Realization and validation of a collaborative automated picking system

Purpose: A picking system is presented ensuring order fulfilment and enabling transformation from manual to automated picking using a continuous learning process. It is based on Machine Learning for object detection and realized by a human-robot collaboration to meet requirements for flexibility and adaptability. A demonstrator is implemented to show cooperation and to evaluate the learning process. Methodology: The collaborative process, system architecture, and an approach for evaluation and workload balancing for order fulfilment and learning of robots during picking have already been introduced. However, a practical application is still missing. A demonstrator is implemented using an agent-based architecture (JADEX) and a physical robot (UR5e) with a camera for object detection and first empirical data are evaluated. Findings: Single components of the demonstrator are already developed, but a pending task is to implement their interaction to analyze overall system performance. This work focuses on human-robot-interaction (Emergency Call), automated generation of images extended by feedback information, and training of algorithms for object detection. Requirements of human-machine interface, technical evaluation of image recording, and effort of algorithm training are discussed. Originality: Many approaches for automated picking assume a static range of objects. However, this approach considers a changing range as well as a concept for transformation of manual to automated picking enabled by human-robot cooperation and automated image recording while enabling reliable order fulfilment.