Online supervised global path planning for AMRs with human-obstacle avoidance

Abstract

In smart factories, the performance of the production lines is improved thanks to the wide application of mobile robots. In workspaces where human operators and mobile robots coexist, safety is a fundamental factor to be considered. In this context, the motion planning of Autonomous Mobile Robots is a challenging task, since it must take into account the human factor. In this paper, an implementation of a three-level online path planning is proposed, in which a set of waypoints belonging to a safe path is computed by a supervisory planner. Depending on the nature of the detected obstacles during the robot motion, the re-computation of the safe path may be enabled, after the collision avoidance action provided by the local planner is initiated. Particular attention is devoted to the detection and avoidance of human operators. The supervisory planner is triggered as the detected human gets sufficiently close to the mobile robot, allowing it to follow a new safe virtual path while conservatively circumnavigating the operator. The proposed algorithm has been experimentally validated in a laboratory environment emulating industrial scenarios