Feng Zhuang — accepted AI-conference papers

2025

A Robust Lifelong Multi-Agent Path Finding With Active Conflict Resolution and Decentralized Execution

Feng Zhuang, Ting Huang, Quan Xu, Yue-Jiao Gong, Jing Liu

RA-L 2025

Multi-Agent Path Finding (MAPF) focuses on navigating agents along cost-efficient and conflict-free paths. This letter investigates a challenging and practical MAPF variant, namely Robust Lifelong MAPF (RLMAPF), where agents sequentially receive tasks and effectively deal with uncertainties. In this

Cited by 5 Source ↗Scholar ↗

2025

TaskSimLF: Efficient Leader-Follower Multi-Agent Path Finding With Clustered Pickup and Delivery

Feng Zhuang, Ting Huang, Jing Liu

RA-L 2025

Multi-Agent Path Finding (MAPF) aims at finding a set of conflict-free and cost-optimal paths for agents from pickup to delivery locations. Most existing MAPF research focus on exhaustively search for path set for the agents with conflict-free paths, which often results in high computational costs.

Cited by 1 Source ↗Scholar ↗