The foundation for the Digital Product Passport (DPP) was presented in the recent European Ecodesign for Sustainable Products Regulation (ESPR) in July 2024. DPP systems will provide ICT support to increase the circular economy of products significantly. With the DPP, a significant step forward will be taken by providing comprehensive, trusted, and publicly accessible product information for items or batches regarding digital records. Currently, the DPP building is undergoing a standardization process. Nevertheless, commercial players are preparing DPP systems but mainly with a proprietary implementation, making it difficult for researchers to experiment. In this demo paper, we will show an open-source implementation of a DPP system for managing the lifecycle of second-hand electronic devices such as computers. Our system is oriented towards the use cases of economic actors such as repairers, refurbishers, and the customers of these devices. Our system integrates a blockchain-based registry for the actors to trust the data and operations. The implementation of the demo DPP system is publicly available and can be deployed as a set of microservices.
To strengthen the engineering education at the NORDAKADEMIE, a new collaborative robot laboratory has been developed. This laboratory should provide local access as well as remote access. This paper describes how the remote access is realised using the CrossLab architecture. For this, a Python client is used which implements Server Oriented Architecture Services provided by packages that were developed as part of the CrossLab project. In addition, the challenges and limitations found during implementation are discussed.
This paper introduces PlanARa, a multi-agent system designed for intelligent and adaptive scheduling in an industrial setting. The system leverages agent-based modeling and simulation to dynamically optimize resource allocation, production flows, and task execution while incorporating real-time adaptability to disruptions. The proposed system is designed for seamless integration into real-world manufacturing environments, facilitating dynamic and intelligent scheduling in real-time. Through a simulation-driven approach, the system enables risk-free scenario testing, allowing manufacturers to analyze different scheduling strategies before real-life deployment. Moreover, this system enhances real-time adaptability by dynamically adjusting schedules in response to unforeseen disruptions, thus improving system resilience. The successful implementation of such systems requires careful management of complexity, communication overhead, and integration challenges to ensure scalability and real world applicability. This work showcases how MAS can enhance scheduling efficiency, improve responsiveness to changing demands, and support human-machine collaboration in modern smart manufacturing ecosystems, driving greater flexibility, resilience and efficiency in industrial operations.
Awareness is a major safety factor in human- machine interaction environments, with worker accustomation to robot operation and consequential oblivion of their surroundings potentially having disastrous consequences for either agent or equipment. This is particularly relevant in collaborative scenarios, with workers and robots engaging in close proximity. While consideration for safety is an inherent, albeit forgettable trait of human workers, robots are yet incapable of making such judgements. This translates to collaborative robots typically integrating overt shutdown procedures activated if and when danger is detected for workers, tying with production and other equipment damage issues. Moreover, human focus is both intrinsically tied with safety judgement and manifestable in variable bio-signalling, of which some can be captured via brain-computer interface technologies. This short paper advocates for the evaluation of worker bio-signalling to avert danger, by instead adapting robot operation. It presents the monitoring of human attention as a by-product of single channel electroencephalography waves, to modulate robot functioning to complete its task, track its human colleague while they do their part, or halt. Implications and limitations are pondered for usage in real-life industrial scenarios.
The Remote FPGA Motion Lab is a remotely accessible platform designed for hands-on experimentation in digital systems design and embedded control. It is based on the FPGA Motion Lab, a physical experimental setup for FPGAbased system development. Through LabsLand LabDiscovery Engine, it offers a scalable Remote Laboratory Management System (RLMS), managing user authentication, session queuing, and concurrent experiment instances. The interface allows cloud-based VHDL synthesis or bitstream upload, providing full control over motors, sensors, and GPIOs. The Remote FPGA Motion Lab will be tested in the 2024-2025 academic year for the Programmable Logic course at University of Deusto.
