**Modular Design** ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ .. figure:: readme_assets/l-smart-2.0.png :alt: LSMART Pipeline :align: center :width: 90% Detailed Architecture of LSMART LSMART encapsulates key design choices of a real-world Fleet Management System (FMS) in separate modules. Red boxes highlight the user customizable modules. **Real-world Considerations** ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ .. raw:: html

Differentiable Drive Robot Kinodynamics

Real-world Execution Uncertainties

Built on top of SMART [1], we consider realistic AGV kinodynamics by modeling them as differentiable drive robots, which can move forward and rotate in place with constraints in maximum velocity and acceleration. This is in contrast to prior works that model AGVs as simple omnidirectional/pebble motion agents. We also simulate real-world execution delays and use Action Dependency Graph (ADG) [2] to ensure collision-free of the executed paths. **Insane Scalability** ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ .. raw:: html

Simulation of 1000 robots in a large warehouse.

LSMART is highly scalable in terms of the number of robots and the size of the map. Here we show a simulation of 1000 robots in the *warehouse-10-20-10-2-1* map from the MAPF benchmark [3]. We use PIBT [4] as the underlying MAPF planner and plan windowed paths every 1 simulation second. The simulation runs for 600 simulation seconds and takes 268 seconds to finish with a AMD Ryzen 9 9950X 16-Core Processor. **References** ++++++++++++++++++++++++++++++ [1] Yan, J.; Li, Z.; Kang, W.; Zheng, K.; Zhang, Y.; Chen, Z.; Zhang, Y.; Harabor, D.; Smith, S. F.; and Li, J. 2025. Advancing MAPF towards the Real World: A Scalable Multi-Agent Realistic Testbed (SMART). ArXiv, abs/2503.04798. [2] Hönig, W.; Kiesel, S.; Tinka, A.; Durham, J. W.; and Ayanian, N. 2019. Persistent and Robust Execution of MAPF Schedules in Warehouses. IEEE Robotics and Automation Letters, 4: 1125-1131. [3] Stern, R.; Sturtevant, N. R.; Felner, A.; Koenig, S.; Ma, H.; Walker, T. T.; Li, J.; Atzmon, D.; Cohen, L.; Kumar, T. K. S.; Barták, R.; and Boyarski, E. 2019. Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks. In Proceedings of the International Symposium on Combinatorial Search (SoCS), 151-159. [4] Okumura, K.; Machida, M.; Défago, X.; and Tamura, Y. 2019. Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 535-542. **Paper and Citation** ++++++++++++++++++++++++++++++++ .. code-block:: bibtex @article{YanAndZhang2026LSMART, author = {Jingtian Yan, Yulun Zhang, Zhenting Liu, Han Zhang, He Jiang, Jingkai Chen, Stephen F. Smith and Jiaoyang Li}, title = {Lifelong Scalable Multi-Agent Realistic Testbed and A Comprehensive Study on Design Choices in Lifelong AGV Fleet Management Systems}, journal = {ArXiv}, volume = {abs/2602.15721}, year = {2026} } .. raw:: html