Enabling Robot Selective Trained Deep Neural Networks for Object Detection Through Intelligent Infrastructure

To save costs in logistics, handling steps are going to be automated by robots in the future. Due to the complex industrial conditions prevailing there, this is only possible with a sufficient degree of intelligence in the respective systems. Despite advances in artificially intelligent algorithms, the latest neural networks reveal significant weaknesses in performance and transferability to other applications. In order to enable a holistic autonomous material flow, the paper presents an infrastructure concept, which makes it possible to identify and train the most suitable networks robot-selectively with very limited effort. Using two practical examples, the functionality of the designed algorithms for the industrial implementation of a new use case as well as the updating and improvement of an existing system is finally outlined. It will be shown that with measures such as the automated collection of training data, the AI-supported labeling process, the intuitive validation of the trained networks via a mobile application and the automated retraining of robots already integrated, a further step can be taken towards holistically automated logistics process chains.