Cyber-physical systems (CPS) rapidly expand within industrial contexts in a new era of digitalization, processing power, and inter-device communication capabilities. These advancements integrate technologies such as the Internet of Things (IoT), artificial intelligence (AI), and cloud and edge computing, granting processes and operations a high degree of autonomy. In addition, these interconnections foster collective intelligence arising from information exchange and collaboration between components, often outperforming individual capabilities. This collective intelligence manifests in fault detection and diagnosis (FDD) tasks within CPS, as it significantly improves the flexibility, performance, and scalability. However, the inherent complexity of CPS poses challenges in determining the best configuration of the collaboration parameters, such as when and how to collaborate, wherein incorrect adjustments may lead to decision errors and compromise the system's performance. With this in mind, this paper proposes seven metrics to evaluate collaboration performance for fault detection and diagnosis in multi-agent systems (MAS)-based CPS, evaluating when the collaboration is beneficial or when the collaboration parameters need to be adjusted. The experiments focus on collaborative fault detection in temperature and humidity sensors within warehouse racks, where the proposed evaluation metrics point out the impact of collaboration on the detection task, as well as possible actions to be adopted to improve the agent's performance.

Industry 4.0 is re-shaping the way companies and individuals operate, but it is also introducing strong demands in education processes to train professionals with adequate competencies in emergent digital technologies, e.g., Internet of Things (IoT), Artificial Intelligence and collaborative robotics. In the last decade, innovative educational methods are being applied, e.g., problem-based learning and project-based learning, to move the traditional education approach into a more student-centric process where the student has a more active role. Recent studies point out that the combination of such educational methods is beneficial, each one selected according to the particularities of the learning subject and objective. Having this in mind, this paper describes the application of a learning methodology that combines different educational methods, namely face-to-face, problem-based learning and project-based learning, in a teaching course unit focusing on IoT technologies. The achieved results show an increase of the student’s assessment performance, motivation and satisfaction, and the opportunity to consolidate their acquired knowledge with hands-on practice. This approach also stimulates the acquisition of soft skills, mainly teamwork, communication, creativity and critical thinking.

The research, training, and learning in robotic systems is a difficult task for institutions that do not have an appropriate equipment infrastructure, mainly due to the high investment required to acquire these systems. Possible alternatives are the use of robotic simulation platforms and the creation of remote robotic environments available for different users. The use of the last option surpasses the former one in terms of the possibility to handle real robotic systems during the training process. However, technical challenges appear in the management of the supporting infrastructure to use the robotic systems, namely in terms of access, safety, security, communication, and programming aspects. Having this in mind, this paper presents an approach for the remote operation of real robotic manipulators under a virtual robotics laboratory. To this end, an open access and safe web-based platform was developed for the remote control of robotic manipulators, being validated through the remote control of a real UR3 manipulator. This platform contributes to the research and training in robotic systems among different research centers and educational institutions that have limited access to these technologies. Furthermore, students and researchers can use this educational tool that differs from traditional robotic simulators through a virtual experience that connects real manipulators worldwide through the Internet.

The advancement of technology and techniques applied to robotics contributes to increasing the quality of life and safety of humanity. One of the most widespread applications of mobile robotics is related to monitoring indoor environments. However, due to factors such as the size of the environment impacting the monitoring response, battery autonomy, and autonomous navigation in environments with unknown obstacles, they are still significant challenges in the diffusion of mobile robotics in these areas. Strategy adopting multiple robots can overcome these challenges. This work presents an approach to use multi-robots in hazardous environments with gas leakage to perform spatial mapping of the gas concentration. Obstacles arranged in the environment are unknown to robots, then a fuzzy control approach is used to avoid the collision. As a result of this paper, spatial mapping of an indoor environment was carried out with multi-robots that reactively react to unknown obstacles considering a point gas leak with Gaussian dispersion.

Modern industry demands techniques that ensure the operability of its processes, and even though the exponential technological advance in the last two decades in the manufacturing field, failures, waste, and unexpected interruptions are still present in this sector's daily routine. Within the Industry 4.0 context, fault-tolerant (FT) production systems remain a complex issue and sometimes represent a vulnerable aspect. Fault-tolerance techniques dedicated to autonomous and distributed systems, in a cyber-physical system (CPS) perspective, need to be investigated to follow the evolutionary pace of the manufacturing scenarios. This paper overviews these concepts and analyses the current situation in developing FT for CPS systems through a systematic literature review. The paper also discusses the research challenges in this new kind of FT systems due to new distributed architectures and emerging technologies, matching the several fault- tolerance phases.

Industry 4.0 promotes the use of emergent technologies, such as Internet of Things (IoT), Big Data, artificial intelligence (AI) and cloud computing, sustained by cyber-physical systems to reach smart factories. The idea is to decen-tralize the production systems and allow to reach monitoring, adaptation and optimization to be made in real time, based on the large amount of data available at shop floor that feed the use of machine learning techniques. This technological revolution will bring significant productivity gains, resources savings and reduced maintenance costs, as machines will have information to operate more efficiently, adaptable and following demand fluctuations. This paper discusses the application of supervised Machine Learning techniques allied with artificial vision, to implement an intelligent, collaborative and adaptive robotic inspection station, which carries out the quality control of Human Machine Interface (HMI) consoles, equipped with pressure buttons and LCD displays. Machine learning techniques were applied for the recognition of the operator's face, to classify the type of HMI console to be inspected, to classify the state condition of the pressure buttons and detect anomalies in the LCD displays. The developed solution reaches promising results, with almost 100% accuracy in the correct classification of the consoles and anomalies in the pressure buttons, and also high values in the detection of defects in the LCD displays.