6 citations · 9 across the 7 of their papers we have counts for
9 papers
Belief-Aware Multi-Agent Path Finding under Map Uncertainty
Viraj Parimi, Shao-Hung Chan, Han Zhang +2
Multi-Agent Path Finding (MAPF) aims to find collision-free paths for multiple agents in a shared environment. Classical MAPF assumes that all static obstacles are known in advance…
ITA-LaCAM: A Complete and Scalable TAPF Solver via Assignment-Aware Configuration-Space Search
Yimin Tang, Han Zhang, Shao-Hung Chan +4
Combined Target Assignment and Path Finding (TAPF) requires assigning targets for agents while simultaneously planning collision-free paths. We present ITA-LaCAM, a complete and sc…
Dynamic Agent Grouping ECBS: Scaling Windowed Multi-Agent Path Finding with Completeness Guarantees
Tiannan Zhang, Rishi Veerapaneni, Shao-Hung Chan +2
Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths for a team of agents. Although several MAPF methods which solve full-horizon MAPF have compl…
New Mechanisms in Flex Distribution for Bounded Suboptimal Multi-Agent Path Finding
Shao-Hung Chan, Thomy Phan, Jiaoyang Li +1
Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths, one for each agent in a shared environment. Its objective is to minimize the sum of path co…
Anytime Multi-Agent Path Finding with an Adaptive Delay-Based Heuristic
Thomy Phan, Benran Zhang, Shao-Hung Chan +1
Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in multi-agent systems. MAPF-LNS, based on Large Neighborhood Search (LNS), is the cur…
Anytime Multi-Agent Path Finding using Operation Parallelism in Large Neighborhood Search
Shao-Hung Chan, Zhe Chen, Dian-Lun Lin +5
Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths for multiple agents in a shared environment while minimizing the sum of travel time. Since s…
Multi-Robot Geometric Task-and-Motion Planning for Collaborative Manipulation Tasks
Hejia Zhang, Shao-Hung Chan, Jie Zhong +6
We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple rob…
A MIP-Based Approach for Multi-Robot Geometric Task-and-Motion Planning
Hejia Zhang, Shao-Hung Chan, Jie Zhong +3
We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple rob…
Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World
Florian Laurent, Manuel Schneider, Christian Scheller +24
The Flatland competition aimed at finding novel approaches to solve the vehicle re-scheduling problem (VRSP). The VRSP is concerned with scheduling trips in traffic networks and th…