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，涉及复杂的单智能体规划与多智能体协调。近年来，扩展流行的基于冲突的搜索（CBS）算法在解决多智能体路径规划（MAPF）问题上取得了重大进展。然而，将CBS应用于M-RAMP仍存在根本性挑战。一个核心挑战在于CBS框架目前依赖于保守的“完备”约束。这些约束虽能确保解的存在性保证，但往往导致搜索空间剪枝缓慢——引发重复昂贵的单智能体规划调用。因此，尽管可以利用领域知识设计非完备的M-RAMP专用CBS约束来更高效地剪枝搜索空间，但使用这些约束会使算法本身丧失完备性。这迫使实践者必须在效率与完备性之间做出选择。针对这些挑战，我们提出了一种新算法——广义ECBS，旨在消除MAPF算法中效率与完备性之间的抉择负担。我们的方法允许在基于冲突的算法中使用任意约束，同时保持完备性并限定次优性边界。这使得实践者能够充分利用任意约束的优势，并为MAPF中尚未探索的约束设计开辟了新空间。我们提供了算法的理论分析，提出了新的“非完备”约束，并通过M-RAMP实验验证了其有效性。