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[2602.15721] Lifelong Scalable Multi-Agent Realistic Testbed and A Comprehensive Study on Design Choices in Lifelong AGV Fleet Management Systems
Summary
The paper presents LSMART, an open-source simulator for evaluating Multi-Agent Path Finding (MAPF) algorithms in Automated Guided Vehicle (AGV) Fleet Management Systems (FMS), addressing design choices for lifelong AGV coordination.
Why It Matters
As industries increasingly adopt automation, understanding how to efficiently manage fleets of AGVs is crucial. This research provides a comprehensive framework for evaluating AGV systems, which can enhance operational efficiency in settings like warehouses and manufacturing.
Key Takeaways
- LSMART is an open-source tool designed for evaluating MAPF algorithms in AGV systems.
- The paper discusses key design choices for centralized, lifelong AGV fleet management.
- It highlights the importance of considering kinodynamics, communication delays, and execution uncertainties.
- Experiment results offer practical guidance for effective AGV system design.
- The research contributes to the growing field of autonomous systems and robotics.
Computer Science > Robotics arXiv:2602.15721 (cs) [Submitted on 17 Feb 2026] Title:Lifelong Scalable Multi-Agent Realistic Testbed and A Comprehensive Study on Design Choices in Lifelong AGV Fleet Management Systems Authors:Jingtian Yan, Yulun Zhang, Zhenting Liu, Han Zhang, He Jiang, Jingkai Chen, Stephen F. Smith, Jiaoyang Li View a PDF of the paper titled Lifelong Scalable Multi-Agent Realistic Testbed and A Comprehensive Study on Design Choices in Lifelong AGV Fleet Management Systems, by Jingtian Yan and 7 other authors View PDF HTML (experimental) Abstract:We present Lifelong Scalable Multi-Agent Realistic Testbed (LSMART), an open-source simulator to evaluate any Multi-Agent Path Finding (MAPF) algorithm in a Fleet Management System (FMS) with Automated Guided Vehicles (AGVs). MAPF aims to move a group of agents from their corresponding starting locations to their goals. Lifelong MAPF (LMAPF) is a variant of MAPF that continuously assigns new goals for agents to reach. LMAPF applications, such as autonomous warehouses, often require a centralized, lifelong system to coordinate the movement of a fleet of robots, typically AGVs. However, existing works on MAPF and LMAPF often assume simplified kinodynamic models, such as pebble motion, as well as perfect execution and communication for AGVs. Prior work has presented SMART, a software capable of evaluating any MAPF algorithms while considering agent kinodynamics, communication delays, and execution uncertainties. Howev...