With the emergence of Industry 4.0, comes an increasing need for multi-robot coordination and communication to efficiently complete joint tasks. A critical technology is the fifth generation (5G) mobile network, which enables cloud-controlled robots to execute tasks with differentiated quality-of-service (QoS) features. While there has been significant research on multi-robot planning, the integration with the capabilities of realistic network systems has been limited. In this paper, we introduce RoboPlan5G, a framework for 5G-aware robot planning. We propose a joint state-search model that includes task planning coordination in conjunction with 5G physical resource block (PRB) allocation. This process ensures efficient usage of the limited indoor 5G spectrum and effective service performance, while still aiming for the completion of tasks in the shortest possible time frame. Scenarios are generated in an Industry 4.0 environment, where the planner is shown to decrease the required 5G spectrum allocation by 50%, and on average improve the plan quality by 45% while maintaining a small computation time.

With the emergence of Industry 4.0, comes an increasing need for multi-robot coordination and communication to efficiently complete joint tasks. A critical technology is the fifth generation (5G) mobile network, which enables multiple robots to execute control tasks with differentiated quality-of-service (QoS) features. However, there has been limited analysis of the impact of real 5G capabilities on multi-agent robot planning problems. In this paper, we provide a review of robot planning algorithms suitable for industrial use-cases, which consider communication aspects in the planning formulation. The paper is further positioned to identify gaps in existing state of the art within communication-aware planning. This is followed by an analysis of key challenges to be targeted at the intersection of 5G, Industry 4.0 and multi-agent robot planning. This analysis is strategically important and would prove useful to academic researchers and industry experts focusing on deployment of robots in industrial settings.

A main challenge of using 5G network slices is to meet all the quality of service requirements of the slices (which are agreed with the customer in a service level agreement (SLA)), throughout the network slices' lifecycle. To avoid the penalty for violation, a proactive solution is presented, including predicting the SLA violation, explaining the violation cause, and then providing an adaptation to traffic. This work uses counterfactual (CF) explanations to 1) explain the main factors affecting the identified model's SLA violation prediction and 2) present modifications in the input values, which are required to configure the network traffic to avoid such a violation. We evaluate the CF explanation at two different levels where the generated CF instance is fed to the predictive model, and then actuation data are generated to evaluate the result in the real network. Our solution minimizes the violation up to 98%. This information can be utilized to reconfigure the system, either by humans or by the system automatically, to make it fully autonomous on the one hand and comply with the 'right to explanation' introduced by the European Union's General Data Protection Regulation on the other hand.

Edge computing is projected to have profound implications in the coming decades, proposed to provide solutions for applications such as augmented reality, predictive functionalities, and collaborative Cyber-Physical Systems (CPS). For such applications, edge computing addresses the new computational needs, as well as privacy, availability, and real-time constraints, by providing local high-performance computing capabilities to deal with the limitations and constraints of cloud and embedded systems. Edge computing is today driven by strong market forces stemming from IT/cloud, telecom, and networking—with corresponding multiple interpretations of “edge computing” (e.g., device edge, network edge, distributed cloud). Considering the strong drivers for edge computing and the relative novelty of the field, it becomes important to understand the specific requirements and characteristics of edge-based CPS, and to ensure that research is guided adequately, e.g., avoiding specific gaps. Our interests lie in the applications of edge computing as part of CPS, where several properties (or attributes) of trustworthiness, including safety, security, and predictability/availability, are of particular concern, each facing challenges for the introduction of edge-based CPS. We present the results of a systematic mapping study, a kind of systematic literature survey, investigating the use of edge computing for CPS with a special emphasis on trustworthiness. The main contributions of this study are a detailed description of the current research efforts in edge-based CPS and the identification and discussion of trends and research gaps. The results show that the main body of research in edge-based CPS only to a very limited extent consider key attributes of system trustworthiness, despite many efforts referring to critical CPS and applications like intelligent transportation. More research and industrial efforts will be needed on aspects of trustworthiness of future edge-based CPS including their experimental evaluation. Such research needs to consider the multiple interrelated attributes of trustworthiness including safety, security, and predictability, and new methodologies and architectures to address them. It is further important to provide bridges and collaboration between edge computing and CPS disciplines.

Artificial Intelligence (AI) is implemented in various applications of telecommunication domain, ranging from managing the network, controlling a specific hardware function, preventing a failure, or troubleshooting a problem till automating the network slice management in 5G. The greater levels of autonomy increase the need for explainability of the decisions made by AI so that humans can understand them (e.g. the underlying data evidence and causal reasoning) consequently enabling trust. This paper presents first, the application of multiple global and local explainability methods with the main purpose to analyze the root-cause of Service Level Agreement violation prediction in a 5G network slicing setup by identifying important features contributing to the decision. Second, it performs a comparative analysis of the applied methods to analyze explainability of the predicted violation. Further, the global explainability results are validated using statistical Causal Dataframe method in order to improve the identified cause of the problem and thus validating the explainability.

Autonomous Vehicles (AV) are expected to bring considerable benefits to society, such as traffic optimization and accidents reduction. They rely heavily on advances in many Artificial Intelligence (AI) approaches and techniques. However, while some researchers in this field believe AI is the core element to enhance safety, others believe AI imposes new challenges to assure the safety of these new AI-based systems and applications. In this non-convergent context, this paper presents a systematic literature review to paint a clear picture of the state of the art of the literature in AI on AV safety. Based on an initial sample of 4870 retrieved papers, 59 studies were selected as the result of the selection criteria detailed in the paper. The shortlisted studies were then mapped into six categories to answer the proposed research questions. An AV system model was proposed and applied to orient the discussions about the SLR findings. As a main result, we have reinforced our preliminary observation about the necessity of considering a serious safety agenda for the future studies on AI-based AV systems.

This paper addresses the problem of reducing the number of messages needed to exchange state updates between the Cyber-Physical System (CPS) components that integrate with the rest of the CPS through Digital Twins in order to maintain uniform communication interface and carry out their tasks correctly and safely. The main contribution is a proposed architecture and the discussion of its suitability to support correct execution of complex tasks across the CPS. A new State Event Filtering component is presented to provide event-based communication among Digital Twins that are based on the Linked Data principles while keeping the fan-out limited to ensure the scalability of the architecture.

Collaborative robotics is recently taking an ever-increasing role in modern industrial environments like manufacturing, warehouses, mining, agriculture and others. This trend introduces a number of advantages, such as increased productivity and efficiency, but also new issues, such as new risks and hazards due to the elimination of barriers between humans and robots. In this paper we present risk assessment for an automated warehouse use case in which mobile robots and humans collaborate in a shared workspace to deliver products from the shelves to the conveyor belts. We provide definitions of specific human roles and perform risk assessment of human-robot collaboration in these scenarios and identify a list of hazards using Hazard Operability (HAZOP). Further, we present safety recommendations that will be used in risk reduction phase. We develop a simulated warehouse environment using V-REP simulator. The robots use cameras for perception and dynamically generate scene graphs for semantic representations of their surroundings. We present our initial results on the generated scene graphs. This representation will be employed in the risk assessment process to enable the use of contextual information of the robot's perceived environment, which will be further used during risk evaluation and mitigation phases and then on robots' actuation when needed.

Autonomous vehicle systems within an intelligent transportation systems (ITS) paradigm have attracted continuously increasing interest from both academia and the industry. Safety is a key characteristic; without safety, the dream of autonomous vehicles cannot come true. This paper focuses on vehicle safety aspects and proposes a new conceptual framework, supported by a consolidated, international normative risk management process and is based on a safety-critical architecture brought from an analogous transportation domain, to design and to implement an autonomous supervision and control system (SCS). This SCS approach, based on automotive cyber physical systems (ACPSs) and called autonomous ACPS, aims to minimize the probability of vehicle collision hazard by employing resilient actions at run-time, reducing risks related to potential loss of human lives in the automotive transportation domain.

5G networks will be a key enabler for the Internet of Things by providing a platform to connect a massive number of devices with heterogeneous sets of network quality requirements. In this environment, 5G network operators will have to solve the complex challenge of managing network services for diverse customer sectors (such as automotive, health or energy) with different requirements throughout their lifecycle. In this paper, we present current state of our work on automating part of the network service lifecycle management using knowledge management- and decision support techniques. We also present our ongoing implementation steps for such management function in 5G networks.