GRACE: A Unified 2D Multi-Robot Path Planning Simulator & Benchmark for Grid, Roadmap, And Continuous Environments

TL;DR

GRACE is a versatile 2D simulation platform that unifies multi-robot path planning across grid, roadmap, and continuous environments, enabling fair comparisons and advancing research in the field.

Contribution

It introduces a unified simulator and benchmark that supports multiple abstraction levels with reproducible operators and evaluation protocols, facilitating comprehensive comparisons.

Findings

Higher fidelity MRMP solves more complex instances but at lower speed.

Grid and roadmap planners scale better but with less detail.

The platform enables fair, cross-representation benchmarking.

Abstract

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.