Wire Harness
Wire harnesses, crucial components in various industries, are the focus of ongoing research aimed at improving their automated assembly and analysis. Current research emphasizes the development of robust computer vision systems, often employing deep learning models like object detection networks, to enable robots to efficiently handle the complex geometries and entanglement challenges presented by wire harnesses. These advancements are driven by the need for increased efficiency and quality control in manufacturing, particularly within the automotive and electronics sectors, and contribute to broader progress in robotics and computer vision. Furthermore, large language models are being explored for applications such as predicting air quality based on physical principles and generating personalized product reviews.
Papers
Deep Learning-Based Connector Detection for Robotized Assembly of Automotive Wire Harnesses
Hao Wang, Björn Johansson
Overview of Computer Vision Techniques in Robotized Wire Harness Assembly: Current State and Future Opportunities
Hao Wang, Omkar Salunkhe, Walter Quadrini, Dan Lämkull, Fredrik Ore, Björn Johansson, Johan Stahre
A Systematic Literature Review of Computer Vision Applications in Robotized Wire Harness Assembly
Hao Wang, Omkar Salunkhe, Walter Quadrini, Dan Lämkull, Fredrik Ore, Mélanie Despeisse, Luca Fumagalli, Johan Stahre, Björn Johansson