Machine learning-based image processing for human-robot collaboration

Abstract: Human-robot Collaboration as a new paradigm in manufacturing has already been a hot topic in both manufacturing science, production research, intelligent robotics, and computer science. Due to the boost of deep learning technologies in the nearly ten years, advanced information processing technologies bring the new possibility to human-robot Collaboration. Meanwhile, machine learning-based image processing such as convolutional neural network has become a powerful tool in dealing with problems like target recognizing and locating. This kind of technologies shows potentials on robotic manufacturing and human-robot Collaboration. A challenge is to implement well-designed deep neural networks linked to a robotic system that can conduct collaborative works with the human. Accuracy and robustness need also be concerned in the development. This thesis work will address this challenge. This thesis tries to implement a solution based in Machine Learning methods for image detection which permits us to, using a low cost image solutions (RGB single camera), detect and localize manufacturing components to pick them and finish an assembly, helping the human co-workers, using an industrial robot, simplifying also the IT tasks to run it.