# Scalable Multi-robot Motion Planning for Congested Environments With Topological Guidance

arXiv: https://arxiv.org/abs/2210.07141 · HTML: https://mlsanity.com/paper/2210.07141/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [Searching with Consistent Prioritization for Multi-Agent Path Finding](https://mlsanity.com/paper/1812.06356/) (2018)
- [Representation-Optimal Multi-Robot Motion Planning using Conflict-Based Search](https://mlsanity.com/paper/1909.13352/) (2019)
- [dRRT*: Scalable and Informed Asymptotically-Optimal Multi-Robot Motion Planning](https://mlsanity.com/paper/1903.00994/) (2019)
- [Finding a needle in an exponential haystack: Discrete RRT for exploration of implicit roadmaps in multi-robot motion planning](https://mlsanity.com/paper/1305.2889/) (2013)
- [An Effective Algorithmic Framework for Near Optimal Multi-Robot Path Planning](https://mlsanity.com/paper/1505.00200/) (2015)
- [Scalable Asymptotically-Optimal Multi-Robot Motion Planning](https://mlsanity.com/paper/1706.09932/) (2017)
- [Hierarchical Planning with Annotated Skeleton Guidance](https://mlsanity.com/paper/2206.11977/) (2022)

## Abstract (arXiv)

Multi-robot motion planning (MRMP) is the problem of finding collision-free paths for a set of robots in a continuous state space. The difficulty of MRMP increases with the number of robots and is exacerbated in environments with narrow passages that robots must pass through, like warehouse aisles where coordination between robots is required. In single-robot settings, topology-guided motion planning methods have shown improved performance in these constricted environments. In this work, we extend an existing topology-guided single-robot motion planning method to the multi-robot domain to leverage the improved efficiency provided by topological guidance. We demonstrate our method's ability to efficiently plan paths in complex environments with many narrow passages, scaling to robot teams of size up to 25 times larger than existing methods in this class of problems. By leveraging knowledge of the topology of the environment, we also find higher-quality solutions than other methods.