Moritz Eckhoff, and Robin Jeanne Kirschner, and Elena Kern, and Saeed Abdolshah and Sami Haddadin
An MPC Framework For Planning Safe & Trustworthy Robot Motions
IEEE International Conference on Robotics and Automation (ICRA)
Abstract
Strategies for safe human-robot interaction (HRI), such as the well-established Safe Motion Unit, provide a velocity scaling for biomechanically safe robot motion. In addition, psychologically-based safety approaches are required for trustworthy HRI. Such schemes can be very conservative and robot motion complying with such safety approaches should be time efficient within the robot motion planning. In this study, we improve the efficiency of a previously introduced approach for psychologically-based safety in HRI via a Model Predictive Control robot motion planner that simultaneously adjusts Cartesian path and speed to minimise the distance to the target pose as fast as possible. A subordinate real-time motion generator ensures human physical safety by integrating the Safe Motion Unit. Our motion planner is validated by two experiments. The simultaneous adjustment of path and velocity accomplishes highly time efficient robot motion, while considering the human physical and psychological safety. Compared to direct path velocity scaling approaches our planner enables 28 % faster motion execution.
@InProceedings{EckhoffICRA2022,
author = {Moritz Eckhoff, and Robin Jeanne Kirschner, and Elena Kern, and Saeed Abdolshah and Sami Haddadin},
title = {An MPC Framework For Planning Safe & Trustworthy Robot Motions},
journal = {IEEE International Conference on Robotics and Automation (ICRA)},
year = {Accepted, 2022},
}