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Autonomous machines are expected to be vastly used at construction sites as they can efficiently perform repetitive and dangerous tasks. However, ensuring the operational safety of such autonomous machines in a highly dynamic environment is challenging. Although autonomous machines usually are equipped with a perception system that permits them to navigate locally, there is a need to share a global view of the construction site to reduce the risk of accidents or errors. A digital twin of the construction site map has the potential of fusing the real-time perception from different sources at the site, such as different autonomous machines working at the construction site, analysing them and sharing the needed information to operate safely and effectively at the site. This paper proposes the adoption of the recently published standard, ISO 23247 digital twin framework for manufacturing, to implement and maintain a dynamic map of construction sites. The proposed framework will enable safe and efficient operation of autonomous machines on construction sites.

Artificial Intelligence (AI) has proven effective in assisting manufacturing companies to achieve Zero Defect Manufacturing. However, certain products may have quality characteristics that are challenging to verify in a manufacturing facility. This could be due to several factors, including the product’s complexity, a lack of available data or information, or the need for specialized testing or analysis. Prior research on using AI for challenging quality detection is limited. Therefore, the purpose of this article is to identify the enablers that contributed to the development of an AI-based defect detection approach in an industrial setting. A case study was conducted at a transmission axle assembly factory where an end-of-line defect detection test was being developed with the help of vibration sensors. This study demonstrates that it was possible to rapidly acquire domain expertise by experimenting, which contributed to the identification of important features to characterize defects. A regression model simulating the normal vibration behavior of transmission axles was created and could be used to detect anomalies by evaluating the deviation of new products compared to the model. The approach could be validated by creating an axle with a built-in defect. Five enablers were considered key to this development.

The Zero Defect Manufacturing (ZDM) concept combined with Artificial Intelligence (AI), a key enabling technology, opens up new opportunities for improved quality management and advanced problem-solving. However, there is a lack of applied research in industrial plants that would allow for the widespread deployment of this framework. Thus, the purpose of this article was to apply AI in an industrial application in order to develop application insights and identify the necessary prerequisites for achieving ZDM. A case study was done at a Swedish manufacturing plant to evaluate the implementation of a defect-detection strategy on products prone to misclassification and on an imbalanced data set with very few defects. A semi-supervised learning approach was used to learn which vibration properties differentiate confirmed defects from approved products. This method enabled the calculation of a defect similarity ratio that was used to predict how similar newly manufactured products are to defective products. This study identified four prerequisites and four insights critical for the development of an AI solution supporting ZDM. The key finding demonstrates how well traditional and innovative quality methods complement one another. The results highlight the importance of starting data science projects quickly to ensure data quality and allow a ZDM detection strategy to build knowledge to allow for the development of more proactive strategies, such as the prediction and prevention of defects. 

Construction sites are a special kind of off-road environment that needs dedicated dynamic maps to enable autonomous navigation in such terrains. In this paper, challenges for autonomous navigation on construction sites are first identified. Later, requirements for dynamic maps for autonomous navigation on construction sites are proposed based on the identified challenges.

This article investigates the possibility of advancing discrete manufacturing using system architectures that are developed for Collaborative Process Automation Systems. Collaborative Process Automation System is a technology that has the potential to achieve production excellence for process industry. However, not much attention has been paid on using the architectures based on Collaborative Process Automation Systems for discrete manufacturing domains. In this article, we propose a base architecture consisting of three layers, and we discuss various alternatives to make the communications among the layers. We consider legacy components in the proposal, in contrast to most of the related works. In order to show the practicality of the proposed alternatives, we present an example that has been implemented in an ongoing project at ABB Robotics in Sweden.

Fixed-priority preemption-threshold scheduling (FPTS) is a generalization of fixed-priority preemptive scheduling (FPPS) and fixed-priority non-preemptive scheduling (FPNS). Since FPPS and FPNS are incomparable in terms of potential schedulability, FPTS has the advantage that it can schedule any task set schedulable by FPPS or FPNS and some that are not schedulable by either. FPTS is based on the idea that each task is assigned a priority and a preemption threshold. While tasks are admitted into the system according to their priorities, they can only be preempted by tasks that have priority higher than the preemption threshold.

This paper presents a new optimal priority and preemption threshold assignment (OPTA) algorithm for FPTS which in general outperforms the existing algorithms in terms of the size of the explored state-space and the total number of worst case response time calculations performed. The algorithm is based on back-tracking, i.e. it traverses the space of potential priorities and preemption thresholds, while pruning infeasible paths, and returns the first assignment deemed schedulable.

We present the evaluation results where we compare the complexity of the new algorithm with the existing one. We show that the new algorithm significantly reduces the time needed to find a solution. Through a comparative evaluation, we show the improvements that can be achieved in terms of schedulability ratio by our OPTA compared to a deadline monotonic priority assignment.

The flexible spin-lock model (FSLM) unifies suspension-based and spin-based resource access protocols for partitioned fixed-priority preemptive scheduling based real-time multi-core platforms. Recent work has been done in defining the protocol for FSLM, providing schedulability analysis, and investigating the practical consequences of the theoretical model. FSLM complies to the AUTOSAR standard for the automotive industry, and prototype implementations of FSLM in the OSEK/VDX-complaint Erika Enterprise Real-Time Operating System have been realized. In this paper, we briefly describe some practical challenges to improve efficiency and generality. 

Recently, the flexible spin-lock model (FSLM) has been introduced, unifying spin-based and suspension-based resource sharing protocols for real-time multi-core platforms. Unlike the multiprocessor stack resource policy (MSRP), FSLM doesn’t allow tasks on a core to share a single stack, however. In this paper, we present a hypothesis claiming that for a restricted range of spin-lock priorities, FSLM requires only two stacks. We briefly describe our implementation of a dual stack for FSLM in the Erika Enterprise RTOS as instantiated on an Altera Nios II platform using 4 soft-core processors.

As the world enters the information era, more and more dependable services controlling and even making our decisions are moved to the ubiquitous smart devices. While various standards are in place to impose the societal ethical norms on decision-making of those devices, the rights of the individuals to satisfy their own moral norms are not addressed with the same scrutiny. Hence, the right of the individuals to reason on their own and evaluate morality of certain decisions is at stake, as many decisions are outsourced from the user to the service providers and third party stakeholders without the user's full awareness of all the aspects of those decisions. In this work we propose an agent-centred approach for assuring ethics in dependable technological service systems. We build upon assurance of safety and security and propose the notion of ethics assurance case as a way to assure that individual users have been made aware of all the ethically challenging decisions that might be performed or enabled by the service provider. We propose a framework for identifying and categorising ethically challenging decisions, and documenting the ethics assurance case. We apply the framework on an illustrative example.

Support for exclusive access to shared (global) resources is instrumental in the context of embedded real-time multi-core systems, and mechanisms for achieving such access must be deterministic and efficient. There exist two traditional approaches for multiprocessors when a task requests a global resource that is locked by a task on a remote core: a spin-based approach, i.e. non-preemptive busy waiting for the resource to become available, and a suspension-based approach, i.e. the task relinquishes the processor. A suspension-based approach can be viewed as a spin-based approach where the lowest priority on a core is used during spinning, similar to a non-preemptive spin-based approach where the highest priority on a core is used. By taking such a view, we previously provided a general model for spinning, where any arbitrary priority can be used for spinning, i.e. from the lowest to the highest priority on a core. Targeting partitioned fixed-priority preemptive scheduled multiprocessors and spin-based approaches that use a fixed priority for spinning per core for all tasks, we aim at increasing the schedulability of multiprocessor systems by using the spin-lock priority per core as parameter. In this paper, we present (i) a generalization of the traditional worst-case response-time analysis for non-preemptive spin-based approaches addressing an arbitrary but fixed spin-lock priority per core, (ii) an optimal spin-lock priority assignment (OSPA) algorithm per core, i.e. an algorithm that will find a fixed spin-lock priority per core that will make the system schedulable, whenever such an assignment exists and, (iii) comparative evaluations of the OSPA algorithm with the spin-based and suspension-based approaches where OSPA showed up to 38% improvement compared to both approaches.