# Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State Search

arXiv: https://arxiv.org/abs/2412.13359 · HTML: https://mlsanity.com/paper/2412.13359/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [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 in Large-Scale Warehouses](https://mlsanity.com/paper/2005.07371/) (2020)
- [Representation-Optimal Multi-Robot Motion Planning using Conflict-Based Search](https://mlsanity.com/paper/1909.13352/) (2019)
- [Multi-Agent Motion Planning with B'ezier Curve Optimization under Kinodynamic Constraints](https://mlsanity.com/paper/2311.14145/) (2023)
- [Multi-agent RRT*: Sampling-based Cooperative Pathfinding (Extended Abstract)](https://mlsanity.com/paper/1302.2828/) (2013)

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

- [Advancing MAPF Toward the Real World: A Scalable Multi-Agent Realistic Testbed (SMART)](https://mlsanity.com/paper/2503.04798/) (2025)
- [DeepFleet: Multi-Agent Foundation Models for Mobile Robots](https://mlsanity.com/paper/2508.08574/) (2025)
- [Lifelong Scalable Multi-Agent Realistic Testbed and A Comprehensive Study on Design Choices in Lifelong AGV Fleet Management Systems](https://mlsanity.com/paper/2602.15721/) (2026)
- [Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2602.23468/) (2026)
- [Search-Aided Joint Agent-Environment Reinforcement Learning for Robust Lifelong Multi-Agent Path Finding with Rotations](https://mlsanity.com/paper/2608.05588/) (2026)
- [From Discrete Plans to Real-World Execution: A World-Model-Driven Framework for Execution-Aware Multi-Agent Path Finding](https://mlsanity.com/paper/2511.21886/) (2025)
- [Analyzing Planner Design Trade-offs for MAPF under ADG-based Realistic Execution](https://mlsanity.com/paper/2512.09736/) (2025)
- [Scalable Long-Horizon Planning with Staggered Updates for Lifelong MAPF](https://mlsanity.com/paper/2608.06702/) (2026)

## Abstract (arXiv)

Multi-Agent Motion Planning (MAMP) finds various applications in fields such as traffic management, airport operations, and warehouse automation. In many of these environments, differential drive robots are commonly used. These robots have a kinodynamic model that allows only in-place rotation and movement along their current orientation, subject to speed and acceleration limits. However, existing Multi-Agent Path Finding (MAPF)-based methods often use simplified models for robot kinodynamics, which limits their practicality and realism. In this paper, we introduce a three-level framework called MASS to address these challenges. MASS combines MAPF-based methods with our proposed stationary state search planner to generate high-quality kinodynamically-feasible plans. We further extend MASS using an adaptive window mechanism to address the lifelong MAMP problem. Empirically, we tested our methods on the single-shot grid map domain and the lifelong warehouse domain. Our method shows up to 400% improvements in terms of throughput compared to existing methods.