# Multi-agent RRT*: Sampling-based Cooperative Pathfinding (Extended Abstract)

arXiv: https://arxiv.org/abs/1302.2828 · HTML: https://mlsanity.com/paper/1302.2828/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2013.

## Built on by (most cited first, 5 of 5)

- [Learning Efficient Multi-Agent Cooperative Visual Exploration](https://mlsanity.com/paper/2110.05734/) (2021)
- [Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State Search](https://mlsanity.com/paper/2412.13359/) (2024)
- [CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces](https://mlsanity.com/paper/2201.09467/) (2022)
- [Factorization of Multi-Agent Sampling-Based Motion Planning](https://mlsanity.com/paper/2304.00342/) (2023)
- [Multi-agent Path Finding in Continuous Environment](https://mlsanity.com/paper/2409.10680/) (2024)

## Abstract (arXiv)

Cooperative pathfinding is a problem of finding a set of non-conflicting trajectories for a number of mobile agents. Its applications include planning for teams of mobile robots, such as autonomous aircrafts, cars, or underwater vehicles. The state-of-the-art algorithms for cooperative pathfinding typically rely on some heuristic forward-search pathfinding technique, where A* is often the algorithm of choice. Here, we propose MA-RRT*, a novel algorithm for multi-agent path planning that builds upon a recently proposed asymptotically-optimal sampling-based algorithm for finding single-agent shortest path called RRT*. We experimentally evaluate the performance of the algorithm and show that the sampling-based approach offers better scalability than the classical forward-search approach in relatively large, but sparse environments, which are typical in real-world applications such as multi-aircraft collision avoidance.