Ricerc@Sapienza

Recurrent patrolling with unmanned vehicles is contributing to fight migrant smuggling and trafficking networks in the Mediterranean. It is also providing a valuable solution to the control and confinement of destructive wildfires all over the world, as well as rapid response to distribute resources, save lives and property. Multi-Agent Frequency-Based Patrolling is the act of travelling around an area, at regular intervals, to supervise it. As an optimization problem, it was addressed in several flavours in the literature. For this project, we focus on the frequency based patrolling problem in highly dynamic scenarios, where targets under supervision can move, change of priority and required visit frequency. We propose a solution rooted on a Markov Decision Processes (MDP), used to train a model with a Deep Reinforcement Learning (DRL) approach. The RL agent will be able to find an optimal visit strategy for the patrolling problem and communication of the observed critical events to a central base station. We propose to evaluate the performance of the whole protocol in a simulated environment to assess desirable metrics of interest, and validate the results obtained from the simulated campaign with a real field experiment.