Trajectory generation algorithm for safe human-robot collaboration based on multiple depth sensor measurements

Abstract

Computing and modifying in real-time the trajectory of an industrial robot involved in a Human-Robot Collaboration (HRC) scenario is a challenging problem, mainly because of two conflicting requirements: ensuring the human worker’s safety and completing the task assigned to the robot. This paper proposes a novel trajectory generation algorithm conceived to maximize productivity while taking into account safety requirements as actual constraints. At first, safety constraints are formulated by taking into account a manipulator and a set of arbitrarily-shaped convex obstacles. Then, a sensor fusion algorithm merges together the measurements acquired from different depth sensors and outputs a noise-free estimation of the kinematic configuration of a human worker moving inside the robotic cell. This estimation is then used to predict the space that the human worker can occupy within the robot stopping time in terms of a set of convex swept volumes. By considering these swept volumes as obstacles, the robot controller can modify the pre-programmed trajectory in order to enforce the safety constraints (thus avoiding collision with the human worker), while preventing task interruption. The proposed trajectory generation algorithm is validated through several experiments performed on an ABB IRB140 industrial robot.