The Robot Operating System (ROS) has become the de facto standard middleware in robotics, widely adopted across domains ranging from education to industrial applications. The RoboStack distribution, a conda-based packaging system for ROS, has extended ROS's accessibility by facilitating installation across all major operating systems and architectures, integrating seamlessly with scientific tools such as PyTorch and Open3D. This paper presents ROS2WASM, a novel integration of RoboStack with WebAssembly, enabling the execution of ROS 2 and its associated software directly within web browsers, without requiring local installations. ROS2WASM significantly enhances the reproducibility and shareability of research, lowers barriers to robotics education, and leverages WebAssembly's robust security framework to protect against malicious code. We detail our methodology for cross-compiling ROS 2 packages into WebAssembly, the development of a specialized middleware for ROS 2 communication within browsers, and the implementation of www.ros2wasm.dev, a web platform enabling users to interact with ROS 2 environments. Additionally, we extend support to the Robotics Toolbox for Python and adapt its Swift simulator for browser compatibility. Our work paves the way for unprecedented accessibility in robotics, offering scalable, secure, and reproducible environments that have the potential to transform educational and research paradigms.

Sparse and imbalanced data is a common challenge that practitioners must overcome when implementing industrial ML applications. This challenge concerns deep learning-based quality inspection systems in particular, as they often are obligated to adhere to high constraints in terms of reliability and performance. As deep learning quality inspection systems are usually implemented in a supervised manner, they additionally require balanced datasets that may be difficult or costly to obtain in production environments. However, new approaches using Generative Adversarial Networks for synthetic image generation promise a remedy by increasing the data amount of sparse classes, such as faults or defects. This paper presents an experimental use case where we employ a state-of-the-art image generator model of StyleGAN2 to a quality inspection application in laser beam welding to increase the number of defect images for training an object detector. We evaluate the generated images and their influence on the object detector’s performance using several training configurations. Our results reveal that with the limited amount of data, we are able to generate synthetic images that look promising at first glance. However, in the evaluation based on the object detector, we find that introducing synthetic images had an adverse effect on detection performance and robustness of the system. Further research is required to generate defect images from sparse datasets that can improve the performance of object detection systems in quality inspection.

Self-optimizing robots and machines in future factories are an exciting next step towards an ever more efficient industry. To achieve this goal, robots used in production must gain an understanding of the quality of their behavior. Machine learning can help us move closer to this goal. In this paper, we provide insights on the feasibility of self-optimized laser welding robots and show how accurate quality analysis based on deep learning and smart computer vision algorithms provide a reliable input for quality evaluation and ultimately a scoring function. Furthermore, the suggested scoring function can capture the defining properties of a weld. In turn, the score can be used as feedback to define a reward for a reinforcement learning agent's action, which then optimizes the robot's behavior accordingly. Our experiments show that we can achieve very good accuracy and consistency when evaluating the quality of the weld with deep learning and statistical modeling. Finally, we provide a production-oriented learning architecture that considers the scoring component in a reinforcement learning pipeline.

Predictive maintenance is one of the major drivers of Industry 4.0 as it can significantly reduce costs by improving overall equipment effectiveness and extending the remaining useful life of production machines. Most of the potential lies in the brownfield with old equipment where no sensors or connectivity are available. This paper shows how these production machines can be enabled for predictive maintenance by retrofitting with low-cost sensors, an Industrial-Internet-of-Things-architecture and machine learning. An industrial implementation on a heavy lift Electric Monorail System at the BMW Group will be shown.