅降低了计算时间；相较于现有基于规则的方法，则显著提升了解的质量，在计算效率与解的最优性之间实现了理想平衡。因此，DCBS与SCBS尤其适用于在密集环境下快速计算多机器人路径规划的高质量解。