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）是一个涉及复杂单智能体规划与多智能体协调的挑战性问题。近期，通过扩展主流冲突基搜索（CBS）算法，多智能体路径规划（MAPF）问题的求解已取得重大进展。然而，将CBS应用于M-RAMP仍面临根本性挑战，核心在于CBS框架对保守型“完全”约束的依赖。这类约束虽能保障解的存在性，却常导致搜索空间剪枝效率低下——引发大量高代价的单智能体重复规划调用。因此，即便可利用领域知识设计针对M-RAMP的不完全CBS约束以提升剪枝效率，采用这些约束亦会使算法丧失完备性，迫使实践者在效率与完备性间做出取舍。针对上述挑战，我们提出新型算法“广义ECBS”，旨在解除MAPF算法中完备性与效率间的两难困境。该方法可支持在冲突基算法中采用任意约束，同时保持完备性并界定次优性界限。这使实践者得以充分利用任意约束的优势，开创了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，提出新型“不完全”约束，并通过M-RAMP实验验证其有效性。