# Multi-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses

arXiv: https://arxiv.org/abs/2408.14527 · HTML: https://mlsanity.com/paper/2408.14527/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 26 Aug 2024.

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

- [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019)
- [Lifelong Multi-Agent Path Finding in Large-Scale Warehouses](https://mlsanity.com/paper/2005.07371/) (2020)
- [Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks](https://mlsanity.com/paper/1705.10868/) (2017)
- [Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery](https://mlsanity.com/paper/1812.06355/) (2018)
- [Solving Simultaneous Target Assignment and Path Planning Efficiently with Time-Independent Execution](https://mlsanity.com/paper/2109.04264/) (2021)
- [Improved Anonymous Multi-Agent Path Finding Algorithm](https://mlsanity.com/paper/2312.10572/) (2023)
- [The Bounded Acceleration Shortest Path problem: complexity and solution algorithms](https://mlsanity.com/paper/2103.02914/) (2021)

## Built on by (most cited first, 2 of 2)

- [CAMAR: Continuous Actions Multi-Agent Routing](https://mlsanity.com/paper/2508.12845/) (2025)
- [WinkTPG: An Execution Framework for Multi-Agent Path Finding Using Temporal Reasoning](https://mlsanity.com/paper/2508.01495/) (2025)

## Abstract (arXiv)

Multi-Agent Path Finding (MAPF) is an important optimization problem underlying the deployment of robots in automated warehouses and factories. Despite the large body of work on this topic, most approaches make heavy simplifications, both on the environment and the agents, which make the resulting algorithms impractical for real-life scenarios. In this paper, we consider a realistic problem of online order delivery in a warehouse, where a fleet of robots bring the products belonging to each order from shelves to workstations. This creates a stream of inter-dependent pickup and delivery tasks and the associated MAPF problem consists of computing realistic collision-free robot trajectories fulfilling these tasks. To solve this MAPF problem, we propose an extension of the standard Prioritized Planning algorithm to deal with the inter-dependent tasks (Interleaved Prioritized Planning) and a novel Via-Point Star (VP*) algorithm to compute an optimal dynamics-compliant robot trajectory to visit a sequence of goal locations while avoiding moving obstacles. We prove the completeness of our approach and evaluate it in simulation as well as in a real warehouse.