21 citations · 26 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State Search
Jingtian Yan, Jiaoyang Li
Multi-Agent Motion Planning (MAMP) finds various applications in fields such as traffic management, airport operations, and warehouse automation. In many of these environments, dif…
Multi-Agent Motion Planning with Bézier Curve Optimization under Kinodynamic Constraints
Jingtian Yan, Jiaoyang Li
Multi-Agent Motion Planning (MAMP) is a problem that seeks collision-free dynamically-feasible trajectories for multiple moving agents in a known environment while minimizing their…
Enhancing Multi-Drone Coordination for Filming Group Behaviours in Dynamic Environments
Aditya Rauniyar, Jiaoyang Li, Sebastian Scherer
Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics and AI, with numerous applications in real-world scenarios. One such scenario is filming scenes with multiple a…