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

Chuanlong Zang, Anna Mannucci, Isabelle Barz, Philipp Schillinger, Florian Lier, Wolfgang Hönig

TL;DR

GRACE is presented, a unified 2D simulator+benchmark that instantiates the same task at multiple abstraction levels via explicit, reproducible operators and a common evaluation protocol that aims to make cross-representation studies more comparable and provides a means to advance multi-robot planning research and its translation to practice.

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.

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

TL;DR

GRACE is presented, a unified 2D simulator+benchmark that instantiates the same task at multiple abstraction levels via explicit, reproducible operators and a common evaluation protocol that aims to make cross-representation studies more comparable and provides a means to advance multi-robot planning research and its translation to practice.

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.
Paper Structure (25 sections, 4 equations, 4 figures, 5 tables)

This paper contains 25 sections, 4 equations, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Benchmarks and Platforms in MAPF and MRMP
  4. Planner Families Across Representations
  5. Grid-centric MAPF (Rep-G)
  6. Roadmap-based MAPF (Rep-R)
  7. Continuous-space MAMP/MRMP (Rep-C)
  8. Problem Formulation
  9. Environment Model and Agent's Goals
  10. Agent Model
  11. Planner Model
  12. Feasibility
  13. Objectives
  14. Assumptions and Scope
  15. Problem statement
  16. ...and 10 more sections

Figures (4)

  • Figure 1: GRACE: Single scenario, multiple planning representations. Identical agents/scenarios, distinct trajectories from representation-specific constraints: (a) occupancy-grid (4-connected discrete actions), (b) graph abstraction (single integrator), and (c) continuous space (double integrator, heterogeneous footprint). Individual path lengths highlight these representation-specific constraints.
  • Figure 2: Architectural Diagram of GRACE.
  • Figure 3: Cross-Environment Comparison of MRMP and MAPF Solvers: SoC, Makespan, Planning Time (db-CBS=100%)
  • Figure 4: Grid MAPF Comparison: Success Rate with Number of Agents.