Deep Learning Framework for Controlling Work Sequence in Collaborative Human–Robot Assembly Processes

project UIDB/EMS/00667/2020 (UNIDEMI)The human–robot collaboration (HRC) solutions presented so far have the disadvantage that the interaction between humans and robots is based on the human’s state or on specific gestures purposely performed by the human, thus increasing the time required to perform a task and slowing down the pace of human labor, making such solutions uninteresting. In this study, a different concept of the HRC system is introduced, consisting of an HRC framework for managing assembly processes that are executed simultaneously or individually by humans and robots. This HRC framework based on deep learning models uses only one type of data, RGB camera data, to make predictions about the collaborative workspace and human action, and consequently manage the assembly process. To validate the HRC framework, an industrial HRC demonstrator was built to assemble a mechanical component. Four different HRC frameworks were created based on the convolutional neural network (CNN) model structures: Faster R-CNN ResNet-50 and ResNet-101, YOLOv2 and YOLOv3. The HRC framework with YOLOv3 structure showed the best performance, showing a mean average performance of 72.26% and allowed the HRC industrial demonstrator to successfully complete all assembly tasks within a desired time window. The HRC framework has proven effective for industrial assembly applicationspublishersversionpublishe

A human-oriented design process for collaborative robotics

The potential of collaborative robotics often does not materialize in an efficient design of the human-robot collaboration. Technology-oriented approaches are no longer enough in the Industry 4.0 era. This work proposes a set of methods to support manufacturing engineers in the human-oriented design process of integrated production systems to obtain satisfactory performance in the mass customization paradigm, without impacting the safety and health of workers. It founds the design criteria definition on five main pillars (safety, ergonomics, effectiveness, flexibility, and costs), favors the consideration of different design alternatives, and leads their selection. The dynamic impact of the design choices on the various elements of the system prevails over the static design constraints. The method has been experimented in collaboration with the major kitchen manufacturer in Italy, which introduced a collaborative robotics cell in the drawers' assembly line. It resulted in a more balanced production line (10% more), a verified risk minimization (RULA score reduced from 5 to 3 and OCRA score from 13.30 to 5.70), and a greater allocation of operators to high added value activities

Application of speed and separation monitoring method in human-robot collaboration: industrial case study

Application of human-robot-collaboration techniques in automotive industries has many advantages on productivity, production quality, and workers’ ergonomy, however workers’ safety aspects play the key role during this collaboration. In this paper, results of the ongoing research about the development of a manufacturing cell for the automotive brake disc assembly that is based on the human-robot collaboration are presented. Operational speed and worker-robot separation monitoring methodology (SSM) as one of the available method to reduce the risk of injury according to the ISO technical specification 15066 on collaborative robot in sharing space with human, has been applied. Virtual environment simulation has been used, considering different percentages of robot maximum speed, to determine the SSM algorithm parameters for estimating the minimum protective distance between the robot and operator. Using ISO/TS 15066 and virtual environment simulation, the minimum separation distance between operator and robot has been estimated. Using human-robot collaboration along with the safety issues specified by SSM system has increased the safety of operation and reduced the operator fatigue during the assembly process