ML Anthology

Fault-Tolerant Offline Multi-Agent Path Planning

Keisuke Okumura, Sébastien Tixeuil
AAAI 2023 pp. 11647-11654
doi:10.1609/AAAI.V37I10.26376 /aaai/2023/okumura2023aaai-fault/

Abstract

We study a novel graph path planning problem for multiple agents that may crash at runtime, and block part of the workspace. In our setting, agents can detect neighboring crashed agents, and change followed paths at runtime. The objective is then to prepare a set of paths and switching rules for each agent, ensuring that all correct agents reach their destinations without collisions or deadlocks, despite unforeseen crashes of other agents. Such planning is attractive to build reliable multi-robot systems. We present problem formalization, theoretical analysis such as computational complexities, and how to solve this offline planning problem.

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Cite

Text

Okumura and Tixeuil. "Fault-Tolerant Offline Multi-Agent Path Planning." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I10.26376

Markdown

[Okumura and Tixeuil. "Fault-Tolerant Offline Multi-Agent Path Planning." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/okumura2023aaai-fault/) doi:10.1609/AAAI.V37I10.26376

BibTeX

@inproceedings{okumura2023aaai-fault,
  title     = {{Fault-Tolerant Offline Multi-Agent Path Planning}},
  author    = {Okumura, Keisuke and Tixeuil, Sébastien},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {11647-11654},
  doi       = {10.1609/AAAI.V37I10.26376},
  url       = {https://mlanthology.org/aaai/2023/okumura2023aaai-fault/}
}