7 papers
Search-Aided Joint Agent-Environment Reinforcement Learning for Robust Lifelong Multi-Agent Path Finding with Rotations
He Jiang, Jingtian Yan, Yulun Zhang +5
Lifelong Multi-Agent Path Finding (LMAPF) requires repeatedly planning collision-free paths for agents that continuously receive new goals upon reaching their current ones. While m…
From Discrete Plans to Real-World Execution: A World-Model-Driven Framework for Execution-Aware Multi-Agent Path Finding
Jingtian Yan, Shuai Zhou, He Jiang +2
Multi-Agent Path Finding (MAPF) studies how to coordinate multiple agents to reach their goals without collisions and underpins a range of large-scale robotic systems, including au…
WinkTPG: An Execution Framework for Multi-Agent Path Finding Using Temporal Reasoning
Jingtian Yan, Stephen F. Smith, Jiaoyang Li
Planning collision-free paths for a large group of agents is a challenging problem in many real-world applications. While recent advances in Multi-Agent Path Finding (MAPF) have sh…
Advancing MAPF Toward the Real World: A Scalable Multi-Agent Realistic Testbed (SMART)
Jingtian Yan, Zhifei Li, William Kang +7
We present Scalable Multi-Agent Realistic Testbed (SMART), a realistic and efficient software tool for evaluating Multi-Agent Path Finding (MAPF) algorithms. MAPF focuses on planni…
Analyzing Planner Design Trade-offs for MAPF under ADG-based Realistic Execution
Jingtian Yan, Zhifei Li, William Kang +2
Multi-Agent Path Finding (MAPF) algorithms are increasingly deployed in industrial warehouses and automated manufacturing facilities, where robots must operate reliably under real-…
Lifelong Scalable Multi-Agent Realistic Testbed and A Comprehensive Study on Design Choices in Lifelong AGV Fleet Management Systems
Jingtian Yan, Yulun Zhang, Zhenting Liu +5
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…