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GRACE is introduced as a unified 2D simulator and benchmark for multi-agent pathfinding (MAPF) and multi-robot motion planning (MRMP) across grid, roadmap, and continuous environments. It enables transparent comparisons by instantiating the same task at multiple abstraction levels with explicit operators and a common evaluation protocol. Experiments on public maps and planners quantify representation-fidelity trade-offs, showing MRMP solves instances at higher fidelity but lower speed compared to grid/roadmap planners.
Finally, a multi-robot path planning benchmark that lets you directly compare grid-based, roadmap, and continuous planners on the same tasks.
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.
