A Mobile Manipulator System for Accurate and Efficient Spraying on Large Surfaces

Abstract

The ability to accurately spray on large surfaces is essential in many industrial applications. This task can be ergonomically challenging. Currently, fixed robotic manipulators are being used for this purpose. However, a fixed manipulator may not be able to spray on the entire surface due to limited reachability. We present a mobile manipulator system that can accurately and efficiently spray on large surfaces by automatically generating a plan based on the given spraying task. We demonstrate the use of self-supervised batch learning to reduce the number of experiments needed to create a model of the spray tool. We report a mobile base placement planner that determines the minimum base locations required to carry out the spraying task. Lastly, we have developed a image-based perception pipeline that enables the robot to characterize spraying error. These algorithms have been experimentally verified in this paper by having a mobile manipulator spray paint a large mural. We demonstrate that our approach enables a mobile manipulator to spray accurately and efficiently on large surfaces.