SOREO: A System for Safe and Autonomous Drones Fleet Navigation with Reinforcement Learning

Réda Alami, Hakim Hacid, Lorenzo Bellone, Michal Barcis, Enrico Natalizio

AAAI 2023 pp. 16398-16400

doi:10.1609/AAAI.V37I13.27058 /aaai/2023/alami2023aaai-soreo/

Abstract

This demonstration introduces SOREO, a system that explores the possibility of extending UAVs autonomy through machine learning. It brings a contribution to the following problem: Having a fleet of drones and a geographic area, how to learn the shortest paths between any point with regards to the base points for optimal and safe package delivery? Starting from a set of possible actions, a virtual design of a geographic location of interest, e.g., a city, and a reward value, SOREO is capable of learning not only how to prevent collisions with obstacles, e.g., walls and buildings, but also to find the shortest path between any two points, i.e., the base and the target. SOREO exploits based on the Q-learning algorithm.

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Cite

Text

Alami et al. "SOREO: A System for Safe and Autonomous Drones Fleet Navigation with Reinforcement Learning." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.27058

Markdown

[Alami et al. "SOREO: A System for Safe and Autonomous Drones Fleet Navigation with Reinforcement Learning." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/alami2023aaai-soreo/) doi:10.1609/AAAI.V37I13.27058

BibTeX

@inproceedings{alami2023aaai-soreo,
  title     = {{SOREO: A System for Safe and Autonomous Drones Fleet Navigation with Reinforcement Learning}},
  author    = {Alami, Réda and Hacid, Hakim and Bellone, Lorenzo and Barcis, Michal and Natalizio, Enrico},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16398-16400},
  doi       = {10.1609/AAAI.V37I13.27058},
  url       = {https://mlanthology.org/aaai/2023/alami2023aaai-soreo/}
}