Action Recognition in Assembly for Human-Robot-Cooperation using Hidden Markov Models

The application of human-robot-collaborations where at least one human and one robot share a workspace and work on the same product give the possibility to combine the strength of the human, e.g. flexibility and adoption to variable processes, and the strength of the robot, e.g. endurance and precision. This gives the chance to automate manual processes while keeping the flexibility in the process. In these applications the tasks are allocated to human and robot. Whereas the human can see and understand, which task the robot conducts, the robot cannot. However, in order to optimize the collaboration between human and robot, the robot should be aware of the task which is being performed by the human so that it can slightly adopt to the human's way of working, such as the timing of its tasks. In order to reach this goal, an action recognition approach for assembly tasks with a Hidden Markov Model is presented. An assembly task is divided into subtasks, which are then recognized by the markov model through the movements of the human. Cameras installed at the shared workspace observe the movements of the worker that serve as emissions for the Hidden Markov Model. The structure of the model is a layered Hidden Markov Model where the lower layer represents the basic movements such as move or bring. Trajectories between the starting position of the movement and the position of the assembly parts are calculated in order to recognize an action with less training of the markov model. The paper describes the structure of the model and first results of the application. (C) 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 7th CIRP Conference on Assembly Technologies and Systems.