Högskolan i Skövde

With robotics applications playing an increasingly significant role in our daily lives, it is crucial to develop effective methods for teaching and understanding their behavior. However, limited access to physical robots in educational institutions and companies poses a significant obstacle for many individuals. To overcome this barrier, a novel framework that combines realistic robot simulation and intuitive control mechanisms within a virtual reality environment is presented. By accurately emulating the physical characteristics and behaviors of various robots, this framework offers an immersive and authentic learning experience. Through an intuitive control interface, users can interact naturally with virtual robots, facilitating the acquisition of practical robotics skills. In this study, a qualitative assessment to evaluate the effectiveness and user satisfaction with the framework is conducted. The results highlighted its usability, realism, and educational value. Specifically, the framework bridges the gap between theoretical knowledge and practical application in robotics, enabling users to gain hands-on experience and develop a deeper understanding of robot behavior and control strategies. Compared to existing approaches, the framework provides a more accessible and effective alternative for interacting with robots, particularly for individuals with limited physical access to such devices. In conclusion, the study presents a comprehensive framework that leverages virtual reality technology to enhance the learning and training process in robotics. By combining realistic simulations and intuitive controls, this framework represents a significant advancement in providing an immersive and effective learning environment. The positive user feedback obtained from the study reinforces the value and potential of the framework in facilitating the acquisition of essential robotics skills. Ultimately, this work contributes to flattening the robotics learning curve and promoting broader access to robotics education. 

In the digitalisation era, overlaying digital, contextualised information on top of the physical world is essential for an efficient operation. Mixed reality (MR) is a technology designed for this purpose, and it is considered one of the critical drivers of Industry 4.0. This technology has proven to have multiple benefits in the manufacturing area, including improving flexibility, efficacy, and efficiency. Among the challenges that prevent the big-scale implementation of this technology, there is the authoring challenge, which we address by answering the following research questions: (1) “how can we fasten MR authoring in a manufacturing context?” and (2) “how can we reduce the deployment time of industrial MR experiences?”. This paper presents an experiment performed in collaboration with Volvo within the remanufacturing of truck engines. MR seems to be more valuable for remanufacturing than for many other applications in the manufacturing industry, and the authoring challenge appears to be accentuated. In this experiment, product lifecycle management (PLM) tools are used along with internet of things (IoT) platforms and MR devices. This joint system is designed to keep the information up-to-date and ready to be used when needed. Having all the necessary data cascading from the PLM platform to the MR device using IoT prevents information silos and improves the system’s overall reliability. Results from the experiment show how the interconnection of information systems can significantly reduce development and deployment time. Experiment findings include a considerable increment in the complexity of the overall IT system, the need for substantial investment in it, and the necessity of having highly qualified IT staff. The main contribution of this paper is a systematic approach to the design of industrial MR experiences.

As the need for more responsible production and consumption grows quickly, so does the interest in the concepts of eco-efficiency and circularity. To make swift progress towards sustainability, solutions must be developed and deployed at scale. It is therefore critical to understand the challenges faced by industry to accelerate the uptake of best practices for circular and efficient production systems. This paper presents the emerging issues from three industrial pilots in an on-going collaborative project. We discuss and suggest further work around crucial questions such as: How to deploy circular solutions from lab to industrial scale? How can digitalization support efficient circular processes?. 

Observational ergonomic evaluation methods have inherent subjectivity. Observers’ assessment results might differ even with the same dataset. While motion capture (MOCAP) systems have improved the speed and the accuracy of motiondata gathering, the algorithms used to compute assessments seem to rely on predefined conditions to perform them. Moreover, the authoring of these conditions is not always clear. Making use of artificial intelligence (AI), along with MOCAP systems, computerized ergonomic assessments can become more alike to human observation and improve over time, given proper training datasets. AI can assist ergonomic experts with posture detection, useful when using methods that require posture definition, such as Ovako Working Posture Assessment System (OWAS). This study aims to prove the usefulness of an AI model when performing ergonomic assessments and to prove the benefits of having a specialized database for current and future AI training. Several algorithms are trained, using Xsens MVN MOCAP datasets, and their performance within a use case is compared. AI algorithms can provide accurate posture predictions. The developed approach aspires to provide with guidelines to perform AI-assisted ergonomic assessment based on observation of multiple workers.