Control System of Collaborative Robotic Based on the Methods of Contactless Recognition of Human Actions

Abstract

Human-robot collaboration is a key concept in modern intelligent manufacturing. Traditional human-robot interfaces are quite difficult to control and require additional operator training. The development of an intuitive and native user interface is important for the unobstructed interaction of human and robot in production. The control system of collaborative robotics described in the work is focused on increasing productivity, ensuring safety and ergonomics, minimize the cognitive workload of the operator in the process of human-robot interaction using contactless recognition of human actions. The system uses elements of technical vision to get of input data from the user in the form of gesture commands. As a set of commands for control collaborative robotic complexes and training the method proposed in the work, we use the actions from the UTD-MHAD database. The gesture recognition method is based on deep learning technology. An artificial neural network extracts the skeleton joints of the human and describes their position relative to each other and the center of gravity of the whole skeleton. The received descriptors feed to the input of the classifier, where the assignment to a specific class occur. This approach allows reducing the error from the redundancy of the data feed at the input of the neural network.