A Novel Constrained Trajectory Planner for Safe Human-robot Collaboration

Abstract

This paper presents a novel collision avoidance algorithm for collaborative robotics that can influence the collision-free trajectory of the robot according to preferred directions with respect to the human posture. The aim is to avoid the human body parts in a controlled manner so that the robot trajectory is predictable. The algorithm is based on closed loop inverse kinematics and uses velocity commands to modify the robot trajectory in real-time. The existing human tracking devices allow to measure the human posture in three dimensions. The idea is to combine the human posture estimation with repulsive volumes, i.e. regions that approximate the human size and that produce repulsive velocities on the robot, and to add attractive surfaces made of cylindrical sectors to condition the avoidance manoeuvre in a chosen direction. The algorithm is tested in a simulation environment built with the model of a collaborative robot and a mock-up of the human, whose motion is generated from real data acquired by 3d vision sensors. The results show the effectiveness of the proposed method during a pick and place task in common scenarios, where the human intersects the robot planned path with different body parts.