Connect Multi-Agent Path Finding: Generation and Visualization

Arthur Queffelec; Ocan Sankur; Francois Schwarzentruber

IJCAI 2021poster0 citations

Connect Multi-Agent Path Finding: Generation and Visualization

Arthur Queffelec, Ocan Sankur, Francois Schwarzentruber

Abstract

We present a generic tool to visualize missions of the Connected Multi-Agent Path Finding (CMAPF) problem. This problem is a variant of MAPF which requires a group of agents to navigate from an initial configuration to a goal configuration while maintaining connection. The user can create an instance of CMAPF and can play the generated plan. Any algorithm for CMAPF can be plugged into the tool.

Multi-agent Systems: GeneralPlanning and Scheduling: General

BibTeX

@inproceedings{ijcai2021p714,
  title     = {Connect Multi-Agent Path Finding: Generation and Visualization},
  author    = {Queffelec, Arthur and Sankur, Ocan and Schwarzentruber, Francois},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Zhi-Hua Zhou},
  pages     = {5008--5011},
  year      = {2021},
  month     = {8},
  note      = {Demo Track},
  doi       = {10.24963/ijcai.2021/714},
  url       = {https://doi.org/10.24963/ijcai.2021/714},
}