Collision Preventing Phase-Progress Control for Velocity Adaptation in Human-Robot Collaboration

Abstract

As robots are leaving dedicated areas on the factory floor and start to share workspaces with humans, safety of such collaboration becomes a major challenge. In this work, we propose new approaches to robot velocity modulation: While the robot is on a path prescribed by the task, it predicts possible collisions with the human and gradually slows down, proportionally to the danger of collision. Two principal approaches are developed-Impulse Orb and Prognosis Window-that dynamically determine the possible robot-induced collisions and apply a novel velocity modulating approach, in which the phase progress of the robot trajectory is modulated while the desired robot path remains intact. The methods guarantee that the robot will halt before contacting the human, but they are less conservative and more flexible than solutions using reduced speed and complete stop only, thereby increasing the effectiveness of human-robot collaboration. This approach is especially useful in constrained setups where the robot path is prescribed. Speed modulation is smooth and does not lead to abrupt motions, making the behavior of the robot also better understandable for the human counterpart. The two principal methods under different parameter settings are experimentally validated in a human-robot interaction scenario with the Franka Emika Panda robot, an external RGB-D camera, and human keypoint detection using OpenPose.