Safe Human Robot Cooperation in Task Performed on the Shared Load

Abstract

Human-robot collaboration in industrial settings calls for implementing safety measures to ensure there is no risk to humans working in such an environment. In human-robot physical collaboration, an object or a load is handled by both human and the robot. Developing a safety framework for the robot is a requirement for preventing collisions during performing a task. In this paper, force myography (FMG) data are used to develop a control scheme for the robot such that it can work with the human worker while avoiding collisions. Force myography quantifies the activities of human muscles when applying forces to handle an object. A neural network-based approach is then used to select the most informative features of the FMG signal. The developed control scheme incorporates the FMG data and the robot dynamics to obtain a prediction about the next step of the cooperation task and to plan the robot motion accordingly. The proposed approach is evaluated experimentally in real time in a moving objects task which requires appropriate complementary actions from the robot and the human user. The results of this study show that the proposed scheme can successfully plan the robot motion based on the actions of the human user.