# Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidance

arXiv: https://arxiv.org/abs/2105.10993 · HTML: https://mlsanity.com/paper/2105.10993/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 67 references that are in the map.
- First posted on arXiv 23 May 2021.

## Builds on (most cited first, 13 of 13)

- [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019)
- [Searching with Consistent Prioritization for Multi-Agent Path Finding](https://mlsanity.com/paper/1812.06356/) (2018)
- [Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks](https://mlsanity.com/paper/1705.10868/) (2017)
- [Analysis and Observations from the First Amazon Picking Challenge](https://mlsanity.com/paper/1601.05484/) (2016)
- [Distributed Constraint Optimization Problems and Applications: A Survey](https://mlsanity.com/paper/1602.06347/) (2016)
- [Optimal Target Assignment and Path Finding for Teams of Agents](https://mlsanity.com/paper/1612.05693/) (2016)
- [Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search](https://mlsanity.com/paper/2103.07116/) (2021)
- [Efficient Large-Scale Multi-Drone Delivery Using Transit Networks](https://mlsanity.com/paper/1909.11840/) (2019)
- [Experimenting with robotic intra-logistics domains](https://mlsanity.com/paper/1804.10247/) (2018)
- [Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering](https://mlsanity.com/paper/2009.05161/) (2020)
- [Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios](https://mlsanity.com/paper/1702.05515/) (2017)
- [Revisiting the Complexity Analysis of Conflict-Based Search: New Computational Techniques and Improved Bounds](https://mlsanity.com/paper/2104.08759/) (2021)
- [Solving Transition-Independent Multi-agent MDPs with Sparse Interactions (Extended version)](https://mlsanity.com/paper/1511.09047/) (2015)

## Built on by (most cited first, 8 of 8)

- [Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers](https://mlsanity.com/paper/2212.11498/) (2022)
- [Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers](https://mlsanity.com/paper/2305.05909/) (2023)
- [Self-Motivated Multi-Agent Exploration](https://mlsanity.com/paper/2301.02083/) (2023)
- [Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks](https://mlsanity.com/paper/2110.08802/) (2021)
- [The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications](https://mlsanity.com/paper/2203.07092/) (2022)
- [CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems](https://mlsanity.com/paper/2406.17425/) (2024)
- [Optimal Multi-Agent Path Finding for Precedence Constrained Planning Tasks](https://mlsanity.com/paper/2202.10449/) (2022)
- [Multi-Robot Path Planning Combining Heuristics and Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.01270/) (2023)

## Abstract (arXiv)

We introduce the Cooperative Multi-Agent Path Finding (Co-MAPF) problem, an extension to the classical MAPF problem, where cooperative behavior is incorporated. In this setting, a group of autonomous agents operate in a shared environment and have to complete cooperative tasks while avoiding collisions with the other agents in the group. This extension naturally models many real-world applications, where groups of agents are required to collaborate in order to complete a given task. To this end, we formalize the Co-MAPF problem and introduce Cooperative Conflict-Based Search (Co-CBS), a CBS-based algorithm for solving the problem optimally for a wide set of Co-MAPF problems. Co-CBS uses a cooperation-planning module integrated into CBS such that cooperation planning is decoupled from path planning. Finally, we present empirical results on several MAPF benchmarks demonstrating our algorithm's properties.