In this paper, autonomous vehicles are considered for addressing logistic operations in manufacturing systems. The starting idea consists in organizing a given group of autonomous robots/vehicles in a finite set of platoons in charge to accomplish prescribed job(s) within the manufacturing system. Three aspects are then needed to be formally outlined: task scheduling, routing decisions and command inputs computations. Here, a new distributed multi-layer architecture has been conceived by using three methodologies: timed colored Petri nets, deep reinforcement learning and model predictive control. Roughly speaking, timed colored Petri nets are exploited to formally model the manufacturing system so that an optimal scheduling task complying with the required jobs and the available vehicles is derived; then, run-time routing decisions are obtained by using a distributed reinforcement learning algorithm which exploits the available information provided by the vehicle sensor module; finally, the distributed model predictive control algorithm is built by resorting to a set-theoretic approach where most of the computations are off-line performed. A flexible manufacturing system consisting of four machines and a Load/Unload station is used for simulation purposes. Specifically, five jobs are hypothesized and some scenarios with an increasing number of available vehicles are simulated. In order to evaluate the benefits of the proposed approach, a time criterion based on the completion of all the jobs is considered with the aim to put in light that increasing the number of vehicles improves the control performance until congestion phenomena become unavoidable. Note to Practitioners—This paper proposes an innovative methodology for addressing the logistic operations within flexible manufacturing systems (FMSs) by using a team of autonomous ground vehicles. Unlike existing approaches, the core of this framework consists in combining along a hierarchical structure the capabilities of timed colored Petri nets and the deep reinforcement learning techniques to determine a near-optimal scheduling and run-time routing decisions that are provided to the distributed model predictive units in charge to accomplish the prescribed task. This multi-layer architecture has two main merits: a single platoon, completely disconnected from the others, is devoted to perform its own job; computational burdens are affordable during the on-line operations because most of the computations are moved in the off-line phase.

This paper presents a set-theoretic receding horizon control strategy for platoons of autonomous vehicles driving in smart cities context. In order to reduce traffic and$CO_{2}$emissions, we propose a path planer based on Deep Reinforcement Learning (DRL). The advantages of this solution is the ability to deal with the actual traffic congestion, while driving the autonomous vehicles to their destination and fulfilling the constraints. In particular, the high-level routing decisions are translated into set-points for the receding horizon controllers, making the control actions on the vehicle dynamics more computational efficient. In order to show the effectiveness of the overall architecture, a campaign of simulations on a platoon of eight vehicles, moving in the city center of Bologna in Italy, is provided.

In this paper, a sustainable routing algorithm for vehicle platoons operating in smart urban networks is presented. The proposed approach makes use of deep reinforcement learning (DRL) and set-theoretic model predictive control (MPC). In particular, the learning process aims at reducing traffic congestion and$CO_{2}$emissions, whereas the MPC unit allows to adequately track the assigned path by using real-time traffic data. To adequately analyze the performance of the resulting control architecture, the SUMO and MATLAB environments are used to implement complex operating scenarios where road maps data and vehicle state trajectories can be shared and exchanged. Finally, numerical studies are provided by resorting to the SUMO environment and considering a platoon of five vehicles. The resulting simulation campaign puts in light the capability of the training process to significantly mitigate the$CO_{2}$emissions of the whole platoon: from a minimum of 3.7 % to a maximum of 13% with respect to the use of the well-known Dijkstra algorithm.

In this paper, the path planning problem under routing decisions is considered for platoons of autonomous vehicles moving in urban road networks. The key idea is to exploit and adequately combine arguments coming from two research fields: deep reinforcement learning and model predictive control. Along these lines, a novel control architecture is proposed and its feasibility formally proved. In particular, the high-level routing decisions arising from the distributed deep reinforcement learning operations are translated into manipulable set-points for the underlying bank of receding horizon controllers by making more computational affordable and efficient the action on the vehicle dynamics. Finally, some simulations on platoon, consisting of three agents described by double-integrator models, are provided to show the effectiveness of the overall architecture.

This paper describes a formal approach to modelling and verification of self-adaptive real-time systems. Such systems can dynamically be affected by exception events either originated in the operational environment or in the internal status, which require to be dealt with through adaptation actions which have to fulfil timing constraints. The approach is based on Time Basic (TB) Petri nets, a formalism well-suited to the specification of time-critical systems. Although some specialcase tools have been developed to support the analysis of TB net models, the original contribution of this paper is an embedding of TB nets into the popular Uppaal toolbox based on timed automata, which makes it possible both non-deterministic exhaustive analysis by model checking and/or a quantitative analysis of model properties through statistical model checking. The paper demonstrates the application of TB net modelling and analysis through a self-healing time-critical system.

This paper proposes an agent-based control framework in JADE which allows the construction of control extensions tailored to the application needs. The approach is based on a minimal actor model which simplifies JADE agent programming. A catalog of reusable control forms, both concurrent/parallel and time-dependent (real time or simulation time are supported) was achieved. The paper introduces the control framework, clarifies its implementation status and demonstrates its practical use

Repast Simphony (RS) is a popular toolbox for agentbased modeling and simulation (ABMS) of complex systems. It can be used from within the Eclipse IDE with Java being the main implementation language. Moreover, visual modeling is supported by agent flowcharts. Powerful features of RS include contexts and projections which allow the modeler to build e.g. situated multi-agent systems (MAS) which can easily be configured and visualized in the RS runtime system. RS lacks of a reference agent methodology. Rather the modeler is free to define and follow her/his own methodology with RS: procedurally, declaratively or visual-based. This openness was exploited in this work for supporting different notions of agents, thus addressing the modeling needs of various application domains. In particular this paper proposes an embed in RS of an actor model which provides a lightweight notion of agents. The actor model is then used as a kernel for supporting more abstract but rigorous modeling languages like Parallel DEVS (P−DEVS) and time-extended Petri nets. A P−DEVS modeling example is reported to demonstrate the usefulness of supporting multiple agent methodological layers in RS.

The work presented in this paper concerns the development of a smartphone application for the monitoring of energy consumption relative to a domestic electricity grid. An algorithm has been integrated in the smartphone application allowing Enel (the main Italian Electrical Company) customers to have, on their mobile phone, clear and transparent information about the energy consumption of their houses in real time.

This paper proposes an original schedulability framework which is based on preemptive Time Petri Nets (pTPNs) and UPPAAL with stopwatches (UPPAALSW). The realization enables a real-time tasking set, along with precedence constraints in the form of data control, message passing etc., to be uniformly formalized using pTPNs and then analyzed through model checking using UPPAALSW in the presence of a reusable library of template processes modelling transitions of the source pTPNs specification and the scheduler algorithm which can be based on fixed priority or earliest deadline first. The paper first introduces and motivates the proposed approach by relating it to similar work described in literature, then summarizes the pTPNs formalism through a modelling example. After that the prototyped library in UPPAALSW is presented and put to work for model checking the chosen real-time tasking set. Analysis of models which depend e.g. on non deterministic execution times and sporadic arrival times of tasks, is conditioned by the use of an over approximation in the generation of the model state graph.

This paper describes an experience of porting the THEATRE agent architecture on top of the grid. The agent architecture consists of light-weight actors and computational theatres which have been proven to be well suited for modeling and simulation of complex systems. THEATRE nodes act as agencies that provide common services of message scheduling and dispatching to mobile actors. THEATRE is currently implemented in Java and can work with different transport layers and middleware. In the last years it was successfully interfaced to HLA/RTI, Terracotta, Java Sockets and Java RMI. The work described in this paper aims at experimenting with THEATRE over the grid, using in particular the Globus toolkit. The goal is to open THEATRE to the exploitation of virtual organizations of computing resources with secure communications, and to favor simulation interoperability through grid services. The paper summarizes THEATRE, describes a design and prototype implementation of THEATRE on top of the Globus Toolkit 4 (GT4), and demonstrates its practical use by means of a modeling example.

The minority game (MG) is a simple yet effective binary-decision model which is well suited to study the collec-tive emerging behaviour in a population of agents with bounded and inductive rationality when they have to compete, through adaptation, for scarce resources. The original formulation of the MG was inspired by the W.B. Arthur’s El Farol Bar problem in which a fixed num-ber of people have to independently decide each week whether to go to a bar having a limited capacity. A de-cision is only affected by information on the number of visitors who attended the bar in the past weeks. In its basic version, the MG does not contemplate communi-cation among players and it supposes that information about the past game outcomes is publicly available. This paper proposes the Dynamic Sociality Minority Game (DSMG), an original variant of the classic MG where (i) information about the outcome of the previously played game step is assumed to be known only by the agents that really attended the bar the previous week and (ii) a dynamically established acquaintance relationship is in-troduced to propagate such information among non at-tendant players. Particular game settings are identified which make DSMG able to exhibits a better coordina-tion level among players with respect to standard MG. Behavioral properties of the DSMG are thoroughly an-alyzed through an agent-based simulation of a simple road-traffic model.

synchronizers, timed automata, UPPAAL, Java. This paper describes the design and implementation of a library of reusable UPPAAL template processes which support reasoning and property checking of concurrent programs, e.g. to be realized in the Java programming language. The stimulus to the development of the library originated in the context of a systems programming undergraduate course. The library, though, can be of help to general practitioners of concurrent programming which nowadays are challenged to exploiting the potentials of modern multi-core architectures. The paper describes the library and demonstrates its usage to modelling and exhaustive verification of mutual exclusion and common concurrent structures and synchronizers. UPPAAL was chosen because it is a popular and continually improved toolbox based on timed automata and model checking and it is provided of a user-friendly graphical interface which proves very important for debugging and property assessment of concurrent models. Java was considered as target implementation language because of its diffusion among application developers.