Abstract
With the increasing adoption of mobile robots for transporting components across several locations in industries, congestion problems appear if the movement of these robots is not correctly planned. This paper introduces a fleet management system where a central agent coordinates, plans, and supervises the fleet, mitigating the risk of deadlocks and addressing issues related to delays, deviations between the planned paths and reality, and delays in communication. The system uses the TEA* graph-based path planning algorithm to plan the paths of each agent. In conjunction with the TEA* algorithm, the concepts of supervision and graph-based environment representation are introduced. The system is based on ROS framework and allows each robot to maintain its autonomy, particularly in control and localization, while aligning its path with the plan from the central agent. The effectiveness of the proposed fleet manager is demonstrated in a real scenario where robots operate on a shop floor, showing its successful implementation.

Abstract
Autonomous Mobile Robots (AMRs) and Automated Guided Vehicles (AGVs) have emerged as key innovations in the industry world, with AMRs offering flexibility a nd adaptability for dynamic environments, while AGVs provide high accuracy for repetitive tasks; thus, this research proposes a study of fleets of both AGVs and AMRs to enhance productivity and efficiency in industrial settings. Several tests were performed where the duration of a mission, the success and collision rate, and the average number of disputes per mission were analyzed in order to obtain results. In conclusion, while AGVs tend to be more reliable and consistent in task completion, AMRs offer greater flexibility a nd speed.

Abstract
Fault tolerance (FT) is a critical aspect of industry, where systems are susceptible to disturbance and faults. Traditional FT models, based on the centralization of information to handle fault episodes, no longer meet the current industrial models based on Cyber-physical Systems (CPS). Self-healing is a promising approach for FT in CPS, consisting of the individual competence of each component in detect, diagnose and recover from failures. With this in mind, this paper discusses the engineering of self-healing fault-tolerance in industrial CPS, analyzing the maturation process of this paradigm from the local model through collaboration models and later to self-organization features. The paper also discusses the main research challenges that self-healing FT faces during this process.

Abstract
Cyber-Physical Systems (CPS) transform traditional systems into a network of connected and heterogeneous systems, integrating computational and physical elements, that works as a complex system whose overall properties are greater than the sum of its parts. However, CPS is not free from faulty episodes and their consequences such as malfunctions, breakdowns, and service interruption. Traditional centralized models for fault-tolerance do not meet the complexity of the current industrial scenarios and particularly the industrial CPS requirements. Having this in mind, this work presents a holonic-based architecture to address the fault-tolerance in CPS by distributing the detection, diagnosis, and recovery in the local individual entities and also considers the emergent behaviour resulting from the collaboration of these entities. An experimental case study is used to illustrate the potential application of the fault-tolerant approach. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Industrial Cyber-Physical Systems (ICPS) deploy a network of connected and heterogeneous systems, integrating computational and physical components, improving production and quality. However, a fault-free system is still utopian, but methodologies related to fault detection and diagnosis are still being treated in isolation or a centralized approach, overlooking the technological advances related to ICPS such as IoT, AI and edge computing. With this in mind, the present work proposes a collaborative architecture for fault detection and diagnosis, regarding the exchange of information for collaborative detection and diagnosis adopting disruptive technologies. Laboratory-scale ICPS experiments were carried out to compare the proposed approach with the approach where each component separately intends to identify and diagnose faults. The results present a faster response generating a system more flexible and robust. © 2022 IEEE.

Abstract
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.