  To enable a mobile manipulator to perform human tasks from a single teaching
demonstration is vital to flexible manufacturing. We call our proposed method
MMPA (Mobile Manipulator Process Automation with One-shot Teaching). Currently,
there is no effective and robust MMPA framework which is not influenced by
harsh industrial environments and the mobile base's parking precision. The
proposed MMPA framework consists of two stages: collecting data (mobile base's
location, environment information, end-effector's path) in the teaching stage
for robot learning; letting the end-effector repeat the nearly same path as the
reference path in the world frame to reproduce the work in the automation
stage. More specifically, in the automation stage, the robot navigates to the
specified location without the need of a precise parking. Then, based on
colored point cloud registration, the proposed IPE (Iterative Pose Estimation
by Eye & Hand) algorithm could estimate the accurate 6D relative parking pose
of the robot arm base without the need of any marker. Finally, the robot could
learn the error compensation from the parking pose's bias to modify the
end-effector's path to make it repeat a nearly same path in the world
coordinate system as recorded in the teaching stage. Hundreds of trials have
been conducted with a real mobile manipulator to show the superior robustness
of the system and the accuracy of the process automation regardless of the
harsh industrial conditions and parking precision. For the released code,
please contact<EMAIL_ADDRESS>