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.

In this paper, Nonlinear Model Predictive Control (NMPC) and Nonlinear Moving Horizon Estimator (NMHE) are combined to control, in a distributed way, a heterogeneous fleet composed of a steering car and a quadcopter. In particular, the ground vehicle in the role of the leader communicates its one-step future position to the drone, which keeps the formation along the desired trajectory. Inequality constraints are introduced in a switching control fashion to the leader’s NMPC formulation to avoid obstacles. In the literature, few works using NMPC and NMHE deal with these two vehicles together. Moreover, the presented scheme can tackle noisy, partial, and missing measurements of the agents’ state. Results show that the ground car can avoid detected obstacles, keeping the tracking errors of both robots in the order of a few centimeters, thanks to trustworthy NMHE estimates and NMPC predictions.

Scheduling is a key component of manufacturing systems. In general, it describes how multiple jobs or workloads are processed within a limited set of resources to achieve certain manufacturing objectives, such as minimizing the makespan or weighted tardiness. Nevertheless, as the demand for customized and versatile products increased, these systems became more flexible by incorporating flexible manufacturing processes. As a result, it is possible to vary either the machine operations or the product route within highly automated and connected material handling systems. Therefore, the scheduling process is more complex as it involves solving the routing problem within different alternatives and sharing redundant resources between multiple production processes, thereby increasing workload and bottlenecks within the system. Thus, it is imperative to address the scheduling problem while taking into account the physical constraints caused by shared machines and product routing. This paper proposes a Mixed Integer Linear Programming algorithm to solve the flow-shop scheduling problem and minimize the makespan while balancing the machine usage to limit emergent behaviours due to over-utilization. The robustness of the approach is then verified in the case of delays which alter the processing time on machines. Experiments on a limited instance of a flexible manufacturing system confirm the effectiveness of the solution, with a small increase in completion time even for high-magnitude delays.

In a system of systems, a system can be modelled as a set of interacting subsystems. When each subsystem can be separately controlled, a distributed algorithm can be applied to manage the control. In this work, we propose a distributed approach, the Alternating Direction Method of Multipliers (ADMM), based on a distributed Linear Quadratic Regulator (LQR) approach to manage the achievement of a common goal cooperatively. The proposed approach can work with different agents with a limited observation of the system of systems state. The method is demonstrated on a natural system including four interconnected water tanks (quad tank system) whose level can be controlled by two pumps. The results show the behaviour of the proposed algorithm when the tanks have to reach a constant set point and when they must agree on a time-varying set point.

Wheeled mobile robots (WMRs) can navigate in uncontrolled environments with the assistance of electronic or physical devices. Several works have been conducted on the control and management of the path-tracking of a vehicle in different road scenarios. This paper aims to create an asynchronous finite state control law for a WMR. The control law is based on a proportional–integral–derivative controller, and the performance of the proposed model is evaluated in virtual and real environments in two different scenarios. In the first one, the WMR must perform a zig-zag maneuver between obstacles, while the second one involves a double left lane change. In the proposed scenarios, the WMR drives along a path until an obstacle is detected at less than 50 cm, causing the WMR to check whether the first lane is free to go and move on. These scenarios and the related required engineering approaches seem particularly suitable for system engineering in a student’s laboratory for the design and implementation of automated guidance system modeling.