GRACE: A Unified 2D Multi-Robot Path Planning Simulator & Benchmark for Grid, Roadmap, And Continuous Environments
🤖 AI Summary
This work addresses the lack of a unified multi-robot motion planning (MRMP) platform capable of enabling transparent and reproducible comparisons across different environmental abstractions—namely grids, roadmaps, and continuous space. To bridge this gap, we present the first 2D simulation and benchmarking framework that integrates all three modeling paradigms within a common infrastructure. By employing unified task generation, standardized evaluation protocols, and implementations of representative planners, our framework facilitates fair algorithmic comparisons across abstraction levels. Experimental results demonstrate that high-fidelity continuous-space approaches achieve superior solution accuracy at the cost of substantial computational overhead, whereas grid- and roadmap-based methods offer better scalability. Our study quantitatively reveals the inherent trade-off between representational fidelity and computational efficiency in MRMP.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Multi-Agent Pathfinding
Multi-Robot Motion Planning
Simulation Benchmark
Representation Abstraction
Cross-Representation Comparison
Innovation
Methods, ideas, or system contributions that make the work stand out.
multi-robot path planning
unified simulation benchmark
cross-representation comparison
abstraction levels
reproducible evaluation
🔎 Similar Papers
No similar papers found.
