activity
20162020
most citedOverview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios

91 citations · 219 across the 7 of their papers we have counts for

collaborators
Showing 2018Show all

5 papers · 1 filter

cs.AI201818 cited

Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery

Hang Ma, Wolfgang Hönig, T. K. Satish Kumar +2

The Multi-Agent Pickup and Delivery (MAPD) problem models applications where a large number of agents attend to a stream of incoming pickup-and-delivery tasks. Token Passing (TP) i…

cs.RO2018

PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning

Guillaume Sartoretti, Justin Kerr, Yunfei Shi +4

Multi-agent path finding (MAPF) is an essential component of many large-scale, real-world robot deployments, from aerial swarms to warehouse automation. However, despite the commun…

cs.AI2018

Multi-Agent Path Finding with Deadlines

Hang Ma, Glenn Wagner, Ariel Felner +3

We formalize Multi-Agent Path Finding with Deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start ve…

cs.AI2018

Multi-Agent Path Finding with Deadlines: Preliminary Results

Hang Ma, Glenn Wagner, Ariel Felner +3

We formalize the problem of multi-agent path finding with deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their…

cs.AI2018

Overview: A Hierarchical Framework for Plan Generation and Execution in Multi-Robot Systems

Hang Ma, Wolfgang Hönig, Liron Cohen +5

The authors present an overview of a hierarchical framework for coordinating task- and motion-level operations in multirobot systems. Their framework is based on the idea of using…