Multi-Robot-Arm Motion Planning (M-RAMP) is a challenging problem featuring complex single-agent planning and multi-agent coordination. Recent advancements in extending the popular Conflict-Based Search (CBS) algorithm have made large strides in solving Multi-Agent Path Finding (MAPF) problems. However, fundamental challenges remain in applying CBS to M-RAMP. A core challenge is the existing reliance of the CBS framework on conservative "complete" constraints. These constraints ensure solution guarantees but often result in slow pruning of the search space -- causing repeated expensive single-agent planning calls. Therefore, even though it is possible to leverage domain knowledge and design incomplete M-RAMP-specific CBS constraints to more efficiently prune the search, using these constraints would render the algorithm itself incomplete. This forces practitioners to choose between efficiency and completeness. In light of these challenges, we propose a novel algorithm, Generalized ECBS, aimed at removing the burden of choice between completeness and efficiency in MAPF algorithms. Our approach enables the use of arbitrary constraints in conflict-based algorithms while preserving completeness and bounding sub-optimality. This enables practitioners to capitalize on the benefits of arbitrary constraints and opens a new space for constraint design in MAPF that has not been explored. We provide a theoretical analysis of our algorithms, propose new "incomplete" constraints, and demonstrate their effectiveness through experiments in M-RAMP.

翻译：多机器人臂运动规划（M-RAMP）是一个具有挑战性的问题，其特点是复杂的单智能体规划与多智能体协调。近年来，流行的冲突基搜索（CBS）算法的扩展在解决多智能体路径寻找（MAPF）问题上取得了长足进展。然而，将CBS应用于M-RAMP仍面临根本性挑战。核心挑战在于CBS框架对保守的“完全”约束的现有依赖。这些约束确保了解决方案的保证，但往往导致搜索空间修剪缓慢——从而反复引发昂贵的单智能体规划调用。因此，即使可以利用领域知识设计不完全的M-RAMP专用CBS约束以更高效地修剪搜索，使用这些约束也会使算法本身变得不完全。这迫使实践者在效率与完全性之间做出选择。鉴于这些挑战，我们提出了一种新颖算法——广义ECBS，旨在消除MAPF算法中完全性与效率之间的选择负担。我们的方法支持在冲突基算法中使用任意约束，同时保持完全性并限制次优性。这使得实践者能够利用任意约束的优势，并开辟了MAPF中尚未探索的约束设计新空间。我们提供了算法的理论分析，提出了新的“不完全”约束，并通过M-RAMP实验证明了其有效性。