SOREO: A System for Safe and Autonomous Drones Fleet Navigation with Reinforcement Learning
Reda Alami, Hakim Hacid, Lorenzo Bellone, Michal Barcis, Enrico Natalizio
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.
BibTeX
@article{Alami_Hacid_Bellone_Barcis_Natalizio_2024, title={SOREO: A System for Safe and Autonomous Drones Fleet Navigation with Reinforcement Learning}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/27058}, DOI={10.1609/aaai.v37i13.27058}, abstractNote={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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Alami, Reda and Hacid, Hakim and Bellone, Lorenzo and Barcis, Michal and Natalizio, Enrico}, year={2024}, month={Jul.}, pages={16398-16400} }