International audience

Managing autonomous vehicles inside restricted areas for internal logistics purpose raises the question of safety. We deal here with this issue, and propose a Safe Shortest Path model, which we handle first through tree search in a static context, and next through learning techniques in a dynamic context.

International audience

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.