ML Anthology

Offline Time-Independent Multi-Agent Path Planning

Keisuke Okumura, François Bonnet, Yasumasa Tamura, Xavier Défago
IJCAI 2022 pp. 4649-4656
doi:10.24963/IJCAI.2022/645 /ijcai/2022/okumura2022ijcai-offline/

Abstract

This paper studies a novel planning problem for multiple agents that cannot share holding resources, named OTIMAPP (Offline Time-Independent Multi-Agent Path Planning). Given a graph and a set of start-goal pairs, the problem consists in assigning a path to each agent such that every agent eventually reaches their goal without blocking each other, regardless of how the agents are being scheduled at runtime. The motivation stems from the nature of distributed environments that agents take actions fully asynchronous and have no knowledge about those exact timings of other actors. We present solution conditions, computational complexity, solvers, and robotic applications.

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Cite

Text

Okumura et al. "Offline Time-Independent Multi-Agent Path Planning." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/645

Markdown

[Okumura et al. "Offline Time-Independent Multi-Agent Path Planning." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/okumura2022ijcai-offline/) doi:10.24963/IJCAI.2022/645

BibTeX

@inproceedings{okumura2022ijcai-offline,
  title     = {{Offline Time-Independent Multi-Agent Path Planning}},
  author    = {Okumura, Keisuke and Bonnet, François and Tamura, Yasumasa and Défago, Xavier},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {4649-4656},
  doi       = {10.24963/IJCAI.2022/645},
  url       = {https://mlanthology.org/ijcai/2022/okumura2022ijcai-offline/}
}