Considering avoidance and consistency in motion planning for human-robot manipulation in a shared workspace

Abstract

This paper presents an approach to formulating the cost function for a motion planner intended for human-robot collaboration on manipulation tasks in a shared workspace. To be effective for human-robot collaboration a robot should plan its motion so that it is both safe and efficient. To achieve this, we propose two factors to consider in the cost function for the robot's motion planner: (1) Avoidance of the workspace previously-occupied by the human, so that the motion is as safe as possible, and (2) Consistency of the robot's motion, so that the motion is as predictable as possible for the human and they can perform their task without focusing undue attention on the robot. Our experiments in simulation and a human-robot workspace sharing study compare a cost function that uses only the first factor and a combined cost that uses both factors vs. a baseline method that is perfectly consistent but does not account for the human's previous motion. We find that using either cost function we outperform the baseline method in terms of task success rate without degrading the task completion time. The best task success rate is achieved with the cost function that includes both the avoidance and consistency terms.