Lithium-ion batteries represent a pivotal component within contemporary energy storage solutions, exhibiting a diverse range of applications spanning from consumer electronics to electric vehicles and renewable energy systems. Nevertheless, the progressive degradation of these batteries, resulting in a reduction in capacity and performance, poses significant challenges in terms of system safety and reliability. In this context, the evaluation of the Remaining Useful Life (RUL) plays a central role in assessing the health of lithium-ion batteries. Ensuring precise and reliable RUL prediction is critical for the proper operation of a system. In this work, to address these challenges, a novel lightweight deep learning approach has been proposed for battery RUL estimation, by using voltage and current data. The proposed model is an approach based on the Echo State Networks (ESNs), which is compared to conventional deep learning models, such as Long Short-Term Memory (LSTM) networks, which re-quire more complex architectures and substantial computational resources. The ESN-based model demonstrates a comparable predictive capacity, while substantially reducing training and inference times. The model was tested with the CALCE dataset, focused on data obtained during charge and discharge cycles of lithium-ions batteries. Specifically, under the test conditions of 1 C discharge, the ESN requires only 0.3 seconds for training and approximately 0.06 seconds for inference, thus offering a computational advantage over the LSTM model, which requires 384 seconds for training and approximately 0.19 seconds for inference with the same hardware.

Route planning is a fundamental function for autonomous vehicles (AVs) tasked with navigating complex road networks. Traditional formulations of the route planning problem typically assume a single destination, where the solution is defined as the optimal path from the starting point to the designated goal. In this paper, we introduce a two-stage hierarchical route planning algorithm designed to determine a feasible and optimal route that sequentially connects multiple target points within a road network. Our approach employs a graph-based representation of the road network and systematically integrates global and local search strategies to guarantee both the feasibility and minimality of the resulting route. The proposed approach also introduces a semantic representation of the planned route, providing natural language indications and specifying the planned behaviour of the car. This is accomplished by classifying the discrete points that define the planned route. The validity of the proposed approach was tested experimentally on a 1:10 scale autonomous vehicle.

In order to successfully integrate circular economy principles into current value chains, it is crucial to ensure the economic sustainability of disassembly processes. Recent scientific and technological innovations, including Large Language Models (LLMs) in artificial intelligence (AI), are greatly accelerating progress in enhancing robotic capabilities. Dismantling is the first step in the re-manufacturing, repair, and recycling processes of end-of-life (EoL) products. Traditionally, this operation is performed manually by operators or by expensive dedicated robotic cells. Although manual disassembly offers flexibility in handling complex situations, it is a time-consuming and labour-intensive process that can negatively affect the health of operators and the cost-effectiveness of the disassembly process. However, robot disassembly presents difficulties in handling complex parts in a flexible manner. Robot application emerges as an automated versatile solution, capable of handling uncertainties in the frequency, quantity, and quality of end-of-life products. A fully automated approach for the removal of clothing zips from garments is proposed. The approach detects the pixels corresponding to the zip using LLM-Based AI techniques to plan the path of the robotic arm. The solution reduces the effort of AI training in industrial applications. Simulations validate the approach and its performance.

Advances in machine learning (ML) have opened up new opportunities to include decision-making capabilities in Internet of Things (IoT) nodes. This opportunity is complex to address, since conventional ML implementations are computationally intensive, thus reducing the possibility of implementing them on resource-constrained systems. In addition, when nodes in a network grow significantly, centralized data processing creates new challenges such as latency, resource efficiency, privacy, bandwidth, etc. Aiming to simultaneously address the above challenges, this paper presents a novel efficient distributed learning strategy for ESN-Based model, that is conceived for training and inference to incorporate part of the model while reducing the sharing of private information. The experiment setup was conducted using Appliances Energy Prediction dataset to forecast energy consumption under different environmental and usage conditions, using simulated IoT devices. The numerical results show that the proposed solution performs well compared to a classical centralized ESN-based approach without sacrificing too much performance at very low computational costs.

The co-transport (collaborative-transport) of deformable materials such as fabrics, composite materials, cables or wires and so on, is a challenging task for robotic applications in industry. The main difficulty lies in the deformability of the material, which can slide, stretch and deform during handling and transport. Common approaches in the literature either take advantage of force sensors to act on the fabric and restore its ideal state, or estimate the deformation state of the fabric with depth images and neural networks (NN). In both cases, issues may arise regarding the effects of force control on the material and the industrial reliability of the NNs, respectively. This paper proposes a method based on the estimation of the deformability constraints of the flexible material to obtain geometric parameters that allow planning the trajectory of a collaborative manipulator for co-transport that guarantees the deformation constraints during transport. By identifying a representative point on the side of the fabric held by the man, the action necessary to restore the desired deformation of the fabric that is initially estimated is planned. Once the material assumes the desired state, the robot's movements are generated to track the human's movement and act in safety conditions. A near-time-optimal control strategy is proposed. Finally, the system is tested in a simulation scenario.

In modern industrial manufacturing, simulation and accurate forecasting of energy consumption are critical to optimise resources and reduce waste meeting the significant energy demands and greenhouse gas emissions of this sector. Industry 5.0 (15.0), focused on sustainability and human-centered manufacturing, emphasises the use of Artificial Intelligence (AI), the Industrial Internet of Things (1IoT) and the Cyber Physical Systems (CPS) to monitor and optimise resource use in real time. This paper proposes a novel energy consumption simulation method using Neural Network (NN), specifically the Deep Echo State Network (DeepESN), integrated with a text embedding model, which allows to combine energy consumption data into production scheduling. Unlike existing approaches, our method considers both machine-level energy consumption and production workflows, enabling comprehensive optimisation of energy effi-ciency. Preliminary tests in a real production scenario show the potential of the approach, highlighting its ability to predict energy consumption simply by using smart meters without additional hardware. This work represents a significant advancement in in-tegrating energy consumption modeling into production planning and contributes to more sustainable industrial practices.

The advent of increasingly flexible and adaptive production processes will require equally flexible robotic collaboration. The forthcoming robotic applications must be able to self-adapt to different applications and scenarios and to handle frequent interaction with humans and dynamic environments. Environment perception, object recognition, and trajectory re-planning in a dynamic and changing environment, even with possible human interaction, are features that are not yet all simultaneously available in collaborative robots, and certainly not in industrial robots. This paper proposes some initial results of the authors’ ongoing research on the development of a ROS2-based framework for industrial applications due to which a robotic manipulator equipped with a depth camera is able to have simultaneously perception, recognition and re-planning capabilities in a dynamic and changing environment. This framework is potentially applicable to various industrial robots and depth cameras; here, the first results of the experimental implementation on an Omron TM5-900 collaborative robot equipped with a fixed depth camera will be shown.

The desired path following for an autonomous vehicle can be achieved by ensuring the desired yaw rate response. In conventional methods the control of lateral vehicle dynamics by means of yaw rate-based PID controller has sometimes been investigated but rarely implemented. An autonomous driving system can therefore be achieved by designing a reliable yaw rate controller for the vehicle. In this paper, an autonomous vehicle control architecture and a steering angle PID controller based on the vehicle’s yaw rate measured by a gyroscope is proposed and designed to handle most of the manoeuvres of an autonomous vehicle. It does not require measurements of lateral acceleration and lateral speed. The yaw rate reference is provided as an output by a higher-level system and used in the lower-level control loop. The validity of the proposed approach was tested experimentally on a 1:10 scale autonomous vehicle.

This paper focuses on the study and development of learning algorithms oriented to wear classification and predictive maintenance (PdM) of the cutting tool (CT) of a clamping machine for producing structural steel bars. While several works dedicated to CTs for turning and milling operations, or in general for metal removal operations, also known as subtractive manufacturing processes, can be found in the literature, the phenomena related to cutting with a cutting knife have not been widely treated in the literature. This article intends to focus on the analysis of the latter problem. The objective is to estimate the wear of the CT, a critical component of the steel bar cutting machine. The SVM classifiers were therefore used to classify the wear. For the predictive maintenance purpose, two algorithms were implemented for the prediction of the remaining service life, based on the Degradation Model and the Similarity Model respectively; in the first method, a prediction and state update function were used, while in the second method, a Long Short-Term Memory (LSTM) Neural Network (NN) was used.

The present paper proposes an architecture for a product process monitoring system suitable for SMEs (Small-Medium Enterprises). The monitoring system is the main means by which decision-making systems based on intelligent automation technologies are aware of the state of the system on which they will take decisions. Methods and tools from best-practice and best-effort approaches are proposed in the context of SMEs, where the requirements of low cost, low initial level of digitalization and high production flexibility often coexist and contribute to the complexity of management and control problems in these companies. The paper focuses on the design of the monitoring system using an OSGi framework to meet industry standards and Industry 4.0 requirements, taking into account the peculiarities of SMEs as design constraints. The proposed architecture was first tested using a simulation tool and then implemented on a full-scale production line used for data collection.

Innovative technologies and comfort-energy efficiency measures are nowadays well known and widely spread, and the main issue is to identify those that will be proven to be the more effective and reliable in the long term. With such a variety of proposed measures, the decision-maker has to compensate environmental, energy, financial and social factors to reach the best possible solution that will ensure the maximization of the comfort and energy efficiency of smart buildings satisfying at the same time the building's final user/occupant/owner needs. A work-in-progress methodological position investigates the feasibility of applying new techniques to improve the comfort and energy efficiency in smart buildings so that the maximum possible number of alternative solutions and energy efficiency measures may be considered. A simple example is used to identify the potential strengths and weaknesses of the proposed approach and highlight potential problems that may arise.

The field of Permanent Magnet Synchronous Motors (PMSMs) diagnosis is of research interest because widely used both in the Industrial environment and in electric vehicles. Amongst various Fault Detection (FD) techniques, the Motor Current Signature Analysis (MCSA) received lots of attention because some defecting frequencies may be monitored through the motor currents in case of steady-state functioning. This latter assumption is not always fulfilled, such e.g. in robotic systems driven by PMSMs, where constant speed assumption is unrealistic in most of the cases. Furthermore, MCSA in not suitable for systems working under non-stationary conditions without using advanced processing techniques. This work investigates the use of load torque information for motor diagnostic purposes under not constant speed assumption. Simulations and experimental results are presented regarding the use of the proposed Motor Torque Analysis (MTA) to overcome these limits.

This paper presents current research on automated synthesis in the context of the Holonic Management Tree (HMT) technique. HMT has been currently challenged in robotics, manufacturing, construction, but in general this technique concerns the management of complexity that emerges in cyber-physical systems context. Although effective, the technique lacks a systematic and possibly automatic process of construction of the HMT, which impinges with artificial and extended intelligence concerns. A methodological work-in-progress position for the automation of HMT synthesis is proposed for the first time, and as a first step for an enduring but disruptive research effort.

PMSMs are widely used in applications on electric vehicles, robotics and mechatronic systems of industrial machinery. Thus it becomes increasingly interesting to prevent their fault or malfunctioning with Predictive Maintenance (PdM). However, reaching this outcome could be difficult, especially if the stationary condition is not achieved and without additional sensors. This paper examines the use of a load torque observer based on Extended Kalman Filter for the diagnosis of electric drives working under non-stationary conditions. The proposed Motor Torque Analysis (MTA) is compared with the Motor Current Signature Analysis by evaluating their diagnostic capabilities under the assumed conditions. Finally, the results of bearing failure detection under non-stationary conditions are presented, highlighting the superior diagnostic capabilities of the MTA under such conditions.

Infrastructure systems strongly influence contemporary society and increasingly our quality of life depends on it, but unfortunately these infrastructures are ageing and failures are becoming common. Automate maintenance procedures can be a real solution to cope with these problems but several limits have to be overcome yet. In this work, these limits are faced by proposing an alternative UAV that would bring advantages in safe inspection tasks and human-interaction, named Double Propeller Ducted-Fan. An alternative UAV's architecture jointly with a dynamic model and a simplified linear control scheme is here proposed, by taking into account the requirements demand to further commercial development. The validity of the model and the proposed control scheme are evaluated in simulations, also taking into account real disturbances and noise from sensors. The results are encouraging in term of performances, compared with tests carried out with linear controllers in similar UAVs.

In Soft Real-Time (SRT) or Hard Real-Time (HRT) applications, the behaviour of the computing system must be predictable at design phase because it impacts on the design itself. Nowadays, hardware architectures are able to provide higher performance and guarantee temporal determinism but few works investigate the computational effort required by control algorithms implementation. This preliminary work aims to be a first step towards a novel analysis of performances and Worst-Case Execution Time (WCET) which can be useful to compare different control techniques and for evaluating also the complexity of the algorithms. To this purpose, a mobile robot consisting of motorized cart-pendulum has been built and it has been equipped with an HRT hardware platform where a nonlinear State Dependent Riccati Equation (SDRE) technique has been implemented.

This paper discusses the problems of material and information flow in a complex product manufacturing process. It presents an ICT solution that augments Kanbans in order to enable flexible and pervasive control on state and delivery of material on workstations. The paper deliberates methods and tools that can be possibly applied in such production systems. The best-practices and modern technologies are taken into consideration in the context of small-medium enterprises (SMEs). It is assumed that important factors and constraints are: low costs, low instrumentations, and high flexibility. The complexity of products and the manufacturing systems makes the management and control process even more difficult. The deployment of distributed intelligence and decision support systems can facilitate the compliance with industrial standards and the migration towards the automation levels required by industry 4.0 also in SMEs.

Predictive Maintenance (PdM) is one of the key enabling technologies in Industry 4.0. The Factories of the Future will adopt highly automated and interconnected environment where predictive fault detection will have an essential role to ensure efficient and reliable industrial operations. Due to their high efficiency and their low cost Cartesian Robots (CRs) represent one of the widely used automation systems in industry. Their movements and efficiency depends on transmission system and its degradation. However not much has been done in terms of PdM for these robots and very few works tries to deal with this problems. Different failures for those kind of robots are attributable to the transmission system. This work details the effect of the transmission system on the robot electrical actuation according to Motor Current Signal Analysis (MCSA) theory. This analysis propose different tools, used in others disciplines for different purposes, to infer features of the faulty condition. By monitoring the motor current of the CR, after a signal preprocessing, a proper fault index have been investigated in order to detect the functionality state of the transmission system. The preliminary results obtained are encouraging compared to classic spectral analysis. The monitoring and analysis have also been extended to the transient state. All the fault detection tests have been carried out directly on the electric drive mounted on a real industrial CR.

Autonomic computing initiative aimed to develop computer systems capable of self-management in order to overcome the rapidly growing complexity of computing systems management. Similar complexity affects the management and control of the systems in the industry of the future that currently relies on the advancements in cyber-physical systems frameworks. With this position paper, the RMAS architecture is checked against the major properties of autonomic systems. RMAS is proposed as a methodological and technological platform to reduce the barriers that complexity poses to further growth in intelligent automation and control.

This work extends a performance metrics method for the treatability of some classes of problems in manufacturing automation that can be represented as a system-of-systems controlled by a cyber-physical infrastructure. With the use of proper distributed and recursive computing approaches, the complexity of the control of cyber-physical systems can be attacked through a unified and human-centered simple framework that complies with the forthcoming pervasive computing challenges posed by the smart manufacturing scenarios. The aim of this work is to provide the proof of concept of an effective methodology that relies on the decomposition of a production goal into a hierarchical self-similar structure of subgoals for the steering of the system toward improved effectiveness. An implementation of the technique is proposed by means of multiactor and multidatabase paradigms. The simulation of implementation and experimental deployment on low cost embedded device is provided.

This paper gives a novel perspective about the role of the self-similarities in the modelling of system of systems with cyber-physical representation. With the adoption of virtual overlays of dynamically self-adapting holarchies, the emergent characteristics and behaviors of the information and automation systems are mapped into a recursive tree of autonomic components that can be typically realized through holonic multiagent systems. The nested computing structures that are used to achieve a productivity goal are kept simple by means of self-similarities. This simplification provides a viable methodology for the pervasive management and the automated programming of the holonic components. The technique described is suitable for a prompt adoption on a vast class of manufacturing, robotics, mechatronics, and facility management processes, whilst enabling a continuum between humans and machines. An advantage of the proposed method is its agnosticism with respect to most of the specific but heterogeneous technological means adopted in the already existent automation realizations.

Although an industrial robot represents a higher class of machine, it is a part of a production system. The robot, regardless of its own complexity, can be represented by a set of actions that leads itself towards the achievement of production goals. In the present paper the robot actions will be modeled as a production system in order to manage and monitoring its actions toward a production target. This will allow an easier integration of the robot within the production process, as required by the new paradigms for the factories of the future.

This work reviews and recaps some lessons learnt in the industrial cyber-physical systems playground to indicate a viable path towards an effective use of artificial intelligence in distributed automation. The holon concept is used as a pivotal tool towards a simplification of the design and implementation of architectures that exploit recursive patterns and self-similarities to keep the industrial systems-of-systems problem under control, whilst addressing the consistent coupling between the physical environmental and the reasoning processes. The Beer’s viable system model is used as an inspiration towards new viable holistic visions of the production system-of-systems.

The present paper proposes a conceptual framework along with a practical solution for the problem of integrating the performance indicators of production processes with the capabilities of robotic systems and machinery. This will enable technology transfer and development of automated production processes within the new vision of industry 4.0. The proposed methodology is a solution for improving the performance of a manufacturing system that integrates robotics, mechatronics and automation systems at different levels. Performance measurements and bottlenecks detection on critical production paths are performed in order to achieve a dynamic and on-line system improvement. This consists of a pervasive control of the effectiveness of the system-of-systems, by means of a well-defined sequence of specific and minimal corrective actions. The proposed technique is advisable in the complex scenario of decentralized manufacturing. The nature of the solution is context-dependent and scalable.

The complexity of cyber-physical systems (CPSs) poses new challenges in their design, model checking and maintenance. The hardware and software designers are in search, more than ever, for simple and interoperable approaches that render the complexity of CPSs a treatable matter. In this work, database language is suggested as an enabling technology and a lean technique to the purpose. An example with best available embedded database technology is conducted by means of a deployment test on tiny embedded electronics.

The present paper proposes an effective metrics for production efficiency and a bottleneck detection algorithm of recursive nature for its application on lightweight embedded systems on board of the robotics and automation components of the factory of the future. The proposed methodology is particularly suited if the fractal paradigm is applied to the factory seen as a complex system of systems but with relevant self-similarities across the several layers of components and structures from the shop-floor up to the enterprise level. A performance test has been conducted to demonstrate the viability of the technology for tiny embedded devices with the use of declarative embedded database language. Due to the high scalability of the algorithm and its simplicity, it seems suitable also for the robotic cloud paradigm, where constituent mechatronics, sensors and actuators components are provided as a service. The results provided suggests that, with the use of similar recursive and distributed form of computing, production bottlenecks or fault detection can be scaled to address the complex and pervasive cyber-physical systems problems that characterize the 4th industrial revolution strategies.

This paper explains how to associate a rigorous probability value to the main straight line features extracted from a digital image. A Bayesian approach to the Hough Transform (HT) is considered. Under general conditions, it is shown that a probability measure is associated to each line extracted from the HT. The proposed method increments the HT accumulator in a probabilistic way: first calculating the uncertainty of each edge point in the image and then using a Bayesian probabilistic scheme for fusing the probability of each edge point and calculating the line feature probability.