Coping with Changing Articles in Automated Picking Systems by Adaptive Object Detection and Human-Robot Cooperation

Picking as core process of intra logistics must cope with an increasing difficulty in acquisition of personnel and with continuously changing product ranges. These challenges can be tackled by partwise automated picking systems to create a cooperative working environment for human pickers and picking robots. However, performance of picking robots is determined significantly by their ability to detect objects. The presented approach enables a stepwise transformation from manual picking to highly automated picking processes by learning robots increasing their ability for successful object detection by a neural network. Its goals are to guarantee reliable order fulfilment by a feedback loop between humans and robots for error handling and to gather data for learning algorithms. A concept for a human-robot collaboration and a process model realizing the feedback loop are proposed. In addition, a quantitative framework to evaluate partwise automated picking systems is introduced. First results are provided by a demonstrator including an environment for image recording and an agent-based simulator. It is shown that the probability for a successful object detection can be improved by the proposed approach.