A Self-Modulated Impedance Multimodal Interaction Framework for Human-Robot Collaboration

Abstract

Human Robot interaction is a fundamental perquisite for any robot performing a physical task in collaboration with a human. The presence of disturbances arising from the partially known tasks payloads, the unexpected interaction forces in general, and the uncertainty in the interpretation of the human intention in terms of motions and forces can pose significant challenges and eventually compromise the execution of the collaborative task. This work presents a novel, intrinsically adaptable multimodal (force, motion and verbal) interaction framework for human-robot collaboration (HRC) that leverages on an online self-tuning stiffness regulation principle to provide adaptation to interaction/payload forces and reject disturbances arising by unexpected interaction loads. Besides the presented method, it enables the rejection of unnecessary motion commands (e.g. oscillations generated by the human operator) to reach the robot co-worker through the filtering of the human generated motions, that are outside the range (in terms of speed and acceleration) of the envisioned manipulation manoeuvres. Finally, a verbal interaction channel allows the operator to convey securely his high level intentions and to control the states of the task execution. We evaluated and demonstrated the effectiveness of the proposed multimodal interaction framework in a high weight carrying human-robot collaboration task using the humanoid robot COMAN +.