# Optimizing Space Utilization for More Effective Multi-Robot Path Planning

arXiv: https://arxiv.org/abs/2109.04677 · HTML: https://mlsanity.com/paper/2109.04677/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2021.

## Built on by (most cited first, 6 of 6)

- [Traffic Flow Optimisation for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2308.11234/) (2023)
- [Guidance Graph Optimization for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2402.01446/) (2024)
- [Engineering LaCAM^ast: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding](https://mlsanity.com/paper/2308.04292/) (2023)
- [Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2602.23468/) (2026)
- [Enhancing Lifelong Multi-Agent Path-finding by Using Artificial Potential Fields](https://mlsanity.com/paper/2505.22753/) (2025)
- [A Lightweight Traffic Map for Efficient Anytime LaCAM*](https://mlsanity.com/paper/2603.07891/) (2026)

## Abstract (arXiv)

We perform a systematic exploration of the principle of Space Utilization Optimization (SUO) as a heuristic for planning better individual paths in a decoupled multi-robot path planner, with applications to both one-shot and life-long multi-robot path planning problems. We show that the decentralized heuristic set, SU-I, preserves single path optimality and significantly reduces congestion that naturally happens when many paths are planned without coordination. Integration of SU-I into complete planners brings dramatic reductions in computation time due to the significantly reduced number of conflicts and leads to sizable solution optimality gains in diverse evaluation scenarios with medium and large maps, for both one-shot and life-long problem settings.