Monitoring a Fleet of Autonomous Vehicles through A* like Algorithms and Reinforcement Learning - Université Clermont Auvergne Access content directly
Book Sections Year : 2022

Monitoring a Fleet of Autonomous Vehicles through A* like Algorithms and Reinforcement Learning

Abstract

We deal here with a fleet of autonomous vehicles which is required to perform internal logistics tasks inside some protected area. This fleet is supposed to be ruled by a hierarchical supervision architecture, which, at the top level distributes and schedules Pick up and Delivery tasks, and, at the lowest level, ensures safety at the crossroads and controls the trajectories. We focus here on the top level, while introducing a time dependent estimation of the risk induced by the traversal of any arc at a given time. We set a model, state some structural results, and design, in order to route and schedule the vehicles according to a well-fitted compromise between speed and risk, a bi-level algorithm and a A* algorithm which both relies on a reinforcement learning scheme
Fichier principal
Vignette du fichier
Monitoring_a_Fleet_of_Autonomous_Vehicles_through_A__like_Algorithms_and_Reinforcement_Learning.pdf (1000.01 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03901424 , version 1 (15-12-2022)

Identifiers

Cite

Mourad Baiou, Aurélien Mombelli, Alain Quilliot. Monitoring a Fleet of Autonomous Vehicles through A* like Algorithms and Reinforcement Learning. Recent Advances in Computational Optimization, 1044, Springer International Publishing, pp.111-133, 2022, Studies in Computational Intelligence, 978-3-031-06839-3. ⟨10.1007/978-3-031-06839-3_7⟩. ⟨hal-03901424⟩
12 View
14 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More