This article explores the management of automated guided vehicles within smart manufacturing systems, presenting an architecture designed to handle various stages in a fully automated production system. Trajectory planning is managed using a potential field controller, coupled with a time-of-arrival optimization for vehicles heading to shared resources, exploiting the concepts of virtual platoons. This approach optimizes arrival times at resources and minimizes energy consumption, prioritizing other elements directed toward currently available resources within the system. In addition, a decision-making algorithm schedules vehicles needing recharging due to low battery levels, ensuring uninterrupted production without overloading recharging stations and guaranteeing system efficiency. The proposed architecture is tested on three production orders of increasing complexity. Results demonstrate that the use of virtual platooning simplifies the dynamic trajectory generation of the potential field controller and, consequently, significantly reduces energy consumption and waiting times at shared resources, while reducing the variability on the production chain's makespan.

Wheeled Mobile Robots (WMR) with the assistance of Information Communication Technology (ICT) can navigate and perform some tasks in an uncontrolled environment. They can experience some problems in task management if they can not localize themself in indoor/outdoor environments. Different algorithms can be exploited to evaluate the proper control law to track a default path. In this paper, a path-tracking algorithm for a WMR has been tested for an indoor environment, with a delimited area spanned by localization tags. The WMR must check some waypoints during its path until the last one, where the WMR re-start the simulation. In this work, the control law applied for the experiments is the Non-Linear Model Predictive Controller (NMPC). The control algorithm is tested using two experiments, the first one is based on the robot movements in a simulation environment using the Gazebo tools, and the second one is related to a real context where the mobile robot moves in an indoor environment.

Smart manufacturing systems combine collaborative and autonomous control systems to provide optimal performance and respond to volatile demands. A key component of smart manufacturing is material management, as raw materials, work-in-progress, and finished products must be transported to the right location at the right time. However, during the production phase, it is imperative to establish an optimal traffic control scheme in order to avoid congestion and optimize material flow. This paper proposes a platooning-based control procedure implemented in a virtual agent-based manufacturing environment to increase the material flow efficiency on a manufacturing shop floor. An analysis of the production makespan and an indicator of the level of congestion in the material handling system was conducted in order to determine productivity improvements. An agent-based simulation is used to test the effectiveness of the novel platooning approach versus a traditional basic approach. A multivariate analysis of variance (MANOVA) reveals that the inclusion of a platoon-based procedure improves production performance and reduces congestion risks.

Roundabouts pose complex challenges for autonomous vehicles. Approaching and crossing them safely requires a significant amount of information, much of which is typically unavailable. With autonomous vehicles becoming increasingly prevalent on the roads, new approaches are necessary to address these upcoming issues. While platoons and distributed control have been extensively studied in the past decade, roundabouts have received less attention. This paper presents a distributed Nonlinear Model Predictive Control (NMPC) approach using the Alternating Direction Method of Multipliers (ADMM) to utilize virtual platooning and enhance the throughput of a roundabout without requiring approaching vehicles to come to a stop. Instead, it manages the velocity of each vehicle while maintaining a safe distance. The proposed approach is validated through two case studies.

This paper introduces a comparison between a decentralized Proportional Integral Derivative (PID) controller and a centralized Linear Quadratic Tracking (LQT) controller to automatise the exchange of two inner vehicles inside a platoon moving on a straight path. Lomonossoff’s model is used to represent vehicle’s longitudinal dynamics. A case study is presented to demonstrate the effectiveness of both controllers respectively on nonlinear and linearized model.

This paper proposes a solution to dynamically adjust vehicle platoon trajectories. The goal of the control algorithm is to keep the optimal interdistance between adjacent vehicles proceeding at cruising speed on a straight road. After a proposal of the interdistance required between neighboring vehicles, a robust decentralized controller based on a linear control law provides the speed profile for each component of the platoon. Its objective is to minimize the divergence in space in respect to the planned trajectories while assuring a safe span between adjacent members of the platoon. The results on a limited instance demonstrate the effectiveness of the proposed approach.