Advancing Multi-Agent Pathfinding (MAPF) and Multi-Robot Motion Planning (MRMP) requires platforms that enable transparent, reproducible comparisons across modeling choices. Existing tools either scale under simplifying assumptions (grids, homogeneous agents) or offer higher fidelity with less comparable instrumentation. We present GRACE, a unified 2D simulator+benchmark that instantiates the same task at multiple abstraction levels (grid, roadmap, continuous) via explicit, reproducible operators and a common evaluation protocol. Our empirical results on public maps and representative planners enable commensurate comparisons on a shared instance set. Furthermore, we quantify the expected representation-fidelity trade-offs (MRMP solves instances at higher fidelity but lower speed, while grid/roadmap planners scale farther). By consolidating representation, execution, and evaluation, GRACE thereby aims to make cross-representation studies more comparable and provides a means to advance multi-robot planning research and its translation to practice.

翻译：推进多智能体路径规划与多机器人运动规划研究需要能够实现建模选择透明化、可复现比较的平台。现有工具要么在简化假设下具备可扩展性，要么提供更高保真度但缺乏可比性评估工具。本文提出GRACE，一个统一的二维仿真+基准测试平台，通过显式、可复现的操作符与通用评估协议，在多个抽象层次上实例化相同任务。在公开地图与代表性规划器上的实证结果支持对共享实例集进行可公度比较。此外，我们量化了表征保真度的预期权衡关系，并通过整合表征、执行与评估环节，使跨表征研究更具可比性，为推进多机器人规划研究及其实际应用转化提供有效工具。