GRACE统一模拟多机器人路径规划在不同抽象层级的表现。
GRACE: A Unified 2D Multi-Robot Path Planning Simulator & Benchmark for Grid, Roadmap, And Continuous Environments
- 支持网格、路线图和连续环境的统一仿真与评估
- 实测显示连续环境精度高但速度慢，网格效率高但精度低
- 适合研究多智能体路径规划算法跨表示比较
推进多智能体路径规划（MAPF）和多机器人运动规划（MRMP）需要能够透明、可复现地比较不同建模选择的平台。现有工具要么在简化假设下运行（如网格、同质代理），要么提供更高保真度但可比性差的实验设置。我们提出GRACE，一个统一的2D仿真器+基准测试平台，通过明确且可复现的操作符，在网格、路线图和连续环境中对同一任务进行实例化，并采用统一评估协议。我们在公开地图和典型规划器上进行实证分析，实现了在共享实例集上的可比性评估。此外，我们量化了表示-保真度之间的权衡：MRMP在更高保真度下求解，但速度更慢；而网格/路线图规划器扩展能力更强。通过整合表示、执行与评估，GRACE旨在使跨表示研究更具可比性，并推动多机器人规划研究及其向实际应用的转化。
原文摘要 · Abstract (English)
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.
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